case study for inventory control

Poor Inventory Management Examples Made by Huge Brands

As many retailers can attest, poor inventory management can seriously harm a company and its brand/brands, leading to short-term financial damage, a fall in stock prices, bankruptcy, or company closure.

From small businesses to companies with large inventories, there's always a concern with the everyday and long-term challenges of inventory management. Some companies may look at updating legacy management systems, while others look for real-time ways to future-proof the business. High-profile, well-known, and loved brands are not exempt from inventory control problems, and many retailers can learn valuable lessons from their mistakes.

How Poor Inventory Management Can Kill a Brand

When supply chain and inventory problems arise, retailers face some consequences. Here are some poor inventory management examples:

1)  Using outdated methods to track items , such as:

• Manual inventory tracking , which becomes time-consuming and error-prone as your company grows. You’ll always be one step behind your actual inventory levels, which will cause ordering issues.
• Excel/electronic spreadsheets , which are prone to severe errors. In a study of errors in 25 sample spreadsheets, the  Tuck Business School at Dartmouth College   found that 15 workbooks contained 117 errors. While 40% of those errors had little impact on the businesses studied, seven errors caused massive losses of $4 million to $110 million, according to the researchers’ estimates.

2)     Too large of an inventory . Reports show that most businesses have 20-40% of their working capital tied up in inventory. If you have a greater amount of product than what demand calls for, you won't be able to fulfill orders optimally. Large stock levels don’t just lead to more management headaches; they can also cut into profits and cause dead stock.

3)     Inadequate reports and demand forecasting . When companies don’t use or have access to accurate information such as sales trends, best-selling products, customer behavior, and the like – they fall into the trap of ordering too much inventory. When this happens, companies experience the problems of an excessive amount of finished goods, ordering too few finished products, experiencing shortages, and losing customers. With accurate real-time reporting accessible 24/7, anytime, from anywhere, companies can forecast their customers’ future behavior and order accordingly to meet customer demand without exceeding their budget.

Four Examples of Poor Inventory Management

Let’s take a look at four high-profile brands and how a crisis of inventory created some real problems that companies could have prevented had they managed their inventory properly:

Nike’s Excess Inventory Problem

As one of the world's most recognized athletic brands, Nike has many goods to manage. And as a result, it has had difficulty keeping inventory under control. In the early 2000s, the company adopted an updated  inventory management software  after losing around $100 million in sales due to issues with tracking goods. The software promised to help Nike predict items that would sell best and prepare the company to meet demands, but bugs and data errors resulted in incorrect demand forecasts and led to millions more lost.

Nike’s case illustrated just how crucial it is to correctly manage stock levels and your inventory management system. When choosing an inventory management solution, it’s vital to ensure the quality of software your vendor provides is accurate, flexible, and customized for your particular business. It needs to be able to grow and change as the business and the customer base change.

Nike continues to have issues with inventory. 2016 was challenging for the retailer, as Nike's gross margin declined due to a higher percentage of discounted sales because of inventory management problems. The retailer continues to take steps to control its inventory management practices through manufacturing overhauls better and allowing new technology to bring manufacturing to the digital age. Ultimately Nike will remain a global leader as it keeps exploring new markets, innovating new products, and generating its supply channels.

Best Buy’s Christmas Inventory Nightmare

In December 2011—smack dab in the middle of the holiday season— Best Buy issued a statement : “Due to the overwhelming demand of hot product offerings on BestBuy.com during the November and December period, we have encountered a situation that has affected redemption of some of our customers’ online orders. We are very sorry for the inconvenience this has caused, and we have notified the affected customers.”

Customers were infuriated by Best Buy’s decision to cancel orders instead of delaying shipment, which was most likely because the company ran out of stock. Reportedly Best Buy sold many of the withdrawn items on Black Friday. The retailer essentially cast a wide net, collecting as many orders as possible, likely knowing it would be unable to fulfill them all.

As Best Buy proved, buying items online as an alternative to in-store still carries a risk. Consumers don't know for a fact that they will get their product. While receipts are issued and shipping estimates are given, some variables still allow consumers to be 100% certain their purchase will be complete every time. It’s not hard to imagine that Best Buy probably lost many of its customers to Amazon after that 2011 debacle.

Target’s Disastrous Failed Expansion into Canada

Target is a well-loved brand in the US, so it seemed only natural that it would be just as well-received with expansion up north into Canada. Target executives had a decision to make. They needed a way to track their stock levels and chose to work with an entirely new and untested system. Target Canada would eventually learn what happens when inexperienced employees working under a tight timeline are expected to launch a retailer using technology that nobody—not even at the US headquarters—understood.

In 2013, the company had  trouble moving products  from its large distribution centers onto store shelves, leaving Target outlets poorly stocked. It didn’t take long for Target to figure out the underlying cause of the breakdown: The data contained within the company’s supply chain software, which governs the movement of inventory, was riddled with flaws. The checkout system was glitchy and didn’t process transactions properly. Worse, the technology managing inventory and sales were new to the organization; no one seemed to fully understand how it worked.

Why Too Much Inventory is Bad

Besides technology issues, problems of ordering and inventory were running amok. Target stalled items with long lead times coming from overseas—products weren’t fitting into shipping containers as expected, or tariff codes were missing or incomplete. Finished goods that made it to a distribution center couldn’t fulfill orders for shipping to a store. Other items weren’t able to fit correctly onto store shelves. What appeared to be isolated fires quickly became a raging inferno threatening to destroy the company’s supply chain.

Target’s distribution centers were bursting with products and dead stock. Target Canada had ordered way more stock than it could sell. The company had purchased a sophisticated forecasting and replenishment system, but it wasn’t beneficial at the outset, requiring years of historical data to provide meaningful sales forecasts. When the buying team was preparing for store openings, it relied on wildly optimistic projections developed at US headquarters.

Roughly two years after they launched, Target Canada filed for creditor protection, marking the end of its first international foray and one of the most confounding sagas in Canadian corporate history. The debacle cost the parent company billions of dollars, sullied its reputation, and put roughly 17,600 people out of work.

Supply Chain Disruption Closed 900 KFC Branches in the UK

In February 2018, Kentucky Fried Chicken (KFC) was forced to  close many of its 900 UK branches due to supply chain disruption . In a press release, the fast-food giant stated, “We've brought a new delivery partner onboard, but they've had a couple of teething problems - getting a fresh chicken out to 900 restaurants across the country is pretty complex!”

By changing their delivery partner, approximately 750 KFC outlets across the UK faced delays in receiving their daily delivery of fresh chicken, meaning their restaurants could not supply customers and ultimately had to close. At the time, many thought the giant could lose up to £ 1 million daily.

Could KFC have done more to ensure their supplier was suitable for the job? The thought is that multiple supplier contracts could have spread KFC could have avoided the weight of the mammoth delivery task and a crisis like this. Another issue was that their supplier only had one distribution spot instead of multiple, which would have been able to service the outlets much more manageable.

What Can Businesses Learn From Inventory Management Problems?

It takes more than having a large inventory of products to keep a retail business running. All that inventory must be stored, moved, and in the right place at the right time. Warehouses need to be efficient, and their tools and workhorse vehicles are kept up to date. Every part of the supply chain needs to coordinate, from obtaining raw materials to distributing finished goods.

The same can be said about the technology to track and manage the inventory as it moves locations. Companies need to know accurate numbers when it comes to inventory. Their livelihood, franchisees, investors, and employees depend on it! When you don’t see what you have or how/when it moves about, there’s no actual knowledge about the most critical aspect of your business – your inventory.

Retailers of all sizes are looking for easy-to-use, mobile, affordable, secure, and rapidly deployable  asset tracking systems . They need a reliable method to track the thousands of inventory items that move through their location(s)/warehouses daily. Asset Panda is the answer. We leverage the cloud and free mobile apps to help retailers get the information they need about their inventory. Our retail and small business clients know where their inventory is, who has what, and its condition.

Asset Panda's Inventory Management System

Asset Panda is simple to use with a very intuitive platform. Our system records the entire lifecycle of an asset. Other capabilities include custom reports, depreciation calculation, mobile enterprise service desk, and more. Leading retailers recognize that better inventory tracking processes will lower business costs by reducing loss, property taxes, and the amount of insurance they must carry. All the home offices must run reports to get detailed, real-time data from the field.

Try our inventory management software  free for 14 days  so you can see what’s truly possible when you manage your inventory the right way! (No credit card required).

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Benchmarking: An International Journal

ISSN : 1463-5771

Article publication date: 14 June 2022

Issue publication date: 24 August 2023

Managing inventory continues to be a growing area of concern for many retailers due to the multitude of issues that arise from either an excess or shortage of inventory. This study aims to understand how a large-scale retail chain can improve its handling of excess seasonal inventory using three common strategies: information sharing, visibility, and collaboration.

Design/methodology/approach

This study has been designed utilizing a case study method focusing on one retail chain at three key levels: strategic (head office), warehouses, and retail stores. The data have been collected by conducting semi-structured interviews with senior-level employees at each of the three levels and employing a thematic analysis to examine the major themes.

The results show how three common strategies are being practiced by this retailer and how utilizing these strategies aids the retailer in improving its performance in regard to seasonal inventory. Among our research findings, some challenges were discovered in implementing the strategies, most notably: human errors, advanced forecasting deficiencies, and the handling of return merchandise authorizations.

Originality/value

This research takes a case study approach and focuses on one big-box retailer. The authors chose to study three levels (head office, warehouses, and retail stores) to gain a deeper understanding of the functions and processes of each level, and to understand the working relationships between them. Through the collection of primary data in a Canadian context, this study contributes to the literature by investigating supply chain strategies for managing inventory. The Canadian context is especially interesting due to the multi-cultural demographics of the country.

• Supply chain
• Information sharing
• Collaboration
• Inventory management
• North America

Esrar, H. , Zolfaghariania, H. and Yu, H. (2023), "Inventory management practices at a big-box retailer: a case study", Benchmarking: An International Journal , Vol. 30 No. 7, pp. 2458-2485. https://doi.org/10.1108/BIJ-11-2021-0716

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Copyright © 2022, Emerald Publishing Limited

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How to Improve Inventory Management – 15 Proven Ways with Case Studies

Demand forecasting.

Predicting product demand enables businesses to have the right amount of inventory on hand. Demand forecasting is a key strategy for inventory management as it predicts consumer demand for products or services, allowing businesses to manage inventory more effectively and efficiently.

Demand forecasting uses historical sales data, market research, and statistical methods to predict future demand. The fundamental theories behind demand forecasting include time series analysis, causal models, and machine learning models.

Time Series Analysis

This involves examining historical data and identifying patterns like seasonality, trends, and cycles. These patterns are then used to project future demand.

Causal Models

These models analyze the relationship between demand and various external factors, such as economic indicators, marketing efforts, and price changes.

Machine Learning Models

Machine learning models use algorithms to analyze large datasets and identify complex patterns. These patterns are then used to predict future demand.

One case study showcasing the importance of demand forecasting is IBM’s use of demand forecasting models powered by AI. The multinational technology company has been able to achieve a reduction in forecasting errors by up to 28% through their AI-enabled models.

IBM applied machine learning to time-series forecasting, which allowed them to generate short-term and long-term sales forecasts at scale across various product categories. They used different AI models such as Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Decision Trees to make their predictions.

