The impact of exponential smoothing forecasts on the bullwhip effect

Chen, Fanglin; Ryan, Jennifer K.; Simchi‐Levi, David

doi:10.1002/(sici)1520-6750(200006)47:4<269::aid-nav1>3.3.co;2-h

Cited by 49 publications

(124 citation statements)

References 8 publications

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“…Lee et al [3] analyzed the four sources of the bullwhip effect by constructing a first order autoregressive (AR(1)) demand model. Chen et al [4,5] used the AR(1) demand model to quantify the bullwhip effect in a simple supply chain, and to investigate the impact of the forecasting, lead time and information. Forecast methods like the minimum mean-squared error (MMSE), moving average (MA) and exponential smoothing (ES) were employed to quantify the bullwhip effect.…”

Section: Introductionsupporting

confidence: 87%

“…We take the Order Variance Ratio (OVR) applied by Chen et al [4,5] to measure the bullwhip effect of the supply chain, and the Inventory Variance Ratio (IVR) proposed by Disney and Towill [32] to measure the amplification of demands on the inventory. In this section, the OVR and IVR of the whole supply chain will be investigated under different states:…”

Section: Experimental Design Numerical Results and Discussionmentioning

confidence: 99%

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Analysis of the Complexity Entropy and Chaos Control of the Bullwhip Effect Considering Price of Evolutionary Game between Two Retailers

Lou

2016

Entropy

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Abstract:In this research, a model is established to represent a supply chain, which consists of one manufacturer and two retailers. The price-sensitive demand model is considered and the price game system is built according to the rule of bounded rationality as well as the entropy theory. With the increase of the price adjustment speed, the game system may go into chaos from the stable and periodic state. The bullwhip effect and inventory variance ratio of different stages that the system falls in are compared in real time. We also employ the delayed feedback control method to control the system and succeed in mitigating the bullwhip effect of the system. On the whole, the bullwhip effect and inventory variance ratio in the stable state are smaller than those in period-doubling and chaos. In the stable state, there is an optimal price adjustment speed to obtain both the lowest bullwhip effect and inventory variance ratio.

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Section: Introductionsupporting

confidence: 87%

Section: Experimental Design Numerical Results and Discussionmentioning

confidence: 99%

Analysis of the Complexity Entropy and Chaos Control of the Bullwhip Effect Considering Price of Evolutionary Game between Two Retailers

Lou

2016

Entropy

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“…Quality of forecasting is seen as an important factor in its control, as identified by Lambrecht and Dejonckheere (1999) and Chen et al (2000). Subsequently in a paper by Dejonckheere et al (2002) an analytical assessment of the impact of forecasting was established via transfer function analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Examples include Sport Obermeyer, (Fisher and Raman, 1996); Dell, (Kapuchinski et al 2004) and Phillips Electronics, (de Kok et al 2005). Many guidelines are available to decision makers in such supply chains, including Bertrand (1986), Wikner et al (1991), Berry et al (1995), Bonney et al (1994), Chen et al (2000), Edwards et al (2001), Cachon and Lariviere, (2005), Chatfield et al (2004) and Hoberg et al (2007).…”

mentioning

confidence: 99%

A pragmatic approach to the design of bullwhip controllers

Zhou¹,

Disney

Towill

2010

International Journal of Production Economics

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It is well known that forecasting mechanisms can greatly increase "bullwhip" -demand variance amplification of orders as processed by both human and algorithmic decision makers. This paper is concerned with the application of the well established APIOBPCS Decision Support System (a variant of the Order-Up-To Rule) in such circumstances. It has two feedback controls (based on the inventory and the ordersin-pipeline respectively) with gains set equal according to the Deziel-Eilon Rule.There is one feed-forward control based on exponential forecasting, although this is not a restriction on the application of this system.We consider the pragmatic role of APIOBPCS in the situation where the echelon decision maker may be handling a wide range of SKU's in a non-altruistic environment where upmarket information may either be withheld or simply unavailable. Under such circumstances it has been established via site-based studies that the decision makers output (the orders) reflect a wide range of strategies (or maybe ignorance). Three strategies may be regarded as "appropriate", i.e. Passorders-Along; Demand Smoothing; and Level Scheduling depending, on context. APIOBPCS can be adapted to each of these modus operandi. In the first case with the added capability of smoothing the "sharp edges" with a modicum of inventory variation, and in the last case with the advantage of built-in trend detection."Players" in non-altruistic supply chains must be able to cope with added uncertainties due to lead-time variations. We show that APIOBPCS may be well matched to such situations and is hence "copable" as well as "capable". The paper includes recommended parameter settings according to desired decision-making policies.

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“…The regression model is one of the main forecasting techniques [28]. These three methods are widely applied in industries that demand forecasting [15,25,6,7,20]. However, in the past three years, the LME model has been broadly applied in various fields, such as the timber industry [30], medicine [9,49], and ecology [31], and to identify crucial influential factors.…”

Section: Forecasting Methodsologymentioning

confidence: 99%

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

Liao

Chiu

2017

Croat. Oper. Res. Rev.

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Abstract. In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS) production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME) model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.

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The impact of exponential smoothing forecasts on the bullwhip effect

Cited by 49 publications

References 8 publications

Analysis of the Complexity Entropy and Chaos Control of the Bullwhip Effect Considering Price of Evolutionary Game between Two Retailers

Analysis of the Complexity Entropy and Chaos Control of the Bullwhip Effect Considering Price of Evolutionary Game between Two Retailers

A pragmatic approach to the design of bullwhip controllers

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

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