Sales forecasting in times of crises at DSM

A system dynamics model has been developed in order predict demand development throughout the supply chain in times of crises. Good insights by using this type of modeling enable managers to make the right decisions and to gain competitive advantage out of the crisis. Using a system dynamics model described in this best practice, DSM was able to predict its sales with astonishing accuracy, and came stronger out of the crisis

Responding to the Lehman wave : sales forecasting and supply management during the credit crisis

In this paper we analyze the strong dip in the manufacturing industry seen at the end of 2008 and provide evidence from various sources that it was caused by cumulative de-stocking, triggered by the bankruptcy of Lehman Brothers. This de-stocking created a giant dampened wave, the so-called Lehman wave. We model the Lehman Wave using system dynamics and validate the model using data from a number of business units and market segments of Royal DSM. We show that the model gives a very good prediction of sales development during the credit crisis. We provide insights into how these results can be used to improve sales forecasting and supply chain management during times of severe crises. We also show that the effects of the current financial crisis are far from over and suggest that our methods be used to predict sales during the year 2010

Taming the Business Cycles in Commercial Aviation: Trade-space analysis of strategic alternatives using simulation modeling

We investigate the effectiveness of strategic alternatives that are designed to dampen the cyclicality manifest in the commercial aviation related industries. The constituent enterprises of the commercial aviation system exhibit managerial and operational independence and have diverse value functions that often viewed the enterprises to view their competition as a zero-sum game. We argue that this need not always be the case; in the commercial aviation system both airline and airframe manufacturers constituents would benefit from a steadier influx of aircraft that counters the current situation that is characterized by relatively stable demand growth rate for air travel while airline profitability and aircraft ordering fluctuate intensely. In order to identify and evaluate the symbiotic potential, we use a system dynamics model of commercial aviation. After testing several individual strategic alternatives, we find that capacity management is key to cycle moderation for non-collusive strategies. Comparing faster aircraft deliveries to semi-fixed production schedules among other alternatives shows only the latter alternative to be Pareto efficient

The bullwhip effect: Progress, trends and directions

This is the final version. Available on open access from Elsevier via the DOI in this recordThe bullwhip effect refers to the phenomenon where order variability increases as the orders move upstream in the supply chain. This paper provides a review of the bullwhip literature which adopts empirical, experimental and analytical methodologies. Early econometric evidence of bullwhip is highlighted. Findings from empirical and experimental research are compared with analytical and simulation results. Assumptions and approximations for modelling the bullwhip effect in terms of demand, forecast, delay, replenishment policy, and coordination strategy are considered. We identify recent research trends and future research directions concerned with supply chain structure, product type, price, competition and sustainability

Mitigating the Bullwhip Effect and Enhancing Supply Chain Performance through Demand Information Sharing: An ARENA Simulation Study

The supply chain is a network of organizations that collaborate and leverage their resources to deliver products or services to end-customers. In today's globalized and competitive market, organizations must specialize and form partnerships to gain a competitive edge. To thrive in their respective industries, organizations need to prioritize supply chain coordination, as it is integral to their business processes.   Supply chain management focuses on the collaboration of organizations within the supply chain. However, when each echelon member optimizes their goals without considering the network's impact, it leads to suboptimal performance and inefficiencies. This phenomenon is known as the Bullwhip effect, where order variability increases as it moves upstream in the supply chain. The lack of coordination, unincorporated material and information flows, and absence of ordering rules contribute to poor supply chain dynamics. To improve supply chain performance, it is crucial to align organizational activities. Previous research has proposed solutions to mitigate the Bullwhip effect, which has been a topic of intense study for many decades. This research aims to investigate the causes and mitigations of the Bullwhip effect based on existing research. Additionally, the paper utilizes ARENA simulation to examine the impact of sharing end-customer demand information. As far as we are aware, no study has been conducted to deeply simulate the bullwhip effect using the ARENA simulation. Previous studies have investigated this phenomenon, but without delving into its intricacies. The simulation results offer potential strategies to mitigate the Bullwhip effect through demand information sharing. Keywords: Supply Chain Management, Bullwhip effect, Inventory management, ARENA simulation, Information sharing, forecasting technique, Demand variability. DOI: 10.7176/JESD/14-14-07 Publication date:August 31st 202

