The bullwhip effect in a 3-stage supply chain considering multiple retailers using a moving average method for demand forecasting

The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.

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Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595916500160 ◽

2016 ◽

Vol 33 (03) ◽

pp. 1650016 ◽

Cited By ~ 4

Author(s):

Xi Gang Yuan ◽

Nan Zhu

Keyword(s):

Supply Chain ◽

Moving Average ◽

Minimum Mean Square Error ◽

Distribution Network ◽

Demand Forecasting ◽

Bullwhip Effect ◽

Effect Analysis ◽

First Order ◽

Basic Work ◽

The Impact

Following the basic work conducted by Lee et al. [(1997a), The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102; (1997b), Information distribution in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558] and using two first-order autoregressive AR(1) models, respectively, this paper provides three quantitative models of the bullwhip effect of the two-level supply chain distribution network consisting of a single manufacturer and two retailers. The paper assumes that two retailers adopt the order point method, uses three kinds of demand forecasting technology, i.e., moving average, exponential smoothing and minimum mean square error methods, respectively, provides three corresponding models for analyzing the impact of bullwhip effect of two-level supply chain distribution network. At the same time, this paper compares and analyzes the results of the three models through simulation.

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Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

Engineering Management Research ◽

10.5539/emr.v7n2p20 ◽

2018 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

M. Tirtana Siregar ◽

S. Pandiangan ◽

Dian Anwar

Keyword(s):

Average Method ◽

Moving Average ◽

Demand Forecasting ◽

Production Capacity ◽

Mean Absolute Percentage Error ◽

Programming Method ◽

Percentage Error ◽

Absolute Percentage Error ◽

Forecasting Method ◽

Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

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ANALISIS MODEL MANAJEMEN PERMINTAAN SCM NETWORK DAN PERAMALAN PERMINTAAN PADA PENJUALAN BUSANA MUSLIM MENGGUNAKAN METODE LINEAR REGRESSION

METHOMIKA: Jurnal Manajemen Informatika dan Komputerisasi Akuntansi ◽

10.46880/jmika.vol5no1.pp28-32 ◽

2021 ◽

Vol 5 (1) ◽

pp. 28-32

Author(s):

Vela Maghfiroh ◽

◽

Yusuf Amrozi ◽

Qushoyyi Bondan Prakoso ◽

Mochamad Adam Aliansyah

Keyword(s):

Time Series ◽

Supply Chain ◽

Data Analysis ◽

Linear Regression ◽

Error Rate ◽

Moving Average ◽

Bullwhip Effect ◽

Lifestyle Changes ◽

Linear Regression Method ◽

Analysis Techniques

Supply chain management is very important for a company because it will affect supply performance in the company. Doing business in this era has many challenges that must be faced, especially in the Muslim clothing business. The way to stabilize the demand diagram of the Muslim clothing business, retailers are required to manage the supply chain so that they can meet the total demand. The object of this research is Rabbani Cirebon which was obtained from a literature study published in a journal entitled "Trend of Muslim Lifestyle Changes" from Banjarmasin State Polytechnic. The journal has sales data based on product types from monthly in 2016. From this data will be processed and analyzed using data analysis techniques. This data analysis technique uses time series forecasting data analysis techniques. From this time series method, this research uses moving average and linear regression. After modeling the data, the forecast error is measured using MAD, MAPE, RMSE, and MSE. The overall MSE results were 103731.8 and RMSE 322.0743. The benefit of demand forecasting is to reduce the Bullwhip Effect, plan future resources, for example, such as stock management, place control, product distribution, and demand for raw materials so as to make the right decisions. The results showed that the linear regression method has better forecasting than the moving average because linear regression has a smaller error rate than the moving average. But even so, the error rate of this study is still very large, so it is necessary to do more research to minimize the error rate.

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Bullwhip Effect Analysis in a Supply Chain

Supply Chain Management ◽

10.4018/978-1-4666-2625-6.ch038 ◽

2012 ◽

pp. 646-665

Author(s):

Mehdi Najafi ◽

Reza Zanjirani Farahani

Keyword(s):

Supply Chain ◽

Statistical Method ◽

Moving Average ◽

Bullwhip Effect ◽

Effect Analysis ◽

Forecasting Methods ◽

Effect Measure ◽

Forecasting Method ◽

Efficiency And Effectiveness ◽

Main Factors

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.