Furthermore, IBM utilized a combination of structured (e.g., sales numbers, stock levels) and unstructured data (e.g., text reviews, social media sentiment) in their forecasting models. By combining different types of data and using machine learning to process it, IBM could create more accurate and sophisticated demand forecasts.

The accuracy of demand forecasting can have a significant impact on business performance. According to a report by the Global Journal of Management and Business Research, a 1% improvement in forecast accuracy can result in a 2% decrease in inventory costs. Thus, demand forecasting can lead to substantial cost savings and increased profitability for businesses.

Despite these benefits, demand forecasting also has some challenges. It requires quality data, sophisticated analytical capabilities, and the ability to adjust forecasts based on changing market conditions. Therefore, businesses need to invest in technology, data management, and analytics capabilities to leverage demand forecasting effectively.

Safety Stock

Having a safety stock can mitigate the risk of stockouts. Safety stock is a fundamental inventory management strategy where companies keep extra inventory on hand to protect against variability in market demand or supply disruptions. It acts as an insurance against stockouts, which can lead to lost sales, disappointed customers, and potential harm to a company’s reputation

The theoretical underpinning for safety stock calculations often revolves around lead time, demand variability, and service level expectations. The classic safety stock formula is:

Safety Stock = (Max Lead Time – Average Lead Time) * Average Demand

Here, the Max Lead Time and Average Lead Time represent the longest and average time taken to replenish stock, and the Average Demand is the average units sold during that lead time.

Many companies modify this formula to consider the standard deviation of lead time and demand, as well as the desired service level (probability of not having a stockout).

Implementing safety stock requires a delicate balance. While having a high level of safety stock can prevent stockouts, it also increases inventory holding costs. The inventory holding cost is critical facet of any inventory management technique on the other hand, keeping a low level of safety stock reduces holding costs but increases the risk of stockouts.

A case study highlighting the effective use of safety stock is Amazon . The e-commerce giant uses advanced algorithms and machine learning techniques to determine optimal safety stock levels for millions of products. Amazon’s sophisticated inventory management system takes into account various factors, such as historical sales data, product life cycle, seasonal trends, and supplier reliability. This approach has reportedly helped Amazon maintain a high in-stock rate of 97.8% in 2020, reducing the risk of lost sales and improving customer satisfaction.

Moreover, a study by the International Journal of Production Economics found that a well-managed safety stock can lead to a 10-20% reduction in total inventory costs. This is achieved by maintaining a balance between holding costs and the cost of stockouts.

Despite its benefits, managing safety stock comes with challenges. It requires accurate data, sophisticated algorithms, and the ability to respond swiftly to changing market conditions. Businesses also need to periodically review and adjust their safety stock levels as demand patterns, lead times, and business goals change.

Batch Tracking

Batch tracking can mitigate the risks of product recalls. Batch tracking, also known as lot tracking, is a quality control inventory management technique that allows businesses to track goods along the distribution chain. It is especially important in industries where products need to be closely monitored for reasons of safety, compliance, or quality control, such as food, pharmaceuticals, and electronics.

In essence, batch tracking records the journey of a batch or lot of products or materials from their origin, through the manufacturing process, to the end consumer. This allows companies to manage recalls effectively, ensure regulatory compliance, and improve product quality.

The concept of batch tracking relies on the following principles:

Traceability

Every batch or lot of products should have a unique identifier that allows it to be tracked throughout the supply chain.

Transparency

Information about each batch, such as its origin, processing history, and distribution, should be recorded and readily available.

Accountability

Companies should take responsibility for the quality and safety of their products and have procedures in place for managing recalls or quality issues.

Case Studies

A case study demonstrating the importance of batch tracking is in the pharmaceutical industry, particularly in the case of the pharmaceutical company XYZ (hypothetical for the sake of the explanation). The company implemented batch tracking to improve its inventory management and product quality control. Inventory management is this highly critical of any product which is sensible to time.

The company assigned unique identifiers to each batch of drugs manufactured, enabling it to trace the journey of each batch from the raw materials used, through the manufacturing process, to the distribution to pharmacies. This allowed XYZ to quickly identify and isolate any batches that were associated with quality issues, reducing the scope and cost of recalls.

Furthermore, the implementation of batch tracking led to a decrease in carrying costs by about 27%, as the company was able to manage its inventory more effectively and reduce waste.

Additionally, a study by the Aberdeen Group found that companies using batch tracking had a 26% higher successful product completion rate compared to those not using batch tracking. This demonstrates how batch tracking can contribute to operational efficiency and product quality.

However, batch tracking can be challenging to implement. It requires sophisticated tracking systems, accurate data, and strong cooperation from all stakeholders in the supply chain. Therefore, businesses need to invest in the right technologies and processes to implement batch tracking effectively.

Vendor-Managed Inventory (VMI)

In a VMI arrangement, suppliers manage inventory levels.  Vendor -Managed Inventory (VMI) is a supply chain practice where the supplier or vendor is responsible for maintaining the customer’s inventory levels. Under this model, the supplier has access to the customer’s inventory data and is responsible for generating purchase orders.

This practice aims to improve inventory turnover and reduce stockouts or overstock situations. Aligning the manufacturer’s production with the retailer’s sales cycle enhances the efficiency of the supply chain. The Collaborative Process of Inventory Management is becoming very popular in lean and agile systems now a days.

The VMI model is based on several principles:

Information Sharing

In a VMI relationship, the customer shares real-time data on stock levels, sales, and forecasts with the supplier. This transparency allows the supplier to better plan production and deliveries.

Inventory Ownership

The supplier retains ownership of the inventory until it’s sold, effectively transferring the risks associated with inventory management from the customer to the supplier.

Performance Metrics

The supplier’s performance is usually evaluated based on the level of customer service they provide, such as their ability to avoid stockouts and maintain optimal inventory levels.

A successful case study of VMI implementation comes from Barilla, an Italian pasta manufacturer. Before implementing VMI, Barilla suffered from significant demand variability, leading to stockouts and excess inventory. By adopting a VMI strategy, Barilla transferred the responsibility of inventory management to its suppliers.

With access to real-time sales data, suppliers were able to better forecast demand, optimize production schedules, and improve delivery performance. This resulted in a 30% reduction in stockout instances and a significant improvement in Barilla’s customer service levels.

The benefits of VMI are supported by numerous studies. According to research by the Journal of Operations Management, VMI can lead to an average inventory reduction of 31% and an increase in service levels by up to 6%.

However, implementing a VMI strategy requires a high degree of collaboration and trust between the customer and the supplier. Both parties need to invest in compatible IT systems, adopt standardized processes, and agree on performance metrics. The benefits of VMI also depend on the nature of the products, the stability of demand, and the capabilities of the supplier.

ABC Analysis

This involves categorizing inventory based on its importance and value.  ABC analysis is a method of categorizing inventory into three categories based on their importance and value to the business. The system gets its name from the classes it involves: ‘A’ items are very important, ‘B’ items are important, and ‘C’ items are marginally important.

‘A’ Items: These are the high-priority items, often representing a small percentage of total items but a large portion of the inventory cost. They require close inventory control and rigorous demand forecasting.

‘B’ Items: These are the intermediate items, making up a larger percentage of total items but representing a lower portion of inventory cost than ‘A’ items. They require a moderate level of inventory control.

‘C’ Items: These are the low-priority items, which constitute the majority of the total items but contribute the least to the inventory cost. They require less stringent control and can be ordered in larger quantities less frequently.

The basic principle of ABC analysis is the Pareto principle, or the 80/20 rule, which suggests that 80% of the effects come from 20% of the causes. In the context of inventory management, it often happens that 80% of a company’s inventory value is made up of only 20% of its items. The categorization is a basic for any inventory management process

Example and Case Study

An example of the application of ABC analysis is in a pharmaceutical company, XYZ (hypothetical for the sake of the explanation). The company categorized its inventory based on the annual consumption value of each product (calculated as the annual demand multiplied by the cost per unit).

‘A’ items were the top 20% of items that accounted for about 70% of the company’s total inventory value. ‘B’ items were the next 30% of items, contributing around 25% of the total inventory value. The remaining 50% of items, classified as ‘C’ items, contributed only 5% to the inventory value.

By focusing its inventory management efforts on ‘A’ items, XYZ was able to manage its inventory more effectively, leading to a decrease in carrying costs by about 27%.

Several studies have validated the benefits of ABC analysis. For instance, a study published in the Journal of Operations Management found that companies using ABC analysis achieved a 14% reduction in inventory costs compared to those not using it.

However, implementing ABC analysis requires a good understanding of the company’s products and market dynamics. It also requires the collection and analysis of accurate demand and cost data. Therefore, companies need to invest in data management and analytical capabilities to apply ABC analysis effectively.

Inventory Turnover Ratio

This measures how often inventory is sold and replaced within a specific period.

The inventory turnover ratio is a key performance indicator that measures the efficiency of inventory management. It represents how many times a company has sold and replaced its inventory during a specific period, usually a year. The ratio provides insights into a company’s operational efficiency, liquidity, and overall financial health.

The formula to calculate the inventory turnover ratio is:

Inventory Turnover Ratio = Cost of Goods Sold (COGS) / Average Inventory

Cost of Goods Sold (COGS) is the total cost of all goods sold during a specific time period.

Average Inventory is the mean value of inventory during the same time period, usually calculated as the average of the inventory levels at the start and end of the period.

A higher inventory turnover ratio indicates that a company sells its inventory quickly, implying efficient inventory management and high demand for its products. Conversely, a lower ratio could suggest overstocking, slow sales, or obsolete inventory.

Consider a hypothetical example: a retail company, XYZ, which had a COGS of $2 million and an average inventory of $500,000 for the year. By applying the formula, XYZ’s inventory turnover ratio would be 4. This means that XYZ sold and replaced its inventory four times during the year.

The optimal inventory turnover ratio can vary significantly across different industries. For instance, in fast-moving industries like fashion or perishable goods, a high turnover ratio is desirable. In contrast, industries with slower-moving goods, like furniture or appliances, may have a lower turnover ratio. Having a solid understanding of a turnover ratio is key to inventory management

According to a study published in the Journal of Business Logistics, companies with higher inventory turnover ratios tend to have higher profit margins. The research found that a 10% increase in the inventory turnover ratio could lead to a 1% increase in the profit margin.

However, interpreting the inventory turnover ratio requires caution. While a high turnover ratio can suggest efficiency, it might also indicate inadequate inventory levels, leading to stockouts and lost sales. On the other hand, a low turnover ratio could signal overstocking or weak sales, but it might also reflect a strategic decision to maintain higher inventory levels to guard against supply chain disruptions.

Just-in-Time (JIT) Inventory

This method reduces inventory carrying costs.  Just-in-Time (JIT) inventory management is a strategy aimed at reducing in-process inventory and its associated carrying costs. The approach is based on producing goods to meet demand precisely when needed in the production process, not before, thereby minimizing inventory levels.

The underlying principles of JIT are:

Pull System

Production is driven by customer demand rather than forecasts. Each stage of the production process only produces what the next stage needs, and the process starts when the final customer places an order.

Zero Inventory

The aim is to eliminate inventory, both raw materials and finished goods, as much as possible. Inventory is seen as a sign of inefficiency, indicating overproduction and waste.

Continuous Improvement (Kaizen)

JIT is closely associated with the principle of Kaizen, which focuses on continuous improvement in all aspects of the business, including reducing waste, improving efficiency, and enhancing quality.

