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.

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.

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.

Capability Maturity Model for Agricultural Supply Chain Management Software

2021 ◽  
pp. 196-216
Author(s):  
Saliya Nugawela ◽  
Darshana Sedera
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Capability Maturity Model ◽  
Maturity Model ◽  
Chain Process ◽  
Chain Management ◽  
Management Software ◽  
Capability Maturity ◽  
Management Processes ◽  
Supply Chain Process

Compared to the supply chain management of other business domains, agricultural supply chain management is affected with issues such as diversity of production and demand, the bulkiness of produce, perishability, seasonality, harvest uncertainty, and climate complexity. These issues are more prominent in rural agricultural sector. Availability of mature supply chain management processes and systems can enhance the productivity of rural agricultural communities. This chapter proposes a five-stage capability maturity model for the implementation and maintenance of supply chain management processes in farm management information systems. The capability maturity model is a valuable aid to determine the digitized supply chain process' ability to consistently and continuously achieve improvement and organizational objectives. The model is proposed based on the findings of the analysis of 121 supply chain management software in the farming sector, the Capability Maturity Model by the Software Engineering Institute, and the Supply Chain Process Management Maturity Model.

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