Fransoo and Wouters bullwhip effect with further theories Essay Example

emphasized the coordination of systemic and strategic functions in traditional business and the planning across these business functions within a specific company and across businesses to enhance the ongoing operations of individual companies and the supply chain as a whole. Lummus et al. (2001) also took into account the logistics stream, customer order processing, industry processes, and essential information flows to understand all activities at the company's trading stage.Lee and Billington (1992) examined the relationship between fabrication and distribution sites in terms of procuring raw materials, transforming them into intermediate or finished goods, and distributing them to customers. This approach helps to reduce risk by minimizing the time between production and exchange. Additionally, the authors recommended a critical review of companies' business activities to improve performance and potentially minimize the bullwhip effect at the company level. Stevens (1990) focused on managing the flow of materials from suppliers through value-added processes and distribution channels to end users. Ellram and Cooper (1990) discussed managing the entire flow of a sharing channel from supplier to final buyer. Houlihan (1988) examined the trade goods stream from traders through manufacturers and distributors to end users. Jones and Riley (1985) addressed the overall flow of materials from suppliers to end users. Oliver and Webber (1982) argued that marketing channels should be viewed as an integrated entity. The imbalance between inventory points in a supply chain can be caused by value-added processes in different business activities.(2009) explored the impact of demand variability on the occurrence of the bullwhip effect. They found that the bullwhip effect was not solely attributed to demand variability, but also resulted from the effects of the value chain

concept. According to Porter (1985), the value chain concept is a tool for understanding how a firm creates customer value by breaking down its activities. Overall, the value chain concept identifies key aspects that contribute to a firm's competitive strengths in the market. Weld (1916) further supported this concept, stating that value is added at each step of the process. The value-added approach helps to comprehend the bullwhip effect between inbound and outbound logistics flows within a company. The International distribution of physical and logistics journal also defines the bullwhip effect as a result of trust, dependencies, and resource interactions that can have negative consequences when variability occurs upstream or downstream. Sterman (1989) demonstrated that misinformation or misperception can lead to significant reactions from individuals. Managing variability in a managerial context, therefore, becomes challenging in a business environment. Lee et al. (2009) studied this issue further.(1997a) indicated that the variability may be symptoms of excessive inventory, poor product anticipation, inadequate or extreme capacities, unsatisfactory customer service due to out-of-stock products or long backlogs, uncertain production planning, and high costs for corrections. Lee et al. (1997b) identified four main causes of the bullwhip effect, namely demand forecast updating, order batching, price fluctuations, and rationing and scarcity gaming. Xu et al. (2001) presented that when the manufacturer's prediction errors were higher than those of the retailer before coordination or collaboration, coordination becomes effective in reducing the manufacturer's safety stocks. Lee and Billington, Towill, Fransoo and Wouters (2000) concluded that the bullwhip effect could be reduced by decreasing lead times, revising reordering procedures, controlling price fluctuations, and incorporating planning and performance measurement.Baljko (1999) suggested ways to eliminate the

bullwhip effect, including improved communication with providers and clients, cooperation with supply chain partners, and the use of internet-enabled technology for faster communication. Lee et al. (1997a) discussed the causes of the bullwhip effect and presented coordination mechanisms such as information sharing and channel alignment to reduce it. They also highlighted the importance of operational efficiency in achieving improved performance. Chen et al. (2000) quantified the bullwhip effect in two phases involving a retailer and a manufacturer, emphasizing the importance of centralized demand information in reducing it.In their study, Kelle and Milne (1999) explored the bullwhip effect by analyzing three key factors: the type of entity marketer, the corporate orders of the seller, and the trader's ordering/producing policy. Their research demonstrated that order-taking can help reduce demand variability and mitigate the negative consequences of high variability and unpredictability. Similarly, Xu et al. (2001) focused on enhancing supply chain coordination through improved information exchange and consistent forecasting. Their findings revealed the detrimental impact of independent activities within a traditional supply chain on order release volatility and forecast error volatility. The authors emphasized the importance of effectively managing fluctuations in orders and fostering collaboration among supply chain actors. According to the Journal of International Distribution of Physical and Logistics, the extent of the bullwhip effect depends on the gap between guesswork and rescheduling of business activities. It is suggested that eliminating or minimizing this gap is preferable in a managerial context to mitigate the bullwhip effect effectively.Swenson (2002) identified three types of dependencies between buyers and sellers in the market: time dependence, functional dependence, and relationship dependence. Forrester (1961) described the "bullwhip" effect as a variability in