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Mean Local Trend Error and Fuzzy-Inference-Based Multicriteria Evaluation for Supply Chain Demand Forecasting

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p0134 ◽

2011 ◽

Vol 15 (2) ◽

pp. 134-144 ◽

Cited By ~ 1

Author(s):

Jingpei Dan ◽

◽

Fuding Xie ◽

Fangyan Dong ◽

Kaoru Hirota ◽

...

Keyword(s):

Supply Chain ◽

Evaluation Method ◽

Fuzzy Inference ◽

Moving Average ◽

Forecast Accuracy ◽

Bullwhip Effect ◽

Multicriteria Evaluation ◽

Accuracy Measure ◽

Local Trend ◽

Evaluating Forecasts

To overcome the inefficiency arising from the separate use of conventional forecast accuracy measures that suffer from the bullwhip effect, especially in uncertain and vague supply chain environments, a forecast accuracy measure, Mean Local Trend Error (MLTE) and a fuzzy-inference-based multicriteria evaluation method are proposed. In contrast to conventional measures, MLTE survives the bullwhip effect by evaluating forecasts based on local trend error. The proposed evaluation method applies fuzzy inference to deal with the uncertainty and vagueness in supply chains and makes a comprehensive evaluation by using an aggregated forecast accuracy index (ACCURACY), which is developed based on fuzzy inference by integrating the proposed MLTE and a conventional measure MAPE, thereby enhancing its efficiency for evaluating supply chain demand forecasts. The proposed MLTE and evaluation method are confirmed by comparative experiments with MAPE based on evaluating four typical forecasting methods – a simple moving average, single exponential smoothing, autoregressive, and autoregressive moving average – on an actual manufacturing-order dataset. The results show that MLTE yields a triple and ACCURACY a quadruple improvement in terms of average distinguishability compared to MAPE. The proposal has potential applications in stock market forecast evaluations.

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Exploring the Bullwhip Effect and Inventory Stability in a Seasonal Supply Chain

International Journal of Engineering Business Management ◽

10.5772/56833 ◽

2013 ◽

Vol 5 ◽

pp. 23 ◽

Cited By ~ 11

Author(s):

Francesco Costantino ◽

Giulio Di Gravio ◽

Ahmed Shaban ◽

Massimo Tronci

Keyword(s):

Supply Chain ◽

Information Sharing ◽

Performance Measures ◽

Moving Average ◽

Bullwhip Effect ◽

Fill Rate ◽

Supply Chain Dynamics ◽

Effect Ratio ◽

The Impact ◽

Demand Seasonality

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

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Study of the Bullwhip Effect under Various Forecasting Methods in Electronics Supply Chain with Dual Retailers considering Market Share

Complexity ◽

10.1155/2018/8539740 ◽

2018 ◽

Vol 2018 ◽

pp. 1-19

Author(s):

Junhai Ma ◽

Jing Zhang ◽

Liqing Zhu

Keyword(s):

Supply Chain ◽

Market Share ◽

Moving Average ◽

Bullwhip Effect ◽

Consumer Electronics ◽

Specific Reference ◽

Chain Model ◽

Forecasting Methods ◽

Effect Measure ◽

The Stability

We establish in this paper a new two-stage supply chain with one manufacturer and two retailers which have a fixed market share in the mature and stable market with specific reference to consumer electronics industry. This paper offers insights into how the three forecasting methods affect the bullwhip effect considering the market share under the ARMA(1,1) demand process and the order-up-to inventory policy. We also discuss the stability of the order with the theory of entropy. In particular, we derive the expressions of bullwhip effect measure under the MMSE, MA, and ES methods and compare them by numerical simulations. Results show that the MA is always better in contrast to the ES for reducing the bullwhip effect in our supply chain model. When moving average coefficient is lower than a certain value, the MMSE method is the best for reducing the bullwhip effect; otherwise, the MA method is the best. Moreover, the larger the market share of the retailer with a long lead time is, the greater the bullwhip effect is, no matter what the forecasting method is.

