Strategic Evaluation in Optimizing the Internal Supply Chain Using
TOPSIS: Evidence In A Coil Winding Machine Manufacturer
- URL: http://arxiv.org/abs/2007.10121v1
- Date: Wed, 8 Jul 2020 12:46:42 GMT
- Title: Strategic Evaluation in Optimizing the Internal Supply Chain Using
TOPSIS: Evidence In A Coil Winding Machine Manufacturer
- Authors: Dilip U Shenoy, Vinay Sharma, Shiva HC Prasad
- Abstract summary: This study takes a critical look into the factors that affect the Performance of internal supply chain.
The results of this indicate that supplier relationship and inventory planning were most principal factors positively influencing on-time delivery of the product.
- Score: 1.6114012813668934
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Most of the manufacturing firm aims to optimize their Supply Chain in terms
of improved profitability of its products through value Addition. This study
takes a critical look into the factors that affect the Performance of internal
supply chain with respect to specific criteria. Accordingly, ranking these
factors to get the critical dimensions of supply chain performance in the
manufacturing industry. A semi-structured interview with the pre-defined set of
questions used to collect the responses from decision makers of the firm. Multi
criteria decision-making tool called TOPSIS is used to evaluate the responses
and rank the factors. The results of this indicate that supplier relationship
and inventory planning were most principal factors positively influencing
on-time delivery of the product, production flexibility, cost savings,
additional costs. This study helps to identify and optimize the process
parameters using objective and subjective evaluation approach. The combined
influence of the thought process of the manager to optimize the internal supply
chain is extracted in this work.
Related papers
- Uncovering Factor Level Preferences to Improve Human-Model Alignment [58.50191593880829]
We introduce PROFILE, a framework that uncovers and quantifies the influence of specific factors driving preferences.
ProFILE's factor level analysis explains the 'why' behind human-model alignment and misalignment.
We demonstrate how leveraging factor level insights, including addressing misaligned factors, can improve alignment with human preferences.
arXiv Detail & Related papers (2024-10-09T15:02:34Z) - Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing [0.0]
This study investigates the comparative effectiveness of two weight selection strategies: Final Epoch Weight Selection (FEWS) and Optimal Epoch Weight Selection (OEWS)
We employ various neural network architectures, including EfficientNet, ResNet, and VGG, to assess the impact of these weight selection strategies on model convergence and robustness.
arXiv Detail & Related papers (2024-08-19T14:18:21Z) - Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning [0.0]
This paper presents a novel approach to root cause attribution of delivery risks within supply chains by integrating causal discovery with reinforcement learning.
We apply our approach to a real-world supply chain dataset demonstrating its effectiveness in uncovering the underlying causes of delivery delays.
arXiv Detail & Related papers (2024-08-11T20:52:51Z) - A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems [67.52782366565658]
State-of-the-art recommender systems (RSs) depend on categorical features, which ecoded by embedding vectors, resulting in excessively large embedding tables.
Despite the prosperity of lightweight embedding-based RSs, a wide diversity is seen in evaluation protocols.
This study investigates various LERS' performance, efficiency, and cross-task transferability via a thorough benchmarking process.
arXiv Detail & Related papers (2024-06-25T07:45:00Z) - Rational Decision-Making Agent with Internalized Utility Judgment [91.80700126895927]
Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications.
This paper proposes RadAgent, which fosters the development of its rationality through an iterative framework involving Experience Exploration and Utility Learning.
Experimental results on the ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving over 10% improvement in Pass Rate on diverse tasks.
arXiv Detail & Related papers (2023-08-24T03:11:45Z) - Large Language Models for Supply Chain Optimization [4.554094815136834]
We study how Large Language Models (LLMs) can help bridge the gap between supply chain automation and human comprehension and trust thereof.
We design OptiGuide -- a framework that accepts as input queries in plain text, and outputs insights about the underlying outcomes.
We demonstrate the effectiveness of our framework on a real server placement scenario within Microsoft's cloud supply chain.
arXiv Detail & Related papers (2023-07-08T01:42:22Z) - Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies.
Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times.
This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
arXiv Detail & Related papers (2022-02-26T02:07:25Z) - Taylor Expansion of Discount Factors [56.46324239692532]
In practical reinforcement learning (RL), the discount factor used for estimating value functions often differs from that used for defining the evaluation objective.
In this work, we study the effect that this discrepancy of discount factors has during learning, and discover a family of objectives that interpolate value functions of two distinct discount factors.
arXiv Detail & Related papers (2021-06-11T05:02:17Z) - Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features.
We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
arXiv Detail & Related papers (2020-12-21T01:01:56Z) - Implicit Feedback Deep Collaborative Filtering Product Recommendation
System [1.6651146574124562]
Collaborative Filtering (CF) approaches with latent variable methods were studied to capture important hidden variations of the sparse customer purchasing behaviours.
The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations.
The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.
arXiv Detail & Related papers (2020-09-08T19:30:14Z) - Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation
based on Deep Learning in the Machining Industry [0.0]
We propose that end-of-life products have -- besides their value as recyclable assets -- additional value for producer and consumer.
We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools.
We present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping.
arXiv Detail & Related papers (2020-07-24T07:06:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.