Supply chain planning in the food industry : a systematic literature review

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

Product Wheels for Scheduling in the Baking Industry: A Case Study

This paper illustrates current challenges and suggests solutions within the area of scheduling in the baking industry. The analysis applies the product wheel heuristic approach of King (2009) and tests the production cycles generated using actual sales and production data from a manufacturer of frozen baked goods. The product wheel method showed to be a suitable method for application at the baked goods manufacturer and generated a 23% reduction in setup and inventory cost at the case company. Despite the benefits, the product wheel method proved difficult to apply in a high variety setting, where an operations research model may have achieved more significant results

A cooperative game approach to a production planning problem

This paper deals with a production planning problem formulated as a Mixed Integer Linear Programming (MILP) model that has a competition component, given that the manufacturers are willing to produce as much products as they can in order to fulfil the market’s needs. This corresponds to a typical game theoretic problem applied to the productive sector, where a global optimization problem involves production planning in order to maximize the utilities for the different firms that manufacture the same type of products and compete in the market. This problem has been approached as a cooperative game, which involves a possible cooperation scheme among the manufacturers. The general problem was approached by Owen (1995) as the “production game” and the core was considered. This paper identifies the cooperative game theoretic model for the production planning MILP optimization problem and Shapley Value was chosen as the solution approach. The results obtained indicate the importance of cooperating among competitors. Moreover, this leads to economic strategies for small manufacturing companies that wish to survive in a competitive environment

Supply chain planning in the food industry : empirical studies on the adoption of advanced planning systems

Supply chain management in the food industry is challenging due to multiple factors such as the limited shelf life of food products. Supply chain planning (SCP) is required to balance the demand with the supply of products. Advanced planning systems (APS) constitute the technological means for sophisticated methods of SCP. APS can contribute to improved decision-making and enhanced efficiency along food supply chains. However, studies reveal limited implementation of APS in practice. This thesis investigates the level of APS implementation in the food industry and factors affecting the adoption of APS by means of mixed methods research comprising a survey among food producers and expert interviews. The study confirms the limited use of specialised software for SCP. Many food companies perform SCP tasks by basic functions of enterprise resource planning (ERP) systems. Lack of human resources and costs associated with implementation projects inhibit companies to adopt APS. Supply chain complexity induces food companies to adopt APS. Besides enhanced planning accuracy, the usability of APS is regarded as particularly important by companies. Based on the findings an adapted technology acceptance model (TAM) for the context of APS is established. In addition, the research provides practical advice how implementation projects can be facilitated. Companies need to ensure the availability of skilled employees, highlight process requirements, and prioritise data quality. Management support for the software implementation should be maintained throughout the project. Furthermore, companies should strategically reflect on SCP practices together with company goals to ensure proper software selection. The analysis of quantitative and qualitative data reveals a comprehensive view on APS implementation in the food industry. This is reinforced by the triangulation of different perspectives through interviews with food producers, software vendors and consultants. Limitations of this research and suggestions for future research are outlined in the concluding chapter of this thesis.Supply chain management in the food industry is challenging due to multiple factors such as the limited shelf life of food products. Supply chain planning (SCP) is required to balance the demand with the supply of products. Advanced planning systems (APS) constitute the technological means for sophisticated methods of SCP. APS can contribute to improved decision-making and enhanced efficiency along food supply chains. However, studies reveal limited implementation of APS in practice. This thesis investigates the level of APS implementation in the food industry and factors affecting the adoption of APS by means of mixed methods research comprising a survey among food producers and expert interviews. The study confirms the limited use of specialised software for SCP. Many food companies perform SCP tasks by basic functions of enterprise resource planning (ERP) systems. Lack of human resources and costs associated with implementation projects inhibit companies to adopt APS. Supply chain complexity induces food companies to adopt APS. Besides enhanced planning accuracy, the usability of APS is regarded as particularly important by companies. Based on the findings an adapted technology acceptance model (TAM) for the context of APS is established. In addition, the research provides practical advice how implementation projects can be facilitated. Companies need to ensure the availability of skilled employees, highlight process requirements, and prioritise data quality. Management support for the software implementation should be maintained throughout the project. Furthermore, companies should strategically reflect on SCP practices together with company goals to ensure proper software selection. The analysis of quantitative and qualitative data reveals a comprehensive view on APS implementation in the food industry. This is reinforced by the triangulation of different perspectives through interviews with food producers, software vendors and consultants. Limitations of this research and suggestions for future research are outlined in the concluding chapter of this thesis