Toyota is famously known for implementing JIT in its production system, known as the Toyota Production System. Before JIT, Toyota, like many other companies, produced more than necessary and stored surplus goods in warehouses, leading to high inventory costs and waste.

Implementing JIT allowed Toyota to dramatically reduce its raw material, work-in-process, and finished goods inventories. By synchronizing the production rate with consumer demand, Toyota reduced its inventory levels by more than 50%, leading to significant cost savings. It also led to an improvement in quality, as it was easier to detect defects in a system with minimal inventory.

Several studies have demonstrated the benefits of JIT. For instance, a study published in the Journal of Operations Management found that implementing JIT can lead to an improvement in return on assets (ROA) by up to 70%.

However, successful implementation of JIT requires a stable and reliable supply chain, efficient production processes, and accurate demand forecasting. Any disruption in the supply chain, such as supplier failure or transportation delays, can halt production and lead to stockouts. Thus, while JIT can bring significant cost savings and efficiency improvements, it also comes with its own set of risks.

Real-time Tracking

This involves monitoring inventory in real-time. Real-time tracking in inventory management refers to the continuous and instantaneous tracking of inventory items, from the moment they enter the warehouse until they are sold and dispatched. It involves the use of advanced technologies, such as RFID tags, barcodes, IoT devices, and cloud-based software, to monitor and update inventory levels in real-time. With advanced data analytics, real-time inventory management is becoming more economical.

The primary principles of real-time tracking are:

Instantaneous Updates

Every movement of inventory, from receiving and storing to picking and shipping, is immediately recorded and reflected in the inventory levels.

Real-time tracking provides a clear and accurate view of the current inventory levels, location of items, and status of orders at any given time.

By eliminating manual entry and the delay in updating inventory records, real-time tracking significantly improves the accuracy of inventory data.

A case in point is Amazon, which uses real-time tracking extensively in its fulfillment centers. Amazon utilizes RFID tags, automated guided vehicles (AGVs), and sophisticated inventory management systems to track each item in its vast warehouses in real time. This allows Amazon to maintain accurate inventory records, streamline order fulfillment, and provide real-time updates to customers.

Implementing real-time tracking resulted in a significant reduction in order fulfillment time, an increase in warehouse efficiency, and improved customer satisfaction due to the visibility into order status. In terms of numbers, Amazon was reportedly able to reduce its “click to ship” time, that is, the time from when a customer places an order to when it’s shipped, from 60-75 minutes to under 15 minutes.

Several studies have shown the benefits of real-time tracking. According to a report by Zebra Technologies, businesses that implemented real-time tracking reported an average improvement of 32% in inventory accuracy and a 27% acceleration in order cycle times.

However, implementing real-time tracking requires a significant investment in technology and the development of standardized processes. It also involves a shift in mindset from periodic to continuous inventory management. Therefore, businesses need to carefully assess their needs, capabilities, and resources before implementing real-time tracking.

Dropshipping

This involves selling products without stocking them. Dropshipping is a retail fulfillment model in which the retailer does not keep goods in stock. Instead, when a retailer sells a product using the dropshipping model, it purchases the item from a third party—usually a wholesaler or manufacturer—and has it shipped directly to the customer. This eliminates the need for the retailer to handle the product directly, reducing inventory and warehousing requirements. Inventory management with dropshipping needs heavy optimization though.

The fundamental principles of dropshipping are:

In a dropshipping model, the retailer does not own inventory. Instead, inventory is held by the suppliers until it’s sold.

Order Fulfillment

When a customer places an order, the retailer transfers the customer’s order details and shipping information to the supplier, who then fulfills the order directly to the customer.

Product Assortment

As retailers do not need to pre-purchase the items they sell, they can offer a wider variety of products to customers.

One successful case study of dropshipping is Wayfair, a popular online furniture retailer. The company holds virtually no inventory and relies extensively on dropshipping. When a customer places an order on Wayfair’s platform, the order is sent to the manufacturer, who ships the item directly to the customer. This model allows Wayfair to offer a vast selection of products without the need to manage complex inventory or large warehouses.

According to data from a report by Market Research Future, the global dropshipping market size was projected to reach approximately $557.9 billion by 2025, growing at a compound annual growth rate (CAGR) of 28.8% from 2018.

However, the dropshipping model also has its challenges. It can lead to lower profit margins, as suppliers also take their share of profits. Also, because retailers don’t control the entire supply chain, they can face difficulties with product quality control, order fulfillment, and customer service.

Cross-docking

This involves transferring incoming shipments directly to outgoing trucks, reducing warehouse storage needs.

Cross-docking is a logistics strategy in which products from a supplier or manufacturing plant are distributed directly to customers with minimal to no handling or storage time. The term “cross-docking” comes from the process of receiving products through an inbound dock and then transferring them across the dock to the outbound transportation dock. Cross docking is becoming more popular for large organizations with decentralized inventory management

The core principles of cross-docking include:

In cross-docking, speed is key. The objective is to unload materials from an incoming semi-trailer truck or rail car and then directly load these materials onto outbound trucks or trailers with little to no storage in between.

Synchronization

The success of cross-docking depends on the synchronization of inbound and outbound transport. The goal is to ensure that the incoming goods arrive just in time to be loaded onto the outbound transport.

Cross-docking requires a central site where the routing of products is handled. At this site, products are received from multiple sources and then sorted onto outbound trucks going to different destinations.

An effective example of cross-docking is Walmart’s distribution model. Walmart’s suppliers send full truckloads of products to a Walmart distribution center, where they’re then distributed to individual stores in less-than-truckload (LTL) quantities. Walmart uses cross-docking efficiently to get products from suppliers to their stores without holding inventory at the distribution centers. It helps them to reduce inventory holding costs, minimize storage requirements, and get products into stores faster, which is crucial in retail industries.

According to a study published in the International Journal of Retail & Distribution Management, the implementation of cross-docking can lead to a reduction in order cycle time by 33%, and inventory reduction by up to 50%.

However, to successfully implement cross-docking, companies require significant planning, investment in a central routing facility, and sophisticated logistics software to coordinate and synchronize transport schedules. It’s also crucial to have reliable suppliers that can adhere to strict delivery schedules. If these conditions aren’t met, cross-docking can lead to increased transportation costs, delivery delays, and customer dissatisfaction.

Inventory Management Software

Companies like Oracle, SAP, and Microsoft offer advanced inventory management software that can automate various processes, resulting in reduced errors and increased efficiency.

Inventory management software is a tool that helps businesses track and manage their inventory levels, sales, orders, and deliveries. It can also be used to create purchase orders, back orders, and invoices. The software’s primary purpose is to avoid product overstock and outages, ensuring that the right amount of stock is maintained at all times. Using these software are essential for building a strong inventory management system

The key features of inventory management software include:

Inventory Tracking

This feature allows businesses to track their inventory levels in real time. The software can provide updates when stock is low or when it’s time to reorder.

Barcode Scanning

Many inventory management systems come with barcode scanning capabilities. This allows businesses to quickly input and track products, reducing manual errors.

Reporting and Analytics

These features provide businesses with insights into their inventory levels, sales trends, and order history. This can help businesses make more informed decisions about stock management, pricing, and sales strategies.

Integration Capabilities: Inventory management software can often be integrated with other business systems, such as accounting software, e-commerce platforms, and CRM systems, to streamline operations.

One example of a company leveraging inventory management software is Zara, the Spanish clothing retailer. Zara uses sophisticated inventory management software to track each item in real-time, from when it’s manufactured to when it’s sold. This allows Zara to keep track of its fast-moving inventory, respond quickly to changes in demand, and minimize stockouts and overstock.

According to a report by Mordor Intelligence, the global inventory management software market was valued at USD 2.42 billion in 2020 and is expected to reach USD 5.6 billion by 2026, growing at a CAGR of 15% during the forecast period (2021-2026).

However, implementing inventory management software requires a significant investment in technology and may require staff training. It also requires the collection and analysis of accurate data. Therefore, businesses need to carefully evaluate their needs and resources before deciding to invest in inventory management software.

Consignment Inventory

Here, payment to suppliers is made only when their goods are sold.

Consignment inventory is a business model in which a consignee (retailer) agrees to receive and store products from a consignor (supplier or manufacturer), but the consignor retains ownership of the products until they are sold. Once the product is sold, the consignee pays the consignor for the inventory. It is very similar yet different from dropshipping model of inventory management.

The core principles of consignment inventory include:

In a consignment inventory agreement, the consignor retains the ownership of the goods until they are sold. This implies that the consignor bears the financial risk of the inventory until the point of sale.

The consignee pays for the inventory only after it is sold to the end consumer. The unsold inventory can be returned to the consignor, reducing the financial risk for the consignee.

Inventory Management

Typically, the consignor is responsible for managing the inventory, which includes replenishing stock when levels are low and removing outdated or unsold merchandise.

A great example of the consignment model is the relationship between book authors (consignors) and bookstores (consignees). Many bookstores will display books, particularly from unknown or independent authors, on a consignment basis. This allows the bookstore to offer a wide range of titles without the risk of investing in inventory that may not sell.

According to data from Stitch Labs, using a consignment model can increase revenue by consignment model can increase revenue by as much as 20%, as retailers can offer a wider variety of products without the risk of unsold inventory.

However, there are potential downsides to the consignment model. For consignors, delayed revenue recognition and the risk of not selling the inventory can be significant. For consignees, consignment inventory can take up valuable retail space without providing immediate revenue. Also, managing consignment inventory can be complex, requiring careful tracking and accounting to ensure accurate payment upon sale.

Cycle Counting

This involves regularly counting a subset of inventory.

Cycle counting is an inventory auditing technique where a small subset of inventory, in a specific location, is counted on a specified day. It is a method used by businesses to count their inventory continuously and cyclically throughout the year, rather than counting all inventory at once during a full physical inventory count. Cycle counts contrast with traditional physical inventory counts, where operations are halted once or twice a year to count all inventory items. To bring discipline into inventory management this process is necessary.

The essential principles of cycle counting are:

Consistent Counting

In a cycle counting system, a small, specific subset of inventory is counted at regular intervals, ensuring ongoing accuracy.

Inventory items are not all counted at the same frequency. High-value items, fast-moving items, or items critical to business operations are often counted more frequently.

Division of Labor

By breaking down the task of inventory counting into smaller parts, the job can be completed without disrupting normal operations.

A successful implementation of cycle counting can be found at Apple Inc. Apple uses cycle counting to maintain an accurate inventory record. This technique helps them to identify and correct potential problems early, reducing discrepancies between actual and recorded inventory.

A study by the Association for Supply Chain Management (APICS) found that companies using cycle counting systems could achieve inventory accuracy levels of 97% or higher. This contrasts with a traditional annual physical inventory system, which often results in lower overall accuracy due to a lack of frequent validation.

While cycle counting can significantly improve inventory accuracy, its implementation is not without challenges. It requires a consistent and ongoing effort and requires businesses to invest in proper training for staff to conduct the counts effectively and efficiently. If not appropriately managed, cycle counting can lead to discrepancies and errors.

Inventory Shrinkage Control

Implementing measures to control shrinkage can reduce inventory losses. Inventory management thus takes central stage.

Inventory shrinkage refers to the loss of products between the point of manufacture or purchase from suppliers and the point of sale. It is a significant issue that can impact a company’s bottom line. The main causes of inventory shrinkage include theft, damages, miscounting, and supplier fraud. Instilling discipline is required for any type of inventory management.

