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Keywords
###### Records with Keyword: Supply Chain
Algorithmic Approaches to Inventory Management Optimization
Hector D. Perez, Christian D. Hubbs, Can Li, Ignacio E. Grossmann
January 24, 2022 (v1)
Keywords: inventory management, multi-echelon, reinforcement learning, stochastic programming, Supply Chain
An inventory management problem is addressed for a make-to-order supply chain that has inventory holding and/or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to an uncertain stationary consumer demand at each time period. Two sales scenarios are considered for any unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies are tested in a stochastic s... [more]
Supply Chain Monitoring Using Principal Component Analysis
Jing Wang, Christopher Swartz, Brandon Corbett, Kai Huang
July 16, 2020 (v1)
Keywords: monitoring, Multivariate Statistics, Supply Chain
Various types of risks exist in a supply chain, and disruptions could lead to economic loss or even breakdown of a supply chain without an effective mitigation strategy. The ability to detect disruptions early can help improve the resilience of the supply chain. In this paper, the application of principal component analysis (PCA) and dynamic PCA (DPCA) in fault detection and diagnosis of a supply chain system is investigated. In order to monitor the supply chain, data such as inventory levels, market demands and amount of products in transit are collected. PCA and DPCA are used to model the normal operating conditions (NOC). Two monitoring statistics, the Hotelling's T-squared and the squared prediction error (SPE), are used to detect abnormal operation of the supply chain. The confidence limits of these two statistics are estimated from the training data based on the $\chi^2$- distributions. The contribution plots are used to identify the variables with abnormal behavior when at le... [more]
Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA
Ilyas Mzougui, Silvia Carpitella, Antonella Certa, Zoubir El Felsoufi, Joaquín Izquierdo
July 17, 2020 (v1)
Keywords: AHP, criticality and risk analysis, FMECA, fuzzy DEMATEL, Supply Chain, systems engineering
Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrate... [more]
Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs
Ilaria Zambon, Massimo Cecchini, Gianluca Egidi, Maria Grazia Saporito, Andrea Colantoni
April 15, 2019 (v1)
Subject: Energy Policy
Keywords: agriculture 4.0, application research, Industry 4.0, open source, SMEs, Supply Chain
The present review retraces the steps of the industrial and agriculture revolution that have taken place up to the present day, giving ideas and considerations for the future. This paper analyses the specific challenges facing agriculture along the farming supply chain to permit the operative implementation of Industry 4.0 guidelines. The subsequent scientific value is an investigation of how Industry 4.0 approaches can be improved and be pertinent to the agricultural sector. However, industry is progressing at a much faster rate than agriculture. In fact, already today experts talk about Industry 5.0. On the other hand, the 4.0 revolution in agriculture is still limited to a few innovative firms. For this reason, this work deals with how technological development affects different sectors (industry and agriculture) in different ways. In this innovative background, despite the advantages of industry or agriculture 4.0 for large enterprises, small- and medium-sized enterprises (SMEs) of... [more]
A Stackelberg Game Theoretic Analysis of Incentive Effects under Perceived Risk for China’s Straw-Based Power Plant Supply Chain
Lingling Wang, Tsunemi Watanabe
November 28, 2018 (v1)
Keywords: biomass power plant, perceived risk, stackelberg game theory, Supply Chain
The rapid expansion of the biomass power generation industry has resulted in the conversion of substantial agricultural waste (crop straw) into energy feedstock, thereby increasing the income of farmers and promoting the development of rural areas. However, the promising industry faces financial deficits because of difficulties in collecting straw from farmers. To determine strategies for overcoming the biomass supply problem, we apply Stackelberg game theory in modeling the Chinese biomass supply chain and design incentive scenarios under stakeholder risk perception. We illustrate the proposed methodology through an empirical case study on China and demonstrate the effects of incentives on farmers and middlemen. Results show that with incentives, straw quantity and stakeholder profit are expected to increase. Incentives exert a particularly remarkable effect on farmers, with such inducements producing the highest social welfare. Moreover, perceived risk dramatically affects stakeholde... [more]
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