Demand-Driven Inventory: Optimal Inventory Levels

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While too much inventory increases costs, its shortage can also lead to the significant loss of sales opportunities. Nestle is a food and beverage company that sells beverages, cereals, baked goods, chocolate, and confectionery. This paper analyzes the company’s demand patterns and their impact on its optimal inventory levels. It also provides a comprehensive elucidation of how demand forecast can reduce inventory cost and the effects of optimal inventory level on operational performance. Having an appropriate inventory level is the cornerstone of a firm’s operational performance.

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Demand Patterns in the Food and Beverage Industry

Constant fluctuating demands characterize the food and beverage industry in Bangladesh. Product quality, characteristics, and continuous quantity significantly influence consumers’ purchasing decisions. In Bangladesh, Nestlé’s product demand is independent as customers purchase finished products. Usually, the products are purchased for household consumption, and consumers will buy whatever amount they believe will meet their household’s needs. Therefore, consumers can buy multiple units within a given timeframe. Consumers also seek variety; therefore, purchasing multiple products at a time is typical (Roni et al., 2016). The product’s packaging, container size, and flavor also influence demands.

While demands for multiple products are typical, zero purchases are also routine. The industry is flocked with competitors, and consumers will only choose a few of them. Therefore, customer loyalty and seasonal changes mediated by promotional and marketing activities will influence its demand ((Roni et al., 2016). The company’s product demand is also affected by weather patterns, regional and demographic preferences, and seasonal fluctuations (“Annual review,” 2019). Additionally, Nestle has a wide product range; customers will only choose a few items, while others will receive zero purchases.

Impact of Demand Patterns on Optimal Inventory Levels

From the above analysis, it is clear that the industry’s demand patterns are characterized by variability. Water shortages, changes in weather patterns, socioeconomic inequality, and shifts in production are typical problems in Nestle’s supply chain (“Annual review,” 2019). The company reports that these factors negatively impact its supply chain and yield quality (Nestle, 2019). These uncertainties can damage the company’s reputation, increase input pricing, growth margins, and targets. According to Davis (2013), a company can achieve optimal inventory levels if the supply chain was retailer-centric. Vendors need to be in control of the supply chain to gain better inventory control.

Contrarily, Nestlé’s supply chain is heavily influenced by customer demands; it has to react to customers’ needs. Typically, the food and beverage industry builds inventories; they maintain a safety stock as insurance against out-of-stock problems. Nestle needs to hold more inventories to meet retailers’ demand on time.

However, keeping inventory can cause the bullwhip effect and increases inventory holding costs. The bullwhip effect is a phenomenon that demonstrates how inventory fluctuations, in response to customer demands, can result in supply chain inefficiencies. Another problem with having a “safety stock” or too much inventory is that when forecasts turn out to be wrong, the company will need to discount or liquidate the product to reduce waste.

On the other hand, if Nestle decides not to use the safety stock strategy, it might have insufficient inventory. Low inventory levels increase the risk of inadequate supply or stockouts, leading to loss of revenues, time, and resources. When the demand for a given product increases, the inventory reduces, consequently causing stockouts or product shortages. On the one hand, Nestle needs to keep the safety stock is crucial in meeting its customers’ demands. On the other hand, these safety stocks only result in inefficient inventory management. According to Trent (2008), stock shortages can waste time and sales revenues. The cost of losing sales opportunities can damage a company’s reputation and possibly permanently turn away potential customers. Additionally, stockouts are a deterrent to most customers considering that convenience is valued in today’s fast-paced world. Loss of sales opportunities occurs when a product’s demand exceeds the company’s maximum available capacity.

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Impact of Demand Forecasts on Inventory Costs

Currently, the model used by Nestlé’s supply chain is the built-to-stock (BTS) model. The company manufactures products before an actual demand is made. This model is different from the built-to-order model, where product manufacturing is only done when a customer’s order has been received. According to Davis (2013), the BTS model can cost-effectively manufacture and deliver inventory if there is a predictable demand pattern. However, it is difficult for business managers to understand the actual demands because production and delivery cannot be accomplished on the same day (Trent, 2008). Therefore, businesses need to estimate or forecast customer demands.