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Study of the Bullwhip Effect in a Multistage Supply Chain with Callback Structure considering Two Retailers

Complexity ◽

10.1155/2018/3650148 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Junhai Ma ◽

Liqing Zhu ◽

Ye Yuan ◽

Shunqi Hou

Keyword(s):

Supply Chain ◽

Moving Average ◽

Bullwhip Effect ◽

Lead Times ◽

Chain Model ◽

Supply Chain System ◽

Forecasting Method ◽

Supply Chain Model ◽

The Impact ◽

Demand Processes

With the purpose of researching the bullwhip effect when there is a callback center in the supply chain system, this paper establishes a new supply chain model with callback structure, which has a material supplier, a manufacture, and two retailers. The manufacture and retailers all employ AR(1) demand processes and use order-up-to inventory policy when they make order decisions. Moving average forecasting method is used to measure the bullwhip effect of each retailer and manufacture. We investigate the impact of lead-times of retailers and manufacture, forecasting precision, callback index, and marketing share on the bullwhip effect of both retailers and manufacture. Then we use the method of numerical simulation to indicate the different parameters in this supply chain. Furthermore, this paper puts forward some suggestions to help the enterprises to control the bullwhip effect in the supply chain with callback structure.

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Pengurangan Efek Bullwhip Pada Vendor Tunggal dan Eceran Multi Di PT. XYZ

Talenta Conference Series Energy and Engineering (EE) ◽

10.32734/ee.v2i4.671 ◽

2019 ◽

Vol 2 (4) ◽

Author(s):

Meilita Tryana Sembiring ◽

Feby Sanna Sibarani

Keyword(s):

Supply Chain ◽

Moving Average ◽

Linear Method ◽

Bullwhip Effect ◽

Exponential Smoothing ◽

Glass Bottle ◽

Forecasting Methods ◽

Forecasting Method ◽

The Impact ◽

A Company

PT. XYZ merupakan perusahaan yang bergerak dalam produksi produk – produk olahan teh. Perusahaan telah memproduksi berbagai varian the yakni bentuk mau pun jenis teh. Objek penelitian ini ialah the dalam kemasan botol kaca dengan ukuran 220 ml. Ukuran the tersebut dipilih berdasarkan akumulasi dari penjualan the tertinggi. Terdapat perbedaan pada prediksi jumlah produksi yang akan dilakukan. Prediksi jumlah produksi dapat dilakukan dengan melakukan peramalan permintaan serta penggunaan metode yang tepat. Rantai pasok yang diteliti pada PT. XYZ terdiri atas Manufaktur (Vendor), Kantor Penjualan, dan Dister. Awalnya peramalan dilakukan pada masing – masing level rantai pasok dengan metode peramalan yang berbeda – beda. Maka, diperlukan penyeragaman metode peramalan pada masing – masing pelaku rantai pasok. Berdasarkan pengujian metode peramalan yang dilakukan yakni metode Linear, Exponential Smoothing, Moving Average, dan Winter’sMethod. Diperoleh bahwa error terkecil terdapat pada metode peramalan Winter’s Method dengan parameter Level sebesar 0,5, Trend sebesar 0,2 dan Seasonal sebesar 0,6. Parameter error yang digunakan ialah MAPE, MAD, dan MSD. Hasil penelitian menunjukkan bahwa penggunaan metode peramalan yang tepat akan mengurangi dampak dari bullwhip effect yang terjadi pada PT. XYZ. PT. XYZ is a company engaged in the production of processed tea products. The company has produced various variants of tea, that is the shape and type of tea. The object of this research is the 220 ml glass bottle packaging. The size of the tea is chosen based on the accumulation of the highest tea sales. There is a difference in the prediction of the amount of production to be carried out. Prediction of the amount of production can be done by forecasting demand and using appropriate methods. The supply chain studied at PT. XYZ consists of Manufacturing (Vendors), Sales Offices, and Disters. Initially forecasting is done at each level of the supply chain with different forecasting methods. Therefore, uniform forecasting methods are needed for each supply chain actor. Based on testing the forecasting method that is done namely the Linear method, Exponential Smoothing, Moving Average, and Winter’s Method. Obtained that the smallest error is found in the Winter’s Method forecasting method with a Level parameter of 0.5, a Trend of 0.2 and a Seasonal of 0.6. The error parameters used are MAPE, MAD, and MSD. The results showed that the use of appropriate forecasting methods would reduce the impact of the bullwhip effect that occurred at PT. XYZ

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