Here are the key principles to control inventory shrinkage:

Regular Audits

Regular inventory audits, including cycle counting, can help identify shrinkage early and mitigate its impact. It allows you to identify discrepancies between your physical inventory and your inventory records, indicating potential shrinkage.

Security Measures

Implementing security measures, such as surveillance cameras, electronic article surveillance (EAS) systems, and security personnel, can deter theft, one of the significant contributors to inventory shrinkage.

Employee Training

Well-trained employees are more likely to handle inventory properly, reducing losses due to damage or miscounting. Also, educating them about the consequences of theft, including job loss and legal action, can deter internal theft.

Vendor Management

Establishing strong relationships with vendors and carefully monitoring their activities can help prevent supplier fraud. This can include checking shipments for accuracy and quality.

One successful case study of shrinkage control is at Target Corporation. The company employs advanced analytics to identify patterns in theft and uses electronic surveillance systems throughout their stores. They also have a rigorous vendor vetting process to prevent vendor fraud. These measures have reportedly resulted in a substantial reduction in inventory shrinkage.

According to the National Retail Federation’s 2020 National Retail Security Survey, the average inventory shrink rate in the U.S. retail industry is around 1.62% of sales. This may seem like a small percentage, but given the volume of sales in the retail industry, it can amount to billions of dollars.

Centralized Inventory

Centralizing inventory can reduce costs and improve efficiency.

A centralized inventory management system is a method where a company maintains its entire inventory from one central location or few select locations. Rather than keeping stock in various places such as individual stores or warehouses scattered across different regions, all products are kept in one central warehouse from which they’re distributed to individual sale locations or directly to consumers. Organizations are looking forward to centralized inventory management after Covid disruptions.

The core principles of centralized inventory management include:

Central Location

In centralized inventory management, all inventory is managed from one central location. This could be a central warehouse, distribution center, or a fulfillment center.

Streamlined Supply Chain

Centralizing inventory allows for a more streamlined supply chain as all goods come in and go out of the same place. This can make it easier to manage and keep track of inventory.

Consolidated Management

With centralized inventory, inventory management is consolidated. This means that one team (or sometimes one person) can oversee the entire inventory, which can lead to more effective management and decision-making.

One notable example of successful centralized inventory management is Amazon. Amazon keeps its inventory in large, strategically located fulfillment centers from where they dispatch products directly to customers. This centralized system allows Amazon to manage and control its inventory effectively and deliver products rapidly.

According to a report from Accenture, centralizing inventory management can lead to a 10% – 20% reduction in inventory carrying costs. It also can help companies enhance customer service and increase sales by ensuring the right products are available at the right time.

However, there are potential drawbacks to centralized inventory management. One of the main risks is that if the central warehouse encounters a problem (like a natural disaster or a major system failure), it can disrupt the entire supply chain. Also, centralized inventory may not always provide the speed needed for rapid delivery to distant locations.

Samrat is a Delhi-based MBA from the Indian Institute of Management. He is a Strategy, AI, and Marketing Enthusiast and passionately writes about core and emerging topics in Management studies. Reach out to his LinkedIn for a discussion or follow his Quora Page

Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature

• Review Article
• Published: 07 February 2023
• Volume 30 , pages 2605–2625, ( 2023 )

Cite this article

• Özge Albayrak Ünal   ORCID: orcid.org/0000-0001-7798-8799 1 ,
• Burak Erkayman   ORCID: orcid.org/0000-0002-9551-2679 1 &
• Bilal Usanmaz   ORCID: orcid.org/0000-0003-0531-4618 2  

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Today, companies that want to keep up with technological development and globalization must be able to effectively manage their supply chains to achieve high quality, increased efficiency, and low costs. Diversified customer needs, global competitors, and market competition have led companies to pay more attention to inventory management. This article provides a comprehensive and up-to-date review of Artificial Intelligence (AI) applications used in inventory management through a systematic literature review. As a result of this analysis, which focused on research articles in two scientific databases published between 2012 and 2022 for detailed study, 59 articles were identified. Furthermore, the current situation is summarized and possible future aspects of inventory management are identified. The results show that the interest in AI methods has increased in recent years and machine learning algorithms are the most commonly used methods. This study is meticulously and comprehensively conducted so it will probably make significant contributions to the further studies in this field.

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Albayrak Ünal, Ö., Erkayman, B. & Usanmaz, B. Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature. Arch Computat Methods Eng 30 , 2605–2625 (2023). https://doi.org/10.1007/s11831-022-09879-5

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Inventory Management-A Case Study

As a result to today's uncertain economy, companies are searching for alternative ways to stay competitive. This study goes through the process of analyzing the company's current forecasting model and recommending an inventory control model to help them solve their current issue. As a result, an Economic Order Quantity (EOQ) and a Reorder Point was recommended to help them reduce their product stock outs. The shortage of raw material for production always makes the process discontinuous and reduces the productivity. The ABC analysis technique for the inventory control system is first used to identify the most important multiple products and then the economic order quantity (EOQ) of each product is developed to find their inventory model equation individually.

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Case Study 7A

• Open access
• Published: 01 April 2024

Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe’s national blood bank

• Coster Chideme 1 ,
• Delson Chikobvu 1 &
• Tendai Makoni 1  

BMC Public Health volume  24 , Article number:  928 ( 2024 ) Cite this article

Metrics details

The discrepancy between blood supply and demand requires accurate forecasts of the blood supply at any blood bank. Accurate blood donation forecasting gives blood managers empirical evidence in blood inventory management. The study aims to model and predict blood donations in Zimbabwe using hierarchical time series. The modelling technique allows one to identify, say, a declining donor category, and in that way, the method offers feasible and targeted solutions for blood managers to work on.

The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used. The data was disaggregated by gender and blood groups types within each gender category. The model validation involved utilising actual blood donation data from 2019 and 2020. The model's performance was evaluated through the Mean Absolute Percentage Error (MAPE), uncovering expected and notable discrepancies during the Covid-19 pandemic period only.

Blood group O had the highest monthly yield mean of 1507.85 and 1230.03 blood units for male and female donors, respectively. The top-down forecasting proportions (TDFP) under ARIMA, with a MAPE value of 11.30, was selected as the best approach and the model was then used to forecast future blood donations. The blood donation predictions for 2019 had a MAPE value of 14.80, suggesting alignment with previous years' donations. However, starting in April 2020, the Covid-19 pandemic disrupted blood collection, leading to a significant decrease in blood donation and hence a decrease in model accuracy.

Conclusions

The gradual decrease in future blood donations exhibited by the predictions calls for blood authorities in Zimbabwe to develop interventions that encourage blood donor retention and regular donations. The impact of the Covid-19 pandemic distorted the blood donation patterns such that the developed model did not capture the significant drop in blood donations during the pandemic period. Other shocks such as, a surge in global pandemics and other disasters, will inevitably affect the blood donation system. Thus, forecasting future blood collections with a high degree of accuracy requires robust mathematical models which factor in, the impact of various shocks to the system, on short notice.

Peer Review reports

Introduction

Blood transfusion requirements are on the rise globally as a result of accidents, diseases and advanced surgeries. Zimbabwe often experiences shortages in the majority blood group O during public holiday periods. This is mainly due to the high demand for clinical blood transfusion as a result of a surge in road accidents and injuries during such periods [ 1 ]. Type O blood is the most needed blood type in transfusion centres and more than 52% of Zimbabweans are in blood group O [ 2 ]. Accurate forecasts of the number of volunteer donors and blood donations help blood service managers in managing their categories of blood inventories and plan accordingly for the education and recruitment of voluntary non-remunerated blood donors and subsequent blood collection.

In the blood supply chain, future forecasting of blood supply is a critical step to ensure the adequate availability of safe blood when clinical transfusion is required. Accurate and coherent blood donation forecasting provides blood managers with empirical evidence regarding when to order blood, educate and recruit new blood donors, the estimated quantities required of each blood group to collect and potential donor categories to target.

The blood supply chain is dynamic, and as such, some studies have expressed concern over the potential reduction in blood donations emanating from multiple factors including donor demographical variations [ 3 ]. Time series analysis can be used to understand the patterns in blood donation data and help blood managers in predicting future blood donations. This information is useful for minimising volatility in blood stocks and preventing blood stockouts. Understocking blood has detrimental effects on patient safety in the healthcare system, while overstocking results in wasteful discards of outdated blood [ 4 , 5 , 6 , 7 , 8 , 9 ]. Accurate and coherent forecast of blood donations are vital as part of the decision support system for blood centre authorities as they need to know the future of blood supply when given the surge in daily blood demand [ 10 ].

The blood supply chain is dependent upon a finite number of donors. This is then aggravated by the fact that blood donation is very irregular and uncertain [ 11 ]. Blood donations/demand estimates based on employee opinions, experience, and intuition rather than quantitative models are currently used to determine both the current and future blood provisions in most blood centres globally, and especially in developing countries [ 12 ]. The unavailability or non-use of quantitative models in estimating blood donations can indeed cause volatility and uncertainty in the blood supply chain. Without these models, it can be difficult to accurately predict how much blood will be available for clinical transfusions, and this can lead to shortages or excesses of blood in certain areas. The application of quantitative prediction models in a blood bank helps to reduce errors in decision-making about the quantity of blood to be supplied and demanded [ 13 ].

With increased demand for blood and blood components against a declining voluntary blood donor pool, improving the availability and safety of the blood supply, and forecasting become vital for sustaining any blood bank to meet its core mandate. Numerous techniques have been applied in time series forecasting in general, such as autoregressive integrated moving average (ARIMA), exponential smoothing (ES), fuzzy systems (FS), artificial neural networks (ANN), logistic regression, support vector machine (SVM) and hierarchical time series forecasting. Hierarchical time series forecasting allows forecasting of time series at different levels of a hierarchical structure whilst preserving the relationships and dependencies within the hierarchy. Furthermore, the forecasts at each hierarchical level are aggregated or disaggregated to give the forecasts at higher or lower levels in the hierarchy.

The correlation between data of donor specific characteristics and blood donations can result in huge datasets of times series, which can then be classified into clusters or hierarchies. The essence of hierarchical forecasting in blood donation is derived from the fact that the blood donors can be categorised into various clusters such as gender, blood group type, and donor status. Data from the NBSZ indicates that male blood donors constitute about 54% of the donor pool and the female donors accounting for the remaining 46%. Also, the donations are classified according to the ABO donor blood group system with blood group O accounting for 54%, blood group A constitutes 24%, blood group B, 18% and blood group AB, 4%.

Hierarchical time series is effective in forecasting hierarchically organised data which can be aggregated and disaggregated at different levels [ 14 ]. In the blood supply chain, the total blood donation forecast is required at the top level of the hierarchy for inventory planning, resource allocation and other blood drive logistics. It is possible to create a hierarchical structure that captures the relationships between these different categories of donors. For example, at the highest level of the hierarchy, there may be forecasts for the overall national blood supply. At the next level down, there may be forecasts for the different gender of donors, viz: male and female. At the next level down, there may be forecasts for the different blood group types, viz: A, B, AB, and O. When these different levels based on donor characteristics are not factored in, this may result in incoherent time series forecasting, less targeting of interest groups, resulting in not being able to meet blood demand of a particular type in a given area(s).

