scholarly journals Review on Trends in Machine Learning Applied to Demand & Sales Forecasting

2019 ◽  
Vol 5 (6) ◽  
pp. 4
Author(s):  
Ravindra Singh Sengar ◽  
Dr. Faisal Ahmed
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Early Stage ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Materials Management ◽  
Chain Management ◽  
Product Demand ◽  
Most Significant Change

Supply Chain Management (SCM) is one of the new concepts put into practice in the commercial sector. At the beginning, Multinational Companies (MNCs) incorporated the supply chain into their structures, then other private conglomerates and local people defended these concepts. From the beginning, the main functions of SCM were the management of purchases and purchases, but subsequently SCM took the integrated form i.e. consists of sourcing, materials management, production support and sales management. Given the highly competitive market scenario, supply chain management is becoming the most important functional area of the business. Demand forecasting is affecting the success of Supply Chain Management (SCM), and the organizations which support them and are in the early stage of a digital transformation. In a near future it could represent the most significant change in the integrated SCM era in today’s complex, dynamic, and uncertain environment. The ability to adequately predict demand by the customers in an SCM is vital to the survival of any business. In this paper a review is presented in which this problem is tried to solved by using various demand forecasting models to predict product demand for grocery items with machine learning techniques.

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

A Survey on Machine Learning Techniques for Supply Chain Management

2021 ◽  
pp. 24-38
Author(s):  
Amal F.Abd El .. ◽  
◽  
◽  
◽  
Shereen Zaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Management Processes ◽  
Learning Techniques ◽  
Hidden Patterns ◽  
Learning Types ◽  
Discontinuous Data

Machine learning arose from the increasing ability of machines to handle large amounts of data over the last two decades, and some machines could also identify hidden patterns and complicated associations that humans couldn't, allowing them to make rational and precise decisions, especially for disruptive and discontinuous data. In several areas of decision-making, machines could produce more reliable outcomes than humans and have already begun to replace them. Machine learning, which is widely recognized as a breakthrough technology, has recently made significant progress in improving supply chain management processes and efficiency. From planning to delivery, machine learning may be applied at different stages of the supply chain management process. Machine learning types are supervised, unsupervised, reinforcement. Each type has many tools which are discussed below in detail. This paper presents a detailed survey on machine learning techniques for supply chain management including supply chain and supply chain management interpretation, machine learning definition, its types, and some algorithms that belong to it.

scholarly journals Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 565 ◽  
Author(s):  
Jiseong Noh ◽  
Hyun-Ji Park ◽  
Jong Soo Kim ◽  
Seung-June Hwang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Window Size ◽  
Batch Size ◽  
Chain Management ◽  
Product Demand ◽  
Forecasting Models ◽  
Gated Recurrent Unit

Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

scholarly journals Supply Chain Management with Demand Forecasting of Covid-19 Vaccine using Blockchain and Machine Learning

2021 ◽  
Author(s):  
Tamara Islam Meghla ◽  
Md. Mahfujur Rahman ◽  
Al Amin Biswas ◽  
Jeba Tahsin Hossain ◽  
Tania Khatun
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Chain Management

Missing Part of Halal Supply Chain Management

2018 ◽  
pp. 5456-5464 ◽  
Author(s):  
Ratih Hendayani ◽  
Yudi Fernando
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Islamic Law ◽  
Financial System ◽  
Chain Management ◽  
Product Demand ◽  
Supply Chain Financing ◽  
Financial Flow ◽  
New Perspective ◽  
The One

The increasing Halal product demand in the world made the Halal product industry should make sure all companies operation especially companies supply chain could guarantee product Halalness, so companies supply chain should comply Shariah or Islamic law. Some research about Halal supply chain already publish and give various perspectives about this topic. But unfortunately, there are one important thing that not discuss in previous research about Halal supply chain, the one important things is Islamic financial system that actually can support operation of Halal supply chain because based on the Islamic financial concept, the Halal supply chain can be not Halal or barakah if the supply chain financing not also comply with the Islamic law. For the integrated Halal supply chain, need included the Islamic Financial system, and this chapter will discuss Islamic financial that include in the Halal supply chain such as a Halal fund for the supply chain cost and Halal Financial flow. And bring a new perspective and definition of Halal supply chain management.

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