Inaccurate forecasts can lead to poor service quality when customers do not get their products or high inventory levels, underscoring the need to reduce forecasting variability (Myerson, 2012). According to Davis (2013), automating demand forecasting enables a right-sizing of inventory, a coherent replenishment flow, faster inventory turnover, and few out-of-stock situations. Nestlé uses SAS, a predictive statistical and artificial intelligence software, to forecast customer demands (Ackerman et al., 2012). The company reports that the software has significantly improved its forecasting accuracy and reduced the “safety inventory” levels (Ackerman et al., 2012). As previously mentioned, Davis (2013) argues that the BTS model can cost-effectively manufacture and deliver inventory with a predictable demand pattern. Accordingly, it can be logically argued that Nestle has achieved inventory cost-effectiveness by using forecasting technology. Nestle’s forecasting technology enables easy access to customer demand information, leading to accurate predictions on demand patterns.

Through data-driven insights, the technology has allowed the company to gain the visibility of its customers. Davis (2013) stresses that companies whose supply chains react to customer demands need to have visibility for optimal inventory levels. A study conducted by Somapa et al. (2018) also showed that even a partial improvement in demand visibility could significantly improve a firm’s inventory control efficiency. These efficiencies can be translated into low inventory costs. The assumption is that the technology will improve the balance between low inventory and high return on investment, reducing inventory depreciation and wastages (Shin et al., 2015). When a company understands the demand velocity, they get better insights into the inventory needed to meet their customers’ demands. Therefore, they can effectively schedule production, warehousing, and transportation, increasing its operational efficiency, reducing operating costs.

Optimal Inventory Levels and Operational Performance

Optimal inventory levels aim to reduce waste, minimize costs, and increase operational performance. Nestlé can use lean management principles to reduce wastes and control variations in its inventory management system. Michael (2004) underscores lean principles’ significance in minimizing costs and enhancing a firm’s competitive advantage. The model emphasizes establishing a firm’s customer-centered values and identifying the strategies, activities, and procedures (value stream) required to deliver that value (Goldsby & Martichenko, 2005). The model asserts that eliminating wastes in the value stream can increase operational efficiency (Michael, 2004). Just-in-Time inventory system is an example of an inventory system that is based on the lean principle.

An optimal inventory improves operational performance by reducing wastes and costs associated with inefficient inventory management. For example, the Just-In-Time (JIT) inventory system aims to improve a company’s financial performance by reducing the costs associated with excess inventory. A study conducted by Shin et al. (2015) shows that an effective inventory management system can lead to optimal resource use, waste reduction, cost reduction, and improved profitability. The assumption here is that effective inventory management will improve a firm’s financial performance by eliminating inefficient activities and operations that generate high costs.

Conclusion

Nestle demands patterns are characterized by variability; product demand is affected by weather patterns, regional and demographic preferences, and seasonal fluctuations. Product quality, characteristics, customer loyalty, and seasonal changes mediated by promotional and marketing activities also influence demand and product availability. It uses the BTS model, and its demand pattern predictions are automated. A high inventory level increases a firm’s financial performance by eliminating inefficient business operations that generate high costs.

References

Ackerman, A., Padilla, A., & Cov, R. (2012). Nestle drives better demand. Consumer Goods Technology. Web.

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Annual review. (2019). Nestle. Web.

Davis, R. A. (2013). Demand-driven inventory optimization and replenishment: Creating a more efficient supply chain. John Wiley & Sons, Inc.

Goldsby, T. J., & Martichenko, R. (2005). Lean six sigma logistics: Strategic development to operational success. J. Ross Publishing.

Myerson, P. (2012). Lean supply chain & logistics management. McGraw Hill.

Roni, M. S., Eksioglu, S. D., Jin, M., & Mamun, S. (2016). A hybrid inventory policywith split delivery under regular and surge demand. International Journal of Production Economics, 172, 126-136. Web.

Shin, S., Ennis, K. L., & Spurlin, W. P. (2015). Effect of inventory management efficiency on profitability: Current evidence from the US manufacturing industry. Journal of Economics and Economic Education Research, 16(1), 98–106. Web.

Somapa, S., Cools, M., & Dullaert, W. (2018). Characterizing supply chain visibility–a literature review. The International Journal of Logistics Management, 29(1), 308–339. Web.

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Trent, R. J. (2008). End-to-end lean management: A guide to complete supply chain improvement. J. Ross Publishing.

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