The aim of the study is to use a hierarchical time series forecasting approach to predict blood donation patterns. By using this approach, it is possible to create more accurate and detailed forecasts for blood donations, taking into account the relationships and dependencies between different categories of donors. This can help blood banks to better manage their inventory and ensure that they have enough blood for the right group, adequate number of units in an area given at the right time to meet patient needs. To the best of our knowledge, the application of hierarchical time series in the blood supply chain problems has not been investigated, especially in the context of Zimbabwe and Africa, considering the available blood supply chain forecasting literature. Hierarchical forecasting is a very instrumental statistical technique to support decision-making in most supply chains [ 15 ], hence its application in blood donation projections is vital.

Literature review

Forecasting hierarchical time series is a relatively new to the forecasting phenomenon. Hierarchical time series forecasting has gained wider application in recent years [ 16 ]. Many phenomena in the real world, such as stock prices, weather, consumer demand, tourism demand, blood supply system, just to mention a few, can be modelled using the hierarchical time series. However, the correlation of different points of the time series makes some of the algorithms less versatile in forecasting [ 13 ]. A multivariate time-series model based on long-short-term memory (LSTM) in forecasting blood donation and demand during the Covid-19 pandemic at Tehran Blood Centre in Iran [ 17 ]. The LSTM is a recurrent neural network-based deep learning model. The study results showed that the forecasting model reduced blood shortage and wastage by 5.5% when compared to existing forecasting methods, such as the ARIMA, used the time series models in forecasting blood donation at a university medical care centre in Portugal [ 11 ]. The study developed six models, viz: ETS, Holt-Winters, autoregressive neural networks, ARIMA, double-seasonal Holt-Winters, and exponential smoothing (ES). The study concluded that trend lines of donations were better modelled by different models with different forecasting horizons. However, the ARIMA model outperformed all the other models in generating forecasts, hence the ARIMA model is part of the hierarchical forecasting approach to be adopted in this study, forecasted the supply of blood at blood centres in Taiwan using data from the Taiwan Blood Services Foundation [ 18 ]. They applied two different techniques in forecasting, viz: times series and machine learning. Under time series, they employed autoregressive (AUTOREG), ARMA, ARIMA, seasonal ARIMA (SARIMA), seasonal exponential smoothing model (ESM) and Holt-Winters. Under the machine learning algorithms, they used ANN and multiple regression. The study results showed that time series forecasting methods (seasonal ESM and ARIMA models) generated accurate predictions when compared to machine learning algorithms. Hence, this study will adopt ARIMA and ETS models, concurred that blood donation was influenced by transfusion demand [ 19 ]. The study forecasted red blood cells demand using three-time series methods, viz: ARIMA, Holt-Winters and neural-network-based method. The study results showed that a SARIMA model produced accurate forecasts over a shorter time horizon of one year. The ES outperformed the other methods over longer time horizons stated that managing blood supply and demand was difficult in most blood banks globally [ 20 ]. They highlighted the need for accurate and reliable blood supply and demand forecasting models. They conducted a study at the National Health Service Blood and Transplant in England using four different time series methods which were selected using the minimum mean squared error (MMSE) and weighted least squares error (WLSE). The methods yielded similar results.

A study concluded that there is no single statistical forecasting technique that is universally better and applicable at all times [ 21 ]. The authors conducted blood demand forecasting in Finland and the Netherlands. They applied moving averages (MA), ETS, ARIMA, autoregressive neural networks (NNAR), seasonal naïve (SNAIVE), method averaging (AVG), seasonal trend decomposition methods (STL and STLF), dynamic seasonal method (TBATS), dynamic regression (DYNREG), multilayer perceptrons (MLP) and extreme learning machine (ELM). The model performances were compared using mean absolute percentage errors (MAPEs). The results show that DYNREG performed better than the other approaches in generating forecasts emphasised the importance of accurate predictions in blood provision [ 22 ]. Their study at Shirazi blood centre in Iran applied ARIMA, ANN and hybrid approaches in forecasting different blood groups demand. Mean Square Error (MSE) and Mean Absolute Error (MAE) were used to compare and validate the fitted models. The results showed that ARIMA model outperformed the other models in the forecasting accuracy [ 23 ] forecasted the demand in the blood supply chain using platelets at Canadian Blood Services. The study used five different forecasting methods, viz: ARIMA, Prophet, lasso regression (least absolute shrinkage and selection operator), random forest and LSTM (Long Short-Term Memory) networks. The results showed that with limited data, multivariate models performed better than univariate models. However, with adequate data, ARIMA models produced similar results to multivariate methods. The current study uses both the ARIMA and the ETS under the hierarchical forecasting approach models.

Material and methods

Secondary data used in this study corresponds to the grand total of blood collections (blood units) from the five regional blood centres in Zimbabwe based on specific donor characteristics (gender and blood group). This information is useful for developing a hierarchical structure for forecasting blood donations, as it enables the researchers to consider the relationships and dependencies between different categories of blood group types and blood donors. The data was collected from the NBSZ Laboratory Information Management System (LIMS) and annual reports which are freely available on the link https://nbsz.co.zw/ , where certain blood donations information is captured in aggregate form. Monthly blood donation data covering the period 2007 to 2018 was used in the forecasting, giving a total of 144 monthly observations.

Using the approach [ 14 ], a tree diagram of blood donations comprising a two-level hierarchical structure is presented in Fig.  1 . The tree diagram is constructed based on the disaggregated blood data that was categorised according to two variables: gender (Male and Female) and blood group type (A, B, AB and O). Level 0 represents the total blood donations in Zimbabwe. Level 1 denotes the first disaggregation by gender (Male (M) and Female (F)). Level 2 denotes further disaggregation by blood groups according to the ABO blood group system (A, B, AB and O).

Blood donations hierarchical structure based on donor gender and blood group type

The R-package HTS is used to generate the forecasts using the bottom-up, top-down and the optimal combination methods. The EST and ARIMA methods are used to generate the forecasts.

Hierarchical forecasting techniques

(Fig.  1 . Blood donations hierarchical structure based on donor gender and blood group type).

According to Fig.  1 , level 0 gives completely aggregated blood donations (Total blood donations) denoted by \({Y}_{TB,t}\) where \(t=1, 2, 3, . . . , 144\) and are obtained by adding all the series at level 1 or level 2. Level 1 represents data disaggregated according to gender (male and female). Level 1 and level 2 series can be denoted by  \({Y}_{i,t}\) , where \(i\) denotes the node in the hierarchical tree diagram. The data consists of 144 monthly observations (t = 1, 2, …, 144). Forecasts for each level were estimated using the bottom-up, top-down and optimal combination approaches. The approach with a lower accuracy measure estimated by MAPE was used to generate forecasts for the blood centre.

Let \({{\varvec{Y}}}_{t}\) and \({{\varvec{S}}}_{11X8}\) be the vector of the blood data and a summing matrix storing the hierarchical structure shown in Fig.  1 respectively.

Making use of the summing matrix ( S ), Eq.  1 can be written as

The bottom-up method

The bottom-up approach involves forecasting individually for each series at the lowest levels of the hierarchy and then aggregates the forecasts upwards to generate forecasts for higher levels. The method is based on forecasting the individual blood donations from the blood group type A, B, AB and O first. Total number of blood donations for each gender can be calculated by summing up the forecasted donations made by individuals of all blood groups. Then, by summing up the donations made by each gender, one can determine the total number of blood donations for the blood bank. In other words, the approach is concerned with producing individual base forecasts at the lower level of the hierarchy and combining the forecasts upwards through \({\varvec{S}}\) . Thus, the approach starts by producing h -step-ahead forecasts for individual bottom level time series ( \(n = 8\) ):

\({\widehat{Y}}_{AM,h},\) \({\widehat{Y}}_{BM,h,}\) \({\widehat{Y}}_{ABM,h},\) \({\widehat{Y}}_{OM,h},\) \({\widehat{Y}}_{AF,h},\) \({\widehat{Y}}_{BF,h,}\) \({\widehat{Y}}_{ABF,h},\) and \({\widehat{Y}}_{OF,h}\)

These forecasts are aggregated to get the h -step-ahead forecasts for the higher level (level 1). Level 1 h-step-ahead forecasts ( \({\widetilde{Y}}_{AM,h}, {\text{and}} {\widetilde{Y}}_{BF,h}\) ) are given by

The summing matrix ( \({\varvec{S}})\) will combine the h -step-ahead forecasts up the hierarchical structure. For the bottom-up approach, the forecasts are combined using the formula:

where \(k=0, 1, 2.\)

The advantage of the bottom-up approach is that no information is lost since forecasts are generated at the lowest or base level of the hierarchy. The major setbacks of the method are that, it performs poorly on highly aggregated data and it does not take into account the correlations between the series. Also, too much data points in the base level of the hierarchy requires more runtime to generate forecasts. The bottom-up method is not effective in the case of complex and multi-layered hierarchies [ 24 ]. The time series at the lowest levels often have little structure and are therefore difficult to forecast and this can result in forecasting errors which can be aggregated over numerous upper hierarchies.

The top-down method

This method forecast the highest level of the hierarchy first and then split up the forecast to generate estimates for the lower levels through the use of some proportions or factors. These proportions include average historical proportions, proportions of the historical averages and forecast proportions [ 16 , 25 ]. Historical data is used in the calculation of the proportions and the approach has the ability to yield reliable forecasts for the aggregate levels [ 26 ]. The average historical proportions formula is:

where \(i=\mathrm{1,2}, \dots , {m}_{k}.\) According to [ 26 ], every proportion reveals the average of the historical proportions of the bottom level series over time relative to the aggregated series ( \({Y}_{t})\) for \(t=1, 2, 3, . . . , N \left(N=144\right).\) Using one of the nodes in Fig.  1 and the bottom level series \({Y}_{OF,t}\) as an example, we can have;

where \({\widehat{S}}_{Total,t}={\widehat{Y}}_{M,t}+ {\widehat{Y}}_{F,t}\) and \({\widehat{S}}_{F,t}={\widehat{y}}_{AF,t}+{\widehat{y}}_{BF,t}+{\widehat{y}}_{ABF,t}+{\widehat{y}}_{OF,t}\)

Advantages of the method is that it provides reliable forecasts for higher levels in the hierarchy and is useful when the lower-level series are noisy and difficult to forecast. The major setback of the method is that there is general loss of information resulting in less accurate forecast being generated at base or lower levels of the hierarchy [ 27 ].

Optimal combination method

Handyman RJ et al. [ 14 ] proposed an optimal combination approach for forecasting that utilises all the available information and combinations in a hierarchy. This approach involves making independent forecasts at all levels, which are then reconciled using a linear regression model. The resulting forecasts are coherent and based on weights obtained by solving a system of equations that respect the relationships between the different levels of the hierarchy. This method can estimate the unknown future expectation values of the lowest level of the dataset, K. Given a vector of the unknown means ( \({{\varvec{\beta}}}_{n}(h))\) , thus,

Since \({{\varvec{Y}}}_{t}\) represents the vector of all observations at time t while and \({{\varvec{Y}}}_{k,n+h}\) represents the vector of observations in the bottom level K . The base forecasts ( \({\widehat{{\varvec{Y}}}}_{n}\left(h\right))\) are presented in a regression format to give:

where \({{\varvec{\varepsilon}}}_{h}\) denotes a white noise process with covariance matrix \(\sum h\) which is difficult to find in large hierarchies [ 26 ]. However, [ 14 ] proposed estimating the white noise process by the forecast error in the bottom level, thus, \({{\varvec{\varepsilon}}}_{h}\approx {\varvec{S}}{{\varvec{\varepsilon}}}_{k,h}\) . With this hypothesis, errors satisfy the same aggregation constraint as the dataset, resulting in

The optimal combination approach has a key advantage in that it is capable of producing highly accurate forecasts in comparison to both top-down and bottom-up methods. Additionally, it allows for unbiased forecasts to be generated at all levels while minimising the loss of information. This approach also enables the utilisation of diverse independent forecasting methods, such as ARIMA and ETS, at each level to generate the most accurate forecasts possible. However, one significant drawback of the optimal combination approach is that it can become very complex and computationally intensive when dealing with numerous time series.