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Possibilistic compositions and state functions: application to the order promising process for perishables

"This is an Author's Accepted Manuscript of an article published in Grillo, H., M.M.E. Alemany, A. Ortiz, and B. De Baets. 2019. Possibilistic Compositions and State Functions: Application to the Order Promising Process for Perishables. International Journal of Production Research 57 (22). Informa UK Limited: 7006 31. doi:10.1080/00207543.2019.1574039, available online at: https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1574039"[EN] In this paper, we propose the concepts of the composition of possibilistic variables and state functions. While in conventional compositional data analysis, the interdependent components of a deterministic vector must add up to a specific quantity, we consider such components as possibilistic variables. The concept of state function is intended to describe the state of a dynamic variable over time. If a state function is used to model decay in time, it is called the ageing function. We present a practical implementation of our concepts through the development of a model for a supply chain planning problem, specifically the order promising process for perishables. We use the composition of possibilistic variables to model the existence of different non-homogeneous products in a lot (sub-lots with lack of homogeneity in the product), and the ageing function to establish a shelf life-based pricing policy. To maintain a reasonable complexity and computational efficiency, we propose the procedure to obtain an equivalent interval representation based on alpha -cuts, allowing to include both concepts by means of linear mathematical programming. Practical experiments were conducted based on data of a Spanish supply chain dedicated to pack and distribute oranges and tangerines. The results validated the functionality of both, the compositions of possibilistic variables and ageing functions, showing also a very good performance in terms of the interpretation of a real problem with a good computational performance.We would also thank Dr. José De Jesús Arias García for useful discussions during the development of this work. This research has been supported by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the Program of Innovation and Human Capital for Competitiveness (PINN) (contract number PED-019-2015-1). We acknowledge the partial support of the project 691249, RUCAPS: Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems , funded by the European Union s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions.Grillo-Espinoza, H.; Alemany Díaz, MDM.; Ortiz Bas, Á.; De Baets, B. (2019). Possibilistic compositions and state functions: application to the order promising process for perishables. International Journal of Production Research. 57(22):7006-7031. https://doi.org/10.1080/00207543.2019.1574039S700670315722Grillo, H., M. Alemany, and A. Ortiz. 2016b. Modelling Pricing Policy Based on Shelf-Life of Non-Homogeneous Available-To-Promise in Fruit Supply Chains, 608–617. doi:10.1007/978-3-319-45390-3_52Steglich, M., and T. Schleiff. 2010. “CMPL: Coliop Mathematical Programming Language.” Technische Hochschule Wildau. doi:10.15771/978-3-00-031701-9

Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models

This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the International Journal of Production Research (2018) © Taylor & Francis, available online at: http://doi.org/10.1080/00207543.2018.1447706[EN] Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC¿s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified.This first author was partially supported by the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport [grant number FPU15/03595]; the partial support of Project 'Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector.' Ref. GV/2017/025, funded by the Generalitat Valenciana. The other authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research. 56(13):4418-4446. https://doi.org/10.1080/00207543.2018.1447706S44184446561

A Review on Quantitative Models for Sustainable Food Logistics Management

The last two decades food logistics systems have seen the transition from a focus on traditional supply chain management to food supply chain management, and successively, to sustainable food supply chain management. The main aim of this study is to identify key logistical aims in these three phases and analyse currently available quantitative models to point out modelling challenges in sustainable food logistics management (SFLM). A literature review on quantitative studies is conducted and also qualitative studies are consulted to understand the key logistical aims more clearly and