required demand further upstream. Supplying upstream providers with electronic point of sale (EPOS) information can significantly reduce this bullwhip effect and eliminate information distortions. Forrester's research on demand elaboration in a supply chain is extensively discussed in the seminal book Industrial Dynamics. Fransoo and Wouters (1986) proposed various improvements to reduce the bullwhip effect, including reducing lead times, revising reorder procedures, implementing price fluctuation restrictions, and integrating planning and performance measurement.J.F. Wouters had multiple jobs due to information system restrictions. According to Lee et Al. (1997a, 1997b), there were four main factors causing the bullwhip effect. Firstly, updates on demand forecasts and anticipation led to supply chain links being made based on future demand. Secondly, order batching resulted in depleted stock levels when demands were received. Thirdly, price fluctuations occurred due to promotions and trade deals, increasing demand variability. Downwind et Al. emphasized that lower product prices caused customers to buy larger quantities than needed, leading to lower purchases when prices returned to normal, depleting their inventory. Therefore, stabilizing prices and reducing promotions could help reduce this effect. Lastly, rationing and scarcity betting occurred when product demand exceeded supply, leading traders to ration their products to customers. This encouraged customers to order more than necessary, causing orders to decrease when scarcity ceased. By implementing rationing methods based on past sales instead of orders placed, the incentive for customers to inflate their order sizes is eliminated.According to Lee et al., (1997), the bullwhip effect is a term used by Procter and Gamble's logistics executives to describe the amplification of orders for upstream participants, such as wholesalers and manufacturers, due to small order variability at the customer

level. This phenomenon occurs even when consumer sales remain relatively constant. The study found that the order placed by a retailer to a wholesaler is likely to fluctuate more than the actual demand perceived by the retailer. Similarly, the order from the wholesaler to the manufacturer and the manufacturer's order to the supplier also experience even greater variability. This increase in order variability at each stage in the supply chain is often referred to as the bullwhip effect. Consequently, this effect results in high variability in different order points throughout the supply chain system. Additionally, Forrester (1961) explained that the order variability towards the manufacturer is typically much higher than the variability in actual consumer demand.Sterman (1989) discovered that the bullwhip effect was caused by irrational decision making by the participants. After analyzing the results of the beer distribution game, Sterman concluded that the participants underestimated inventory holds and did not consider the entire supply chain when placing orders. This poor decision making was attributed to difficulties in evaluating complex feedback loops and time delays.

In addition, Lee et al. (1997) studied four potential causes of the bullwhip effect: updating demand forecasts and orders, fluctuating costs, splitting and shortage of materials, and price fluctuation. The authors found that updating demand forecasts led to demand amplification, as orders were forecasted and transmitted, resulting in the creation of safety stocks. This, in turn, led to the occurrence of the bullwhip effect.

Lee et al. (1997) also discussed how material procurement and planning, as well as required transportation, led companies to place orders at specific times. This intermittent batching caused a surge in demand followed by periods with little to

