scholarly journals Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints

2021 ◽  
Vol 7 (2) ◽  
pp. 138
Author(s):  
Ika Nurkasanah
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Reinforcement Learning ◽  
Supply Chain Management ◽  
Machine Learning Algorithms ◽  
Inventory Policy ◽  
Chain Management ◽  
Q Learning ◽  
Lead Time Uncertainty ◽  
The Right

Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses.Objective: This paper aims to minimise total inventory cost in SCM by applying a multi-agent-based machine learning called Reinforcement Learning (RL).Methods: The ability of RL in finding a hidden pattern of inventory policy is run under various constraints which have not been addressed together or simultaneously in previous research. These include capacitated manufacturer and warehouse, limitation of order to suppliers, stochastic demand, lead time uncertainty and multi-sourcing supply. RL was run through Q-Learning with four experiments and 1,000 iterations to examine its result consistency. Then, RL was contrasted to the previous mathematical method to check its efficiency in reducing inventory costs.Results: After 1,000 trial-error simulations, the most striking finding is that RL can perform more efficiently than the mathematical approach by placing optimum order quantities at the right time. In addition, this result was achieved under complex constraints and assumptions which have not been simultaneously simulated in previous studies.Conclusion: Results confirm that the RL approach will be invaluable when implemented to comparable supply network environments expressed in this project. Since RL still leads to higher shortages in this research, combining RL with other machine learning algorithms is suggested to have more robust end-to-end SCM analysis. Keywords: Inventory Policy, Multi-Echelon, Reinforcement Learning, Supply Chain Management, Q-Learning

scholarly journals Blockchain technology in supply chain management: insights from machine learning algorithms

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enna Hirata ◽  
Maria Lambrou ◽  
Daisuke Watanabe
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Language Processing ◽  
Scientific Journal ◽  
Machine Learning Algorithms ◽  
Chain Management ◽  
Content Type ◽  
Blockchain Technology ◽  
Research Findings

Purpose This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from academic literature. Design/methodology/approach It first applies a text mining method to retrieve information from scientific journal papers on the related topics. The text information is then analyzed through machine learning (ML) models to identify the important implications from the existing literature. Findings The research findings are three-fold. While challenges are of concern, the focus should be given to the design and implementation of blockchain in the supply chain field. Integration with internet of things is considered to be of higher importance. Blockchain plays a crucial role in food sustainability. Research limitations/implications The research findings offer insights for both policymakers and business managers on blockchain implementation in the supply chain. Practical implications This paper exemplifies the model as situated in the interface of human-based and machine-learned analysis, potentially offering an interesting and relevant avenue for blockchain and supply chain management researchers. Originality/value To the best of the knowledge, the research is the very first attempt to apply ML algorithms to analyzing the full contents of blockchain-related research, in the supply chain sector, thereby providing new insights and complementing existing literature.

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.

An Evaluation and Scenario Analysis of the Representative Supply Chain Management Software

2011 ◽  
pp. 76-91
Author(s):  
Ruiliang Yan ◽  
Zhongxian Wang ◽  
Ruben Xing
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Business Environment ◽  
Competitive Advantages ◽  
Analytic Hierarchy ◽  
Chain Management ◽  
Software Packages ◽  
The Right ◽  
Hierarchy Process ◽  
A Company

Supply Chain Management (SCM) has proven to be an effective tool that aids companies in the development of competitive advantages. SCM Systems are relied on to manage warehouses, transportation, trade logistics and various other issues concerning the coordinated movement of products and services from suppliers to customers. Although in today’s fast paced business environment, numerous supply chain solution tools are readily available to companies, choosing the right SCM software is not an easy task. The complexity of SCM systems creates a multifaceted issue when selecting the right software, particularly in light of the speed at which technology evolves. In this chapter, we use the approach of Analytic Hierarchy Process (AHP) to determine which SCM software best meets the needs of a company. The AHP approach outlined in this paper can be easily transferred to the comparison of other SCM software packages.

scholarly journals Logistics Information Systems (LIS) on the Go-Mobile Apps and Social Media

2015 ◽  
Vol 7 (4) ◽  
pp. 64-73
Author(s):  
Hermann Gruenwald
Keyword(s):  
Social Media ◽  
Supply Chain ◽  
Information Systems ◽  
Supply Chain Management ◽  
Global Supply Chain ◽  
Smart Phones ◽  
Chain Management ◽  
The Past ◽  
Planning Software ◽  
The Right

Logistics has evolved over the past few decades from transportation and warehousing to global Supply Chain Management (SCM). This requires the coordination of the flow of material, money and information. The velocity of doing business has increased and manual operations have been automated. Modern Logistic Information Systems (LIS) with all its logistics related sub systems are replacing muscle power with brain power and pencil and paper with smart phones and social media. The virtual aspect of logistics has become equally important to the physical realm of transportation and warehousing. Supply Chain Management (SCM) deals with getting the right stuff to the right people at the right time in the right amount. To accomplish this task there are a number of more or less integrated logistics software application. Demand forecasting models based on historical data from data marts and data warehouses with built in seasonality and pricing models. Load planning software to appropriately palletize, containerize and load trucks, trains and vessels. Route planning software with real time traffic and weather updates combined with Global Positioning Systems (GPS) to reduce transportation time and fuel costs. Warehouse Management Systems (WMS) to receive, put-away, store, receive and marshal the shipment. Electronic documents accompany the shipment from purchase order, letter of credit to customs clearing and back-haul charges. While these applications in the past have been mostly desktop applications used in the office at the management level, the move is to mobile applications. The footprint of LIS is getting smaller and is moving from the desktop to the Smartphone. At the core of any logistic information systems (LIS) is electronic communication. With the advent of the internet and social media personal communication has taken on other forms. With smart phones and tablets like the I-Phone and I-Pad e-commerce advanced to m-commerce. While technology enables the global supply chain, how do future logistics professionals feel about applying this cutting edge communication technology in their personal and professional lives? This quantitative study compares the aptitude of Thai logistics management students towards the use of social media and modern mobile telecommunication technology in their personal lives and in the context of professional use in connection with logistics information systems (LIS).

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