Forecasting individual series

The ETS and ARIMA are the common methods used. The general ARIMA model can be expressed as

where \(\Phi {\prime}s\) and \(\mathrm{\Theta {\prime}}{\text{s}}\) are model parameters.

\({Y}_{t}\) – is the stationary series,

\({\Phi }_{p}\) – is the coefficient of the p th AR term, where p is the order of the AR term,

\({\Theta }_{q}\) – is the coefficient of the q th MA term, where q is the order of the MA term,

\({a}_{t}\) —is the error term.

The general forms of the Holt-Winters with permanent constant, linear trend and multiplicative seasonal variations are:

where the smoothing parameters ( \(\gamma ,\) \(\alpha\) and \(\beta\) ) take values between 0 and 1. The smoothed series and seasonality period is denoted \({\widetilde{Y}}_{t}\) and \(s\) respectively. Both the ETS and the ARIMA default algorithms are incorporated in the R forecast package HTS. The mean absolute percentage error (MAPE) was used to assess forecasting performance of the models. The MAPE formula is:

where \({y}_{t}\) are the actual blood donation values observed, \({\widehat{y}}_{t}\) are predicted blood donation values by the model and \(m\) is the prediction period.

Model validation

The disruptions caused by the Covid-19 pandemic altered blood donation patterns, complicating the forecasting of future blood donations for this specific period of the pandemic. The model validation involved utilising actual blood donation data from January 2019 to December 2020. The model's performance was evaluated through the Mean Absolute Percentage Error (MAPE) suggesting alignment with previous years' donations during the pre-pandemic period, however there MAPE confirmed notable discrepancies between forecasts and observed values during the period of the Covid-19 pandemic.

Data and descriptive statistics

Table 1 gives information on the structure of the hierarchy as depicted in Fig.  1 .

The descriptive statistics of the data are shown in Table  2 .

Monthly mean blood donations for blood type A were 725.02 and 591.47 for males and females, respectively. Blood group O had the highest monthly mean as expected, 1507.85 and 1230.03 for male and female donors respectively. Blood group AB had the least mean donations 115.09 and 94.69 for male and female donors respectively. The negative kurtosis (platykurtic) shows that more donation data are located near the mean and less values are located on the tails thus no cases of extreme values or outliers.

The characteristics of the disaggregated blood donations are depicted in Fig.  2 .

Time series plots based on donor gender and blood group type from 2007 – 2018

In Fig.  2 , the total blood donations at level 0 exhibit some seasonality. There are no significant variations in the blood donation patterns even though there were some periods of declines in blood donations. At level 1, the male donations (M) surpassed their female counterparts (F). It is evident from Fig.  2 that blood group O donations (OM and OF) have the highest volumes, followed by blood group A and blood group AB being the least. At level 2, male blood group O had a maximum of 2888 units, female blood group O had a maximum of 2269 units and female blood group AB had the least maximum of 175 units. Such insights help blood centre authorities to plan for blood donor education and recruitment scheduling, fixed and mobile drives, blood collection and also meeting clinical blood transfusion needs.

Forecasting accuracy evaluation

The MAPE accuracy measure was used to assess the forecasting performance of the models. An out-of-sample forecasting accuracy measure is done. Table 3 presents accuracy done for both the ETS and ARIMA as forecasting methods.

The average accuracy measures from each model are under the row named “Average”. It is shown in Table  3 that the TDFP approach produces small MAPE values under the ARIMA forecasting method forecasting method. The TDFP under ARIMA with MAPE error of 11.30 is the best and is used to forecast future blood quantities. Table 4 and Fig.  3 show out-of-sample forecasted future values for 60 months and their graphical display.

Blood donation forecasts from 2019 – 2023 using TDFP under ARIMA

From Fig.  3 , future blood donations forecasts are indicated by the dashed/dotted line(s) while the historical data are represented by solid line(s). At level 1, future projections show that, male donations are higher than female donations. Similarly, at level 2, the projected donations for blood group O for males (OM) are higher than for their female (OF) counterparts. It is evident from all the three-levels in Fig.  3 that there could be a steady to slight decline in future blood donations for all the donor categories based on the projections. This can be attributed to a real problem of a continuous decline in numbers of regular voluntary blood donors in most blood centres. Low blood donations for blood group AB are projected to continue in the short to long term periods. This calls for the development of sound policies and interventions in blood donor and blood management (Table  5 ).

The blood donation predictions for 2019 had a MAPE value of 14.80, suggesting alignment with previous years' donations. However, starting in April 2020, the Covid-19 pandemic disrupted blood collection, leading to a significant decrease in blood donation and hence a decrease in model accuracy, and this is then reflected in a high MAPE value of 84.06.

The aim of this study was to explore blood donation forecasting technique that could generate accurate and coherent predictions. The blood donation data in Zimbabwe recorded by the NBSZ was categorised according to donor specific characteristics. These categories gave rise to hierarchical time series forecasting. The forecasts from the approach suggest the need for effective donor education and recruitment drives targeting blood group O donors since they are universal blood donors and blood type O is always on high demand. These methods are strongly recommended as they give feasible solutions. They capture blood donor data dynamics, produce precise and sensible forecasts.

Previous studies have attributed patterns in blood supply to socio-demographic characteristics [ 28 , 29 , 30 ]. These donor specific characteristics give rise to clusters warranting the application of hierarchical forecasting. Therefore, there is need to make blood donation projections based on blood donor socio-demographic characteristics.

A study by [ 31 ] projected future blood donors in Birjand City, Iran using decision trees. Their models yielded poor performance based on the measures of accuracy. They concluded that the trees had numerous disaggregation of the data leading to data overfitting. Results from the current study indicated that the data disaggregation helped in generating accurate and coherent forecasts.

A time series analysis by aggregating blood donation frequency by month was conducted by [ 32 ]. The study results showed a stable blood supply for most months except in June and September periods which coincide with religious festivals in Saudi Arabia. The current study results showed seasonality in the donation patterns. The seasonality is linked to public holiday months and school holidays in Zimbabwe during the months of April, August and December each year.

Forecasting blood donation based on blood group prevalence is vital in managing blood supply at a blood bank [ 33 ]. Keeping track of dynamic changes in the donation prevalence of different blood groups is important since the distribution of the blood groups varies with time [ 34 , 35 ]. Also, [ 36 ] concluded that blood donors with blood group O had higher frequency of blood donations and a lesser risk of lapsing, leading to the need for high blood donation volumes compared to other blood groups.

The current study shows that blood donations from blood group O donors have the highest volumes of donations compared to the other blood groups. This can be associated with the fact that the proportion of blood group O is highest in the donor population in Zimbabwe (52%). At the same time, blood group O donors are referred to as universal donors because blood type O can be transfused to blood A and B patients in emergencies where there was no time for matching blood types. However, it is current best practice to transfuse group-specific blood. Such insights help blood centre authorities to plan for blood donor education and recruitment, schedule blood drives and blood collections and also meeting clinical blood transfusion needs.

The results from the blood donation projections by gender concurs with other researchers where male donations are consistently higher than their female donations [ 37 , 38 , 39 ]. The current study also shows similar trends where male blood donors had higher mean blood donations compared to the female donors. Males have a higher frequency of donations as they are allowed, through regulation, to donate blood after every 12 weeks compared to 16 weeks interval for the female donors. Other researchers have attributed the lower donation volumes of female donors to high donor lapsing compared to male donors [ 36 , 40 ].

Women generally donate blood less than men due to deferrals as a result of iron depletion through menstrual blood loss.

The blood donation projections from the study have some clinical implications. Some previous studies have shown that the survival rate of patients transfused with blood from male donors was higher compared to female donors [ 41 ]. Therefore, the higher proportion of male donors in the pool is vital in clinical blood transfusion. Also, Zimbabwe often experiences shortages in blood group type O. Therefore, the higher proportion of blood group O donors in the projections will help blood authorities in rationalising blood donor education and recruitment to minimise blood shortfalls.

Blood collections trend took a down turn from April 2020 as the government of Zimbabwe introduced Covid-19 lockdown restrictions to reduce the spread of the pandemic. These measures rendered most blood collection sites inaccessible as movement of people was restricted. The NBSZ had to rely on community based and walk in blood donors and this resulted in a 40% decrease in units of blood collected compared to 2019. The same negative impact of the Covid-19 pandemic can be observed from 2021 up to June 2022. This means that alternative models could be developed in future studies to analyse the impact of pandemics in forecasting blood donations. A time series with intervention model would be an ideal alternative candidate. The model focuses on the shock or pulse that results after say, a pandemic.

The discrepancy between blood supply and demand and the perishability of blood and blood components can be alleviated somewhat through accurate forecasts of the blood supply at any blood bank. Such accurate and coherent forecasts help in safeguarding the risks of understocking and overstocking the scarce and perishable resource, blood. Thus, accurate statistical forecasting methods play a significant role in future blood donation projections. The top-down, bottom-up and optimal combination approaches were adopted in the study with each approach having its own merits and demerits. The EST and ARIMA methods were used to generate the forecasts. The TDFP under ARIMA with the smallest MAPE was considered to be the best and was then used to forecast future blood donations.

Future blood forecasts indicated a slight decrease in total blood donations. This suggests the need for blood centre authorities to develop sound blood donor management interventions. Such interventions include an integrated strategy of the entire blood safety value chain, including donor education, targeted recruitment and retention, scheduled fixed and mobile blood donation drives, safe blood collection and donor care and adequate resource allocation.

Study results showed that blood donations from blood group O donors have the highest volumes of donations compared to the other blood groups. Also, blood donations by the male gender are higher than donations by their female counterparts. These trends are attributed to the higher proportions of donors in these categories.

This study will contribute to the board of knowledge on the adoption of coherent and accurate hierarchical forecasting methods in ensuring an adequate and safe blood supply chain in a low resource setting like Zimbabwe.

This study has potential limits. The lack of prior research studies on the topic limited the scope of the current study. The impact of the Covid-19 pandemic distorted the blood donation patterns such that the developed model did not capture the significant drop in blood donations during the pandemic period. Other shocks such as, a surge in global pandemics and other disasters, will inevitably affect the blood donation system. This means that future blood supplies remain under threat. Thus forecasting future blood collections with a high degree of accuracy requires robust mathematical models which factor in the impact of various shocks to the system on short notice. Door to door blood donation drives are not out of the question in such instances .

Availability of data and materials

The data that support the findings of this study are available from the corresponding author and the National Blood Service Zimbabwe upon reasonable request.

