MRO Spare Parts Management and Inventory Optimization

In this chapter, considering the importance of spare parts inventory management in the equipment life cycle, the excellence models in spare parts supply chain management are reviewed, and MRO-MMM framework based on the MMM maintenance excellence model will be presented in eight steps. In this comprehensive framework, all necessary actions are considered in terms of maintenance excellence, improvement, and optimization spare parts management. The steps include compiling strategies, spare parts management policies, and related indicators; determining criticality, classification, and spare parts data management; data and procedure preparation for analysis of inventory management; optimization of inventory management system; supplier management; integrity of automation and information system; continuous improvement; and risk based and smarting inventory management. This framework has been used in various industries and proved that the implementation of the MRO-MMM framework will optimize and significantly improve spare parts management.

Inventory Replenishment Policies for Two Successive Generations of Technology Products under Permissible Delay in Payments

In this age of digitalization, when every industry is undergoing technological disruption, there is a big role of digital gadgets and technology products. A key feature of these digital gadgets is the short length of the product life cycle, since the newer and more advanced generations of technologies are developed regularly to replace the earlier conventional technologies. The traditional EOQ models that assume a constant demand cannot be used here. This research paper formulates an inventory optimization model for the multi-generational products under the trade credits and the credit-linked and innovation diffusion dependent demand. The study also performs a numerical illustration of the proposed model, and establishes important dynamics among the key variables. It also performs the sensitivity analysis with the cost of credit and the trade credit period. The paper concludes with the managerial implications for the inventory practitioners and the possible areas of extension for this research in the future.

Omnichannel: A Multi-Purpose Approach

This paper studies how omnichannel is different from other channels and gives it the upper hand over the rest. The paper expands on customer experience or journey in omnichannel and how the backend, i.e., retailing, works. Moreover, it explains how different supply chains are integrated to build a successful omnichannel network. It also elucidates how traditional suppliers have evolved and shifted to omnichannel. Furthermore, it explains the role of inventory optimization & sales and operations planning and how it helps us develop and maintain a stable omnichannel supply chain. In addition, the paper also discusses some points that should be taken into accounts while modeling omnichannel environments.

IbM Pelatihan, Pembinaan dan Pendampingan Penggunaan Aplikasi Management Stock Control

The purpose of this service activity is to provide Training, Coaching, and Assistance for Stock Control Management Applications and it is hoped that the participants can; 1) Understand the strategy for the supply of goods, 2) Understand and be able to control spare parts, 3) Be able to provide the goods needed in the remodeling process, 4) Be able to manage spare parts efficiently and be able to apply methods Determine the level of reordering and minimum inventory for inventory optimization, and 5) Evaluating the material planning system that has been running so that we can find out the core of the problems faced and how to solve them. The method of implementing the program to be carried out is; 1) Presentation, 2) Experience Sharing Discussion, 3) Training (Training for Users), and 4) Further assistance in the use of the application. This Community Service Implementation Activity was held for 4 (four) months from early December 2019 to the end of March 2020. This activity was attended by all personnel of Gober Indo's local business partners. Based on the results of the activities that have been carried out, several conclusions can be drawn, namely; 1) The development of a web-based Stock Control Management application has been successfully built, 2) Assistance and training activities for the use of the appropriate Stock Control Management Application and can help partners, 3) Service activities that are followed very enthusiastically, It can be seen that many members and owners have attended the training, and 4) Supporting activities for the Stock Control Management Application are expected to continue with the development of partner business finances.

A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization

Problem definition: The beer game is widely used in supply chain management classes to demonstrate the bullwhip effect and the importance of supply chain coordination. The game is a decentralized, multiagent, cooperative problem that can be modeled as a serial supply chain network in which agents choose order quantities while cooperatively attempting to minimize the network’s total cost, although each agent only observes local information. Academic/practical relevance: Under some conditions, a base-stock replenishment policy is optimal. However, in a decentralized supply chain in which some agents act irrationally, there is no known optimal policy for an agent wishing to act optimally. Methodology: We propose a deep reinforcement learning (RL) algorithm to play the beer game. Our algorithm makes no assumptions about costs or other settings. As with any deep RL algorithm, training is computationally intensive, but once trained, the algorithm executes in real time. We propose a transfer-learning approach so that training performed for one agent can be adapted quickly for other agents and settings. Results: When playing with teammates who follow a base-stock policy, our algorithm obtains near-optimal order quantities. More important, it performs significantly better than a base-stock policy when other agents use a more realistic model of human ordering behavior. We observe similar results using a real-world data set. Sensitivity analysis shows that a trained model is robust to changes in the cost coefficients. Finally, applying transfer learning reduces the training time by one order of magnitude. Managerial implications: This paper shows how artificial intelligence can be applied to inventory optimization. Our approach can be extended to other supply chain optimization problems, especially those in which supply chain partners act in irrational or unpredictable ways. Our RL agent has been integrated into a new online beer game, which has been played more than 17,000 times by more than 4,000 people.