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Related Concept Videos

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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A decision-making framework for automating distribution centers in the Retail supply.

Vivek Kumar Dubey1,2, Dharmaraj Veeramani1

  • 1Department of Industrial & Systems Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA.

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|May 29, 2024
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Summary

Investing in automated warehouse technology requires a comprehensive framework for decision-making and risk management. This study provides an integrated model to analyze automated distribution centers (DCs) at multiple levels, ensuring operational and financial viability.

Keywords:
Automated distribution center frameworkDecision making frameworkWarehouse automationWarehouse automation frameworkWarehouse decision framework

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Industrial Engineering

Background:

  • Warehouse/distribution center (DC) automation offers significant benefits for retail omni/multichannel (OC/MC) operations, including cost reduction and improved customer responsiveness.
  • High investment costs necessitate a thorough operational and financial evaluation, considering the network-wide impact and risks associated with automated DCs.
  • Existing research lacks a comprehensive decision-making framework and integrated sub-frameworks for managing automated DCs within the OC/MC value chain.

Purpose of the Study:

  • To address the gap in decision-making frameworks for automated distribution centers in retail.
  • To provide a generalized, integrated three-part framework and corresponding sub-frameworks for evaluating DC automation investments.
  • To develop analytical tools for rapid sizing and analysis at DC, network, economic, and contract levels.

Main Methods:

  • Development of discrete event, economic, and mathematical programming models.
  • Creation of rapid-sizing/analysis tools for DC, network, economic, and contract levels.
  • Empirical data collection through interviews, on-site observations, and secondary data analysis.

Main Results:

  • A generalized and integrated framework for decision-making and safeguarding automated DCs.
  • Insights into strategic trade-offs, including automation extent, labor vs. capital investment, and response vs. efficiency.
  • An illustrative real-life application/case study demonstrating the framework's utility.

Conclusions:

  • The proposed framework provides a comprehensive approach to investment decisions for automated DCs.
  • The study highlights the importance of considering network-wide impacts and safeguarding strategies for expensive automated assets.
  • The research informs strategic decision-making for retailers implementing warehouse automation in OC/MC environments.