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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.
Abstract:
Warehouse/distribution center (DC) automation technology for the retail industry promises to reduce operational costs, improve flexibility and response time for customers, and help improve network productivity, thus making it very relevant for omni/multichannel (OC/MC) settings. However, the investment required to acquire the DC automation technology is high, and hence, the investment decision must be operationally and financially comprehensive. In fact, an automated DC has a network-wide impact: it can benefit players in the network, but in turn is exposed to network risks and the investment must be safeguarded. While the need for a comprehensive decision-making framework and safeguarding strategy is stressed by scholars, such a framework is lacking. Further, corresponding integrated sub-frameworks for key elements in the OC/MC value chain are also missing. In this paper, we address these gaps and contribute by providing a) generalized and integrated three-part framework, b) corresponding sub-frameworks, c) discrete event, economic, and math programming models, d) rapid-sizing/analysis tools based on: i) analysis at the DC-level, ii) network level, iii) economic/business level, and iv) contract level (sustainable supplier/distribution relationship). In this reference, we investigate a new generation 'full-case' technology that has been recognized as a key to warehouse automation. The insights from our research inform several strategic tradeoffs (extent of automation, investment in labor vs. capital, response vs. efficiency, and sustainable supplier management) relevant for decision-making and safeguarding an expensive asset such as an automated DC. Our analysis is based on interviews (retailers, automated and conventional DCs, and DC equipment suppliers), on-site observations, secondary data, and learning from analytical models. We also present an illustrative real-life application/case study of the framework and the modeling details in the E-component.
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