Data Science in Supply Chain Management: Data-Related Influences on Demand Planning

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and efficient decisions. Yet, the accuracy of these metrics is dependent on the proper specification and parameterization of models and measurements. Thus, demand planners need to step away from a black box approach to supply chain data science. Utilizing paired weekly point-of-sale (POS) and order data collected at retail distribution centers, this dissertation attempts to resolve three conflicts in supply chain data science. First, a hierarchical linear model is used to empirically investigate the conflicting observation of the magnitude and prevalence of demand distortion in supply chains. Results corroborate with the theoretical literature and find that data aggregation obscure the true underlying magnitude of demand distortion while seasonality dampens it. Second, a quasi-experiment in forecasting is performed to analyze the effect of temporal aggregation on forecast accuracy using two different sources of demand signals. Results suggest that while temporal aggregation can be used to mitigate demand distortion\u27s harmful effect on forecast accuracy in lieu of shared downstream demand signal, its overall effect is governed by the autocorrelation factor of the forecast input. Lastly, a demand forecast competition is used to investigate the complex interaction among demand distortion, signal and characteristics on seasonal forecasting model selection as well as accuracy. The third essay finds that demand distortion and demand characteristics are important drivers for both signal and model selection. In particular, contrary to conventional wisdom, the multiplicative seasonal model is often outperformed by the additive model. Altogether, this dissertation advances both theory and practice in data science in supply chain management by peeking into the black box to identify several levers that managers may control to improve demand planning. Having greater awareness over model and parameter specifications offers greater control over their influence on statistical outcomes and data-driven decision

Measuring and avoiding the bullwhip effect: A control theoretic approach

An important contributory factor to the bullwhip effect (i.e. the variance amplification of order quantities observed in supply chains) is the replenishment rule used by supply chain members. First the bullwhip effect induced by the use of different forecasting methods in order-up-to replenishment policies is analysed. Variance amplification is quantified and we prove that the bullwhip effect is guaranteed in the order-up-to model irrespective of the forecasting method used. Thus, when production is inflexible and significant costs are incurred by frequently switching production quantities up and down, order-up-to policies may no longer be desirable or even achievable. In the second part of the paper a general decision rule is introduced that avoids variance amplification and succeeds in generating smooth ordering patterns, even when demand has to be forecasted. The methodology is based on control systems engineering and allows important insights to be gained about the dynamic behaviour of replenishment rules

A delayed demand supply chain: Incentives for upstream players

We study a decentralized supply chain where only delayed market demand information is available for making replenishment decisions. The impact of this delay is quantified in a serially linked two-level supply chain where each player exploits the order-up-to replenishment policy. The market demand is assumed to be a first-order autoregressive process. It is shown that the first level of the supply chain benefits from shorter time delays; however, the benefit for the second level is quite minor at best and can sometimes even be (counter-intuitively) detrimental. We conclude that the second level does not have a strong incentive to reduce the time delays in the shared market demand information

TEDARİK ZİNCİRİNDE BİLGİ ÇARPITMASININ ETKİSİ: KIRBAÇ ETKİSİ

Tedarik zinciri üyeleri birbiriyle sürekli etkileşim halindedir. Tedarik zincirinde bilginin paylaşımında ortaya çıkan aksaklıklar zincirde aksamalara ve verimsizliklere neden olur; aşırıstok yatırımlarıortaya çıkar, abartılısiparişseviyeleri ve maliyetlerin yükselmesine neden olan talep dalgalanmalarıoluşur. Bu çalışmada, tedarik zincirinde bilgi çarpıtmasının ortaya çıkardığıetkiler, kırbaç etkisi olarak adlandırılan bu durumun işletmelerde oluşturduğu olumsuzluklar ve çözüm yöntemleri ele alınmıştır