Supply chain planning in the food industry

David Stüve, Robert van der Meer, Matthias Lütke Entrup and Mouhamad Shaker Ali Agha

A chapter in Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain, 2020, pp 317-353 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: Purpose: Advanced Planning Systems (APS) can contribute to improved decision-making and enhanced efficiency along complex food supply chains. This paper presents a systematic literature review of supply chain planning (SCP) in the food industry. In particular, the literature on three increasingly important planning tasks supported by APS is examined, namely Supply Chain Network Design, Sales & Operations Planning and Production Planning & Scheduling. Methodology: A literature review is conducted by systematically collecting the existing literature published between 1998 and 2020 and classifying it based on three planning tasks supported by APS modules (Supply Chain Network Design, Sales & Operations Planning and Production Planning & Scheduling). Furthermore, research papers are categorized according to the product under consideration, geographic region and method. Findings: Multiple models for SCP practices have been developed. The modelling literature is fragmented around specific challenges faced in food supply chains. Empirical literature including case studies on the implementation of APS is sparse. The findings suggest that developed models for the three examined planning tasks are only implemented to a limited extent in practice. Originality: This paper focuses on three planning tasks that are of increasing relevance for the food industry. The literature review can help practitioners within the food industry to get insights regarding the opportunities offered by the three software modules examined in this paper. Further research should be conducted in these areas to make literature on SCP more practically relevant for managers.

Keywords: Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228926

DOI: 10.15480/882.3135

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