no orders, as well as periods with high demand. The authors also noted that price fluctuations contributed to increased demand variability and raggedness.Finally, in situations where demand outpaces supply, manufacturers often ration products to their customers based on order size. Towill (1999) conducted a study on the bullwhip effect using a computer simulation model. This research was based on Forrester's simulation model, which consisted of a retailer, distributor, factory warehouse, and factory. The study revealed that delays in information and material flow could be a significant contributing factor to the bullwhip effect. The author also found that reducing the production lead time can decrease the bullwhip effect. Taylor (2000) also discussed how supply variability can potentially cause the bullwhip effect. Problems with machine reliability and quality issues can lead to fluctuations in outputs from unreliable machines, which then trigger demand variability at upstream stages. This variability at the production level becomes the initial driver of demand variability, resulting in the bullwhip effect. In addition to these potential causes, the author also explored downstream members' stock policies aimed at minimizing their inventories. It was argued that simply passing inventory holding responsibility to upstream members can contribute to the bullwhip effect.According to the International Journal of Retail & Distribution Management, a diagram (Figure 2.4) displayed nine potential causes of the bullwhip effect that were examined in the research conducted by Sterman, Disney, and Towill (2003). These variables were analyzed and their relationships were investigated. In addition, Forrester (1961) discovered that when an order, consisting of inventory quantities for future demands and its associated safety stock, was anticipated and transmitted throughout the supply chain, order quantities increased along

with the accumulation of safety stock. Consequently, the amount of orders placed at a factory generally exceeded actual consumer demand. The author also determined that machine breakdowns were identified as one of the potential factors contributing to the bullwhip effect. Therefore, if a machine experienced a breakdown or malfunction, it could result in production delays, ultimately leading to the bullwhip effect. The same author further examined how price reductions such as sales promotions affected customers. The study revealed that price discounts resulted in a decrease in the time gap between when a consumer became inclined towards a promotion and when a purchase was actually made.Due to this reason, the purchase hold was associated with the rate of consumption. The writer concluded that both transit hold and mail hold could lead to order hold and increased transit lead-time and information hold, such as order preparation and processing time, contributing to demand amplification. The bullwhip effect causes the greatest inefficiency in the higher levels of a supply chain. However, all the companies involved in the relevant supply chain contribute to the effect and need to collaborate in order to reduce it. Holmstrom (1997) conducted a case study on supply chain operations in the European food market industry. The writer identified distributors and retailers as the main causes of the bullwhip effect due to variability. The increase in variability was partially due to a slow and inaccurate flow of demand information in the supply chain. Lee, Bagchi, Skjoett-Larsen, Disney, and Towill (2003) found that the use of the latest information technology not only reduces material and information holds among supply chain members but also enables accurate and transparent

sharing of actual customer demands throughout the supply chain. Lack of coordination or collaboration among each stage of the supply chain may result in actions that increase variability and decrease overall supply chain profits.The writers additionally discovered that by reducing or eliminating intermediaries, supply chain partners can prevent unclear demand information and better understand the purchasing patterns of real customers. Stein (1998) found that there has been a significant increase in the quality and quantity of information shared across supply chains in modern times. This increase has been driven in part by advancements in technology for gathering and distributing data. The introduction of enterprise logistics software, such as SAP, has allowed companies to maintain and share stock information for various delivery points on a centralized record. Forrester (1958) discovered that one of the main concerns for supply chains was the increased variability in demand, which led to higher costs in the form of increased inventory requirements, expedited orders, or customer shortages. Lee et al. (1997) studied the factors that can contribute to this variability, including demand signal processing, stock allocation, order batching, and price fluctuations. Chen et al. (1998) demonstrated that strategies for improving operational issues include enhancing order forecasting techniques and capacity allocation strategies. Cachon (1999) found that reducing order batching over a period of time and implementing daily low pricing can help address these issues. Kaminsky, Simchi-Levi, and Steckel et al. (unspecified year) also conducted research on this topic.In 2004, it was stated that the control for inaccuracies in demand signal processing could be achieved by distributing information about the retail demand allotment to all participants. This approach was associated with the stationary

beer game. Chen et al. (1998) discussed the main causes of the bullwhip effect. In their paper, they proposed reducing the bullwhip effect through information sharing schemes (centralized information) and changing order batching (altering the frequency of reordering using two inventory control policies). Seung-kuk Paik and Prabir K. Bagchi (2006) identified various possible causes of the bullwhip effect, including price fluctuation, supply shortages, delays in updating demand forecasts, interruptions in information flow, production issues, material procurement, and transportation delays.