scholarly journals A New Sales Forecasting Method for Industrial Supply Chain

2021 ◽  
Vol 9 (2) ◽  
pp. 1-12
Author(s):  
Mahmoud Zadeh ◽  
Keyword(s):  
Supply Chain ◽  
Forecasting Method

Bullwhip Effect Analysis in a Supply Chain

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.

scholarly journals Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

2014 ◽  
Vol 24 (3) ◽  
pp. 669-682 ◽  
Author(s):  
D. Thresh Kumar ◽  
Hamed Soleimani ◽  
Govindan Kannan
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Supply Chain Design ◽  
Second Phase ◽  
Planning Problem ◽  
Two Phase ◽  
Closed Loop Supply Chain ◽  
Inference System ◽  
Forecasting Method ◽  
Phase Optimization

Abstract Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
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.

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

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

Forecasts impacts on sanitary risk during a crisis: a case study

2014 ◽  
Vol 25 (2) ◽  
pp. 358-378 ◽  
Author(s):  
Daniel Thiel ◽  
Thi Le Hoa Vo ◽  
Vincent Hovelaque
Keyword(s):  
Supply Chain ◽  
Word Of Mouth ◽  
Meat Products ◽  
Production Capacity ◽  
Buffer Size ◽  
Content Type ◽  
Crisis Period ◽  
Meat Demand ◽  
Forecasting Method ◽  
Additional Costs

Purpose – During a crisis situation, a poultry supply chain is faced with high variations on fresh chicken meat demand and has therefore to simultaneously manage excessive shelf-life stocks (in case of falling demand) and external purchases due to inventory shortages. In this case, the production plan is often established according to non-accurate sale forecasts which require ongoing adjustment. The paper aims to discuss these issues. Design/methodology/approach – By using system dynamics, the paper developed a model of the French poultry supply chain during a given avian influenza crisis period. The authors compared exponential smoothing forecasting method to a word-of-mouth diffusion model which makes sense in a sanitary crisis context. Findings – An interesting result shows a complex relationship between the sanitary risk (which increases according to the slaughtered chicken's volume and storage time) and the additional external purchases (in case of low production generated by an insufficient forecasting launched 40 days before customer orders). Research limitations/implications – Additional costs which vary over time are required for further assumptions testing. Practical implications – The paper proposes to use a forecasting model which is not currently used by the professionals during a sanitary crisis period. This model is able to simulate an internal dissemination of a call for boycott of meat products (cf. negative word-of-mouth spread). Originality/value – The problem is how to maintain a less risky but significant buffer size to respond to a supply chain coping with both changes in customers’ demand and instability in production capacity.

scholarly journals AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND

Transport ◽  
2008 ◽  
Vol 23 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Xin Miao ◽  
Bao Xi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Kalman Filter ◽  
Influencing Factors ◽  
Extended Kalman Filter ◽  
Comparison Analysis ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Logistics Demand

The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results. Comparison analysis shows that the forecasting method has better reliability for agile forecasting of dynamic logistics demand.

Forecasting method selection in a global supply chain

2012 ◽  
Vol 28 (4) ◽  
pp. 842-848 ◽  
Author(s):  
Yavuz Acar ◽  
Everette S. Gardner
Keyword(s):  
Supply Chain ◽  
Global Supply Chain ◽  
Method Selection ◽  
Forecasting Method

Exploring a Downstream Demand Inference Strategy in a Decentralized Two-Level Supply Chain

2021 ◽  
pp. 1-65
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chain ◽  
Mean Squared Error ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Inventory Level ◽  
Squared Error ◽  
Forecasting Method ◽  
Demand Information ◽  
The Mean ◽  
Inference Strategy

A coordination approach for forecast operations, known as downstream demand inference, enables an upstream actor to infer the demand information at his formal downstream actor without the need for information sharing. This approach was validated if the downstream actor uses the simple moving average (SMA) forecasting method. To answer an investigative question through other forecasting methods, the authors use the weighted moving average (WMA) method, whose weights are determined in this work thanks to the Newton's optimization of the upstream average inventory level. Starting from a two-level supply chain, the simulation results confirm the ability of the approach to reduce the mean squared error and the average inventory level, compared to a decentralized approach. However, the bullwhip effect is only improved after a certain threshold of the parameter of the forecasting method. Still within the framework of the investigation, they carry out a comparison study between the adoption of the SMA method and the WMA method. Finally, they generalize their results for a multi-level supply chain.

scholarly journals Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain

2019 ◽  
Vol 13 (2) ◽  
pp. 4816-4834
Author(s):  
E. Fradinata ◽  
S. Suthummanon ◽  
W. Suntiamorntut ◽  
Muhamad Mat Noor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Support Vector Regression ◽  
Bullwhip Effect ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Inventory Policy ◽  
Forecasting Method ◽  
Artificial Neural

The objective of this study was to compare the Bullwhip Effect (BWE) in the supply chain through two methods and to determine the inventory policy for the uncertainty demand. It would be useful to determine the best forecasting method to predict the certain condition. The two methods are Artificial Neural Network (ANN) and Support Vector Regression (SVR), which would be applied in this study. The data was obtained from the instant noodle dataset where it was in random normal distribution. The forecasting demands signal have Mean Squared Error (MSE) where it is used to measure the bullwhip effect in the supply chain member. The magnification of order among the member of the supply chain would influence the inventory. It is quite important to understand forecasting techniques and the bullwhip effect for the warehouse manager to manage the inventory in the warehouse, especially in probabilistic demand of the customer. This process determines the appropriate inventory policy for the retailer. The result from this study shows that ANN and SVR have the variance of 0.00491 and 0.07703, the MSE was 1.55e-6 and 1.53e-2, and the total BWE was 95.61 and 1237.19 respectively. It concluded that the ANN has a smaller variance than SVR, therefore, the ANN has a better performance than SVR, and the ANN has smaller BWE than SVR. At last, the inventory policy was determined with the continuous review policy for the uncertainty demand in the supply chain member.

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