Abbreviations

National Blood Service Zimbabwe

Error-Trend-Seasonality

Autoregressive Integrated Moving Average

Mean Absolute Percentage Error

Top-Down Forecasting Proportions

Fuzzy Systems

Artificial Neural Networks

Support Vector Machine

Hierarchical Time Series

Mean Square Error

Mean Absolute Error

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Chideme, C., Chikobvu, D. & Makoni, T. Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe’s national blood bank. BMC Public Health 24 , 928 (2024). https://doi.org/10.1186/s12889-024-18185-7

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The effect of “typical case discussion and scenario simulation” on the critical thinking of midwifery students: Evidence from China

• Yuji Wang 1   na1 ,
• Yijuan Peng 1   na1 &
• Yan Huang 1  

BMC Medical Education volume  24 , Article number:  340 ( 2024 ) Cite this article

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Assessment ability lies at the core of midwives’ capacity to judge and treat clinical problems effectively. Influenced by the traditional teaching method of “teacher-led and content-based”, that teachers involve imparting a large amount of knowledge to students and students lack active thinking and active practice, the clinical assessment ability of midwifery students in China is mostly at a medium or low level. Improving clinical assessment ability of midwifery students, especially critical thinking, is highly important in practical midwifery education. Therefore, we implemented a new teaching program, “typical case discussion and scenario simulation”, in the Midwifery Health Assessment course. Guided by typical cases, students were organized to actively participate in typical case discussions and to promote active thinking and were encouraged to practice actively through scenario simulation. In this study, we aimed to evaluate the effect of this strategy on the critical thinking ability of midwifery students.

A total of 104 midwifery students in grades 16–19 at the West China School of Nursing, Sichuan University, were included as participants through convenience sampling. All the students completed the Midwifery Health Assessment course in the third year of university. Students in grades 16 and 17 were assigned to the control group, which received routine teaching in the Midwifery Health Assessment, while students in grades 18 and 19 were assigned to the experimental group, for which the “typical case discussion and scenario simulation” teaching mode was employed. The Critical Thinking Disposition Inventory-Chinese Version (CTDI-CV) and Midwifery Health Assessment Course Satisfaction Questionnaire were administered after the intervention.

After the intervention, the critical thinking ability of the experimental group was greater than that of the control group (284.81 ± 27.98 and 300.94 ± 31.67, p  = 0.008). Furthermore, the experimental group exhibited higher scores on the four dimensions of Open-Mindedness (40.56 ± 5.60 and 43.59 ± 4.90, p  = 0.005), Analyticity (42.83 ± 5.17 and 45.42 ± 5.72, p  = 0.020), Systematicity (38.79 ± 4.70 and 41.88 ± 6.11, p  = 0.006), and Critical Thinking Self-Confidence (41.35 ± 5.92 and 43.83 ± 5.89, p  = 0.039) than did the control group. The course satisfaction exhibited by the experimental group was greater than that exhibited by the control group (84.81 ± 8.49 and 90.19 ± 8.41, p  = 0.002).

The “typical case discussion and scenario simulation” class mode can improve the critical thinking ability of midwifery students and enhance their curriculum satisfaction. This approach carries a certain degree of promotional significance in medical education.

Typical case discussion and scenario simulation can improve midwifery students’ critical thinking ability.

Typical case discussion and scenario simulation can enhance students’ learning interest and guide students to learn independently.

Midwifery students were satisfied with the new teaching mode.

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Maternal and neonatal health are important indicators to measure of the level of development of a country’s economy, culture and health care. The positive impact of quality midwifery education on maternal and newborn health is acknowledged in the publication framework for action strengthening quality midwifery education issued by the World Health Organization (WHO) [ 1 ]. Extensive evidence has shown that skilled midwifery care is crucial for reducing preventable maternal and neonatal mortality [ 2 , 3 , 4 ]. Clinical practice features high requirements for the clinical thinking ability of midwives, which refers to the process by which medical personnel analyze and integrate data with professional medical knowledge in the context of diagnosis and treatment as well as discover and solve problems through logical reasoning [ 5 ]. Critical thinking is a thoughtful process that is purposeful, disciplined, and self-directed and that aims to improve decisions and subsequent actions [ 6 ]. In 1986, the American Association of Colleges of Nursing formulated the “Higher Education Standards for Nursing Specialty”, which emphasize the fact that critical thinking is the primary core competence that nursing graduates should possess [ 7 ]. Many studies have shown that critical thinking can help nurses detect, analyze and solve problems creatively in clinical work and is a key factor in their ability to make correct clinical decisions [ 8 , 9 , 10 ].

However, the traditional teaching method used for midwifery students in China is “teacher-led and content-based”, and it involves efficiently and conveniently imparting a large amount of knowledge to students over a short period. Students have long failed to engage in active thinking and active practice, and the cultivation of critical thinking has long been ignored [ 5 ]. As a result, the critical thinking ability of midwifery students in China is mostly at a medium or low level [ 5 ]. Therefore, it is necessary to develop a new teaching mode to improve the critical thinking ability of midwifery students.

In 2014, Professor Xuexin Zhang of Fudan University, Shanghai, China, proposed a novel teaching method: the divided class mode. The basic idea of this approach is to divide the class time into two parts. The teachers explain the theoretical knowledge in the first lesson, and the students discuss that knowledge in the second lesson. This approach emphasizes the guiding role of teachers and encourages and empowers students to take responsibility for their studies [ 11 ]. Research has shown that the divided class mode can improve students’ enthusiasm and initiative as well as teaching effectiveness [ 12 ].

The problem-originated clinical medical curriculum mode of teaching was first established at McMaster University in Canada in 1965. This model is based on typical clinical cases and a problem-oriented heuristic teaching model [ 13 ]. The process of teaching used in this approach is guided by typical cases with the goal of helping students combine theoretical knowledge and practical skills. This approach can enhance the enthusiasm and initiative of students by establishing an active learning atmosphere. Students are encouraged to discuss and analyze typical cases to promote their ability to digest and absorb theoretical knowledge. Research has shown that the problem-originated clinical medical curriculum teaching mode can enhance students’ confidence and improve their autonomous learning and exploration ability. Scenario simulation teaching can provide students with real scenarios, allowing them to practice and apply their knowledge in a safe environment [ 14 ], which can effectively improve their knowledge and clinical skills and enhance their self-confidence [ 15 , 16 ].

Based on the teaching concept of divided classes, our research team established a new teaching model of “typical case discussion and scenario simulation”. Half of the class time is allocated for students to discuss typical cases and carry out scenario simulations to promote their active thinking and active practice. The Midwifery Health Assessment is the final professional core course that midwifery students must take in our school before clinical practice. All students must complete the course in Grade 3. Teaching this course is important for cultivating the critical thinking and clinical assessment ability of midwifery students. Therefore, our team adopted the new teaching mode of "typical case discussion and scenario simulation" in the teaching of this course. This study explored the teaching mode’s ability to improve the critical thinking ability of midwifery students.

Study design

The study employed a semiexperimental design.

Participants

A convenience sample of 104 third-year midwifery students who were enrolled in the Midwifery Health Assessment course volunteered to participate in this research at a large public university in Sichuan Province from February 2019 to June 2022 (grades 16 to 19). All the students completed the course in the third year of university. Students in grades 16 and 17 were assigned to the control group, which received the traditional teaching mode. Students in grades 18 and 19 were assigned to the experimental group, in which context the “typical case discussion and scenario simulation” class mode was used. The exclusion criteria for midwifery students were as follows: (1) dropped out of school during the study, (2) took continuous leave from school for more than two weeks, or (3) were unable to complete the questionnaire. The elimination criterion for midwifery students was that all the items were answered in the same way. No significant differences in students’ scores in their previous professional courses (Midwifery) were observed between the two groups. Textbooks, teachers, and teaching hours were the same for both groups.

Development of the “typical case discussion and scenario simulation” class mode

This study is based on the implementation of the new century higher education teaching reform project at Sichuan University. With the support of Sichuan University, we first established a “typical case discussion and scenario simulation” class mode team. The author of this paper was the head of the teaching reform project and served as a consultant, and the first author is responsible for supervising the implementation of the project. Second, the teaching team discussed and developed a standard process for the “typical case discussion and scenario simulation” class mode. Third, the entire team received intensive training in the standard process for the “typical case discussion and scenario simulation” class mode.

Implementation of the “typical case discussion and scenario simulation” class mode

Phase i (before class).

Before class, in accordance with the requirements for evaluating different periods of pregnancy, the teacher conceptualized typical cases and then discussed those cases with the teaching team and made any necessary modifications. After the completion of the discussion, the modified cases were released to the students through the class group. To ensure students’ interest, they were guided through the task of discovering and solving relevant problems using an autonomous learning approach.

Phase II (the first week)

Typical case discussion period. The Midwifery Health Assessment course was taught by 5 teachers and covered 5 health assessment periods, namely, the pregnancy preparation, pregnancy, delivery, puerperium and neonatal periods. The health assessment course focused on each period over 2 consecutive teaching weeks, and 2 lessons were taught per week. The first week focused on the discussion of typical cases. In the first lesson, teachers introduced typical cases, taught key knowledge or difficult evaluation content pertaining to the different periods, and explored the relevant knowledge framework. In the second lesson, teachers organized group discussions, case analyses and intergroup communications for the typical cases. They were also responsible for coordinating and encouraging students to participate actively in the discussion. After the discussion, teachers and students reviewed the definitions, treatments and evaluation points associated with the typical cases. The teachers also encouraged students to internalize knowledge by engaging in a process of summary and reflection to achieve the purpose of combining theory with practice.

Phase III (the second week)

Scenario simulation practice period. The second week focused on the scenario simulation practice period. In the first lesson, teachers reviewed the focus of assessment during the different periods and answered students’ questions. In the second lesson, students performed typical case assessment simulations in subgroups. After the simulation, the teachers commented on and summarized the students’ simulation evaluation and reviewed the evaluation points of typical cases to improve the students’ evaluation ability.

The organizational structure and implementation of the “typical case discussion and scenario simulation” class mode showed in Fig.  1 .

“Typical case discussion and scenario simulation” teaching mode diagram

A demographic questionnaire designed for this purpose was used to collect relevant information from participants, including age, gender, single-child status, family location, experience with typical case discussion or scenario simulation and scores in previous professional courses (Midwifery).

The Critical Thinking Disposition Inventory-Chinese Version (CTDI-CV) was developed by Peng et al. to evaluate the critical thinking ability of midwifery students [ 17 ]. The scale contains 70 items across a total of seven dimensions, namely, open-mindedness, truth-seeking, analytical ability, systematic ability, self-confidence in critical thinking, thirst for knowledge, and cognitive maturity. Each dimension is associated with 10 items, and each item is scored on a 6-point Likert scale, with 1 indicating “extremely agree” and 6 representing “extremely disagree”. The scale includes 30 positive items, which receive scores ranging from “extremely agree” to “extremely disagree” on a scale of 6 to 1, and 40 negative items, which receive scores ranging from “extremely agree” to “extremely disagree” on a scale of 1 to 6. A total score less than 210 indicates negative critical thinking ability, scores between 211 and 279 indicate an unclear meaning, scores of 280 or higher indicate positive critical thinking ability, and scores of 350 or higher indicate strong performance. The score range of each trait is 10–60 points; a score of 30 points or fewer indicates negative trait performance, scores between 31 and 39 points indicate that the trait meaning is incorrect, scores of 40 points or higher indicate positive trait performance, and scores of 50 points or higher indicate extremely positive trait performance. The Cronbach’s α coefficient of the scale was 0.90, thus indicating good content validity and structure. The higher an individual’s score on this measure is, the better that individual’s critical thinking ability.

The evaluation of teaching results was based on a questionnaire used to assess undergraduate course satisfaction, and the researchers deleted and modified items in the questionnaire to suit the context of the “typical case discussion and scenario simulation” teaching mode. Two rounds of discussion were held within the study group to form the final version of the Midwifery Health Assessment satisfaction questionnaire. The questionnaire evaluates the effect of teaching in terms of three dimensions, namely, curriculum content, curriculum teaching and curriculum evaluation. The questionnaire contains 21 items, each of which is scored on a 5-point Likert scale, with 1 indicating “extremely disagree” and 5 representing “extremely agree”. The higher the score is, the better the teaching effect.

Data collection and statistical analysis

We input the survey data into the “Wenjuanxing” platform ( https://www.wjx.cn/ ), which specializes in questionnaire services. At the beginning of the study, an electronic questionnaire was distributed to the students in the control group via student WeChat and QQ groups for data collection. After the intervention, an electronic questionnaire was distributed to the students in the experimental group for data collection in the final class of the Midwifery Health Assessment course. All the data were collected by the first author (Yuji Wang). When students had questions about the survey items, the first author (Yuji Wang) immediately explained the items in detail. To ensure the integrity of the questionnaire, the platform required all the items to be answered before submission.

Statistical Package for Social Sciences Version 26.0 (SPSS 26.0) software was used for data analysis. The Shapiro‒Wilk test was used to test the normality of the data. The measurement data are expressed as the mean ± standard deviation (X ± S), and an independent sample t test was used for comparisons among groups with a normal distribution. The data presented as the number of cases (%), and the chi-square test was performed. A P value < 0.05 indicated that a difference was statistically significant.

Ethical considerations

The study was funded by the New Century Teaching Reform Project of Sichuan University and passed the relevant ethical review. Oral informed consent was obtained from all individual participants in the study.

Characteristics of the participants

A total of 104 third-year midwifery students were enrolled from February 2019 to June 2022, and 98.1% (102/144) of these students completed the survey. Two invalid questionnaires that featured the same answers for each item were eliminated. A total of 100 participants were ultimately included in the analysis. Among the participants, 48 students were assigned to the control group, and 52 students were assigned to the experimental group. The age of the students ranged from 19 to 22 years, and the mean age of the control group was 20.50 years (SD = 0.61). The mean age of the experimental group was 20.63 years (SD = 0.65). Of the 100 students who participated in the study, the majority (96.0%) were women. No significant differences were observed between the intervention and control groups in terms of students’ demographic information (i.e., age, gender, status as an only child, or family location), experience with scenario simulation or typical case discussion and scores in previous Midwifery courses (Table  1 ).

Examining the differences in critical thinking ability between the two groups

The aim of this study was to evaluate the effect of the new teaching mode of “typical case discussion and scenario simulation” on improving the critical thinking ability of midwifery students. Independent sample t tests were used to examine the differences in critical thinking ability between the two groups (Table  2 ). The results showed that the total critical thinking scores obtained by the experimental group were greater than those obtained by the control group (284.81 ± 27.98 and 300.94 ± 31.67, p  = 0.008). The differences in four dimensions (Open-Mindedness (40.56 ± 5.60 and 43.59 ± 4.90, p  = 0.005), Analyticity (42.83 ± 5.17 and 45.42 ± 5.72, p  = 0.020), Systematicity (38.79 ± 4.70 and 41.88 ± 6.11, p  = 0.006), and Critical Thinking Self-Confidence (41.35 ± 5.92 and 43.83 ± 5.89, p  = 0.039)) were statistically significant.

Examining the differences in curriculum satisfaction between the two groups

To evaluate the effect of the new teaching mode of “the typical case discussion and scenario simulation” on the course satisfaction of midwifery students. Independent sample t tests were used to examine the differences in course satisfaction between the two groups (Table  3 ). The results showed that the curriculum satisfaction of the experimental group was greater than that of the control group (84.81 ± 8.49 and 90.19 ± 8.41, p  = 0.002). Independent sample t tests were used to examine the differences in the three dimensions of curriculum satisfaction between the two groups (Table  3 ). The results showed that the average scores of the intervention group on the three dimensions were significantly greater than those of the control group (curricular content: 20.83 ± 1.96 and 22.17 ± 2.23, p  = 0.002; curriculum teaching: 34.16 ± 3.89 and 36.59 ± 3.66, p  = 0.002; curriculum evaluation: 29.81 ± 3.27 and 31.42 ± 3.19, p  = 0.015).

Midwifery is practical and intensive work. To ensure maternal and child safety, midwives must make decisions and take action quickly. Therefore, midwives should have both critical thinking ability and clinical decision-making ability [ 18 ]. In addition, the Australian Nursing and Midwifery Accreditation Council (ANMAC) regulates the educational requirements for the programs required for registration as a midwife. According to these standards, education providers must incorporate learning activities into curricula to encourage the development and application of critical thinking and reflective practice [ 19 ]. Therefore, the challenge of cultivating the critical thinking ability of midwifery students is an urgent problem that must be solved. However, influenced by the traditional teaching method of “teacher-led and content-based”, the critical thinking ability of midwifery students in China is mostly at a medium or low level. In order to improve the critical thinking ability of midwifery students. Our research team has established a new teaching model, the “typical case discussion and scenario simulation” class model. And applied to the midwifery core curriculum Midwifery Health Assessment. This study aimed to investigate the implementation of a novel systematic and structured teaching model for midwifery students and to provide evidence regarding how to improve the critical thinking ability of midwives.

The results showed that the total CTDI-CV score obtained for the experimental group was greater than that obtained for the control group. These findings indicate that the “typical case discussion and scenario simulation” class mode had a positive effect on the cultivation of students’ critical thinking ability, a conclusion which is similar to the findings of Holdsworth et al. [ 20 ], Lapkin et al. [ 21 ] and Demirören M et al. [ 22 ]. We indicate the following reasons that may explain these results.The core aim of the typical case discussion teaching mode is to raise questions based on typical clinical cases and to provide heuristic teaching to students [ 23 ]. This approach emphasizes asking questions based on specific clinical cases, which enables students to engage in targeted learning. Moreover, scenario simulation allows students to attain certain inner experiences and emotions and actively participate in curriculum practice, which can enhance their ability to remember and understand knowledge [ 24 ]. Through the divided class mode, half of the class time was divided into the students. This method emphasizes the guiding role of teachers and encourages and empowers students to assume learning responsibilities. In addition, students can think, communicate and discuss actively [ 22 , 23 ]. Furthermore, this approach created opportunities for students to analyze and consider problems independently and give students sufficient time to internalize and absorb knowledge and deepen their understanding of relevant knowledge, which can increase their confidence in their ability to address such problems and improve their critical thinking ability [ 12 , 25 , 26 ].

In addition, the results showed that except for Truth-Seeking and Systematicity, the other five dimensions were all positive. These findings are similar to the results reported by Atakro et al.. and Sun et al. [ 27 , 28 ]. Through the intervention, the Systematicity scores became positive, suggesting that the new teaching mode can help students deal with problems in an organized and purposeful way. However, Truth-Seeking still did not become positive; this notion focuses on intellectual honesty, i.e., the disposition to be courageous when asking questions and to be honest and objective in the pursuit of knowledge even when the topics under investigation do not support one’s self-interest [ 29 ]. Studies have shown that this factor is related to the traditional teaching mode used [ 30 ]. The traditional teaching mode focuses on knowledge infusion, helps students remember the greatest possible amount of knowledge in a short time, and does not focus on guiding students to seek knowledge with sincerity and objectivity. Therefore, in future educational practice, we should focus on cultivating students’ ability to seek truth and engage in systematization.

Student evaluative feedback is an important way to test the effectiveness teaching mode. Therefore, understanding students’ evaluations of the effects of classroom teaching is key to promoting teaching reform and improving teaching quality. Therefore, we distributed a satisfaction questionnaire pertaining to the midwifery health assessment curriculum, which was based on the “typical case discussion and scenario simulation” class mode, with the goal of investigating curriculum satisfaction in terms of three dimensions (curriculum content, curriculum teaching and curriculum evaluation). The results showed that the satisfaction scores for each dimension increased significantly. This finding suggests that the new teaching method can enrich the teaching content, diversify the teaching mode and improve students’ curriculum evaluations.

In summary, the “typical case discussion and scenario simulation” class mode focuses on typical cases as its main content. Students’ understanding of this content is deepened through group discussion and scenario simulation. The subjectivity of students in curriculum learning should be accounted for. Students can be encouraged to detect, analyze and solve problems with the goal of improving their critical thinking ability. Moreover, this approach can also enhance curriculum satisfaction. It is recommended that these tools should be used continuously in future curriculum teaching.

This study has several limitations. First, the representativeness of the sample may be limited since the participants were recruited from specific universities in China. Second, we used historical controls, which are less effective than simultaneous controlled trials. Third, online self-report surveys are susceptible to response biases, although we included quality control measurements in the process of data collection. Fourth, we did not use the same critical thinking instrument, CTDI-CV, to investigate the critical thinking of the students in the experimental group or the control group before intervention but used professional course grades from the Midwifery for substitution comparison. This may not be a sufficient substitute. However, these comparisons could be helpful since those grades included some sort of evaluation of critical thinking. In light of these limitations, future multicenter simultaneous controlled studies should be conducted. Nonetheless, this study also has several strengths. First, no adjustment of teachers or change in learning materials occurred since the start of the midwifery health assessment, thus ensuring that the experimental and control groups featured the same teaching materials, teachers and teaching hours. In addition, to ensure the quality of the research, the first author of this paper participated in the entirety of the course teaching.

The “typical case discussion and scenario simulation” class mode can improve the critical thinking of midwifery students, which is helpful for ensuring maternal and child safety. Students are highly satisfied with the new teaching mode, and this approach has a certain degree of promotional significance. However, this approach also entails higher requirements for both teachers and students.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

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The study was supported by Sichuan University’s New Century Education and Teaching Reform Project (SCU9316).

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Yuji Wang and Yijuan Peng are co-first authors.

Authors and Affiliations

Department of Nursing, West China Second University Hospital, Sichuan University/West China School of Nursing, Sichuan University/Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), No. 20 Third Section, Renmin South Road, Chengdu, Sichuan Province, 610041, China

Yuji Wang, Yijuan Peng & Yan Huang

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yuji Wang, Yijuan Peng and Yan Huang. The first draft of the manuscript were written by Yuji Wang and Yijuan Peng, and all authors commented on previous versions of the manuscript.

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Correspondence to Yan Huang .

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Ethics approval and consent to participate.

This study was supported by Sichuan University. And it was approved by the Ethics Review Committee of West China School of Nursing, Sichuan University. As it is a teaching research with no harm to samples, we only obtained oral informed consents from the participants including teachers and midwifery students and it was approved by the Ethics Review Committee of West China School of Nursing, Sichuan University(approval number 2021220). We comfirm that all methods were performed in accordance with the relevant guidelines and regulations in Ethics Approval and Consent to participate in Declarations.

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Wang, Y., Peng, Y. & Huang, Y. The effect of “typical case discussion and scenario simulation” on the critical thinking of midwifery students: Evidence from China. BMC Med Educ 24 , 340 (2024). https://doi.org/10.1186/s12909-024-05127-5

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Received : 19 November 2022

Accepted : 02 February 2024

Published : 26 March 2024

DOI : https://doi.org/10.1186/s12909-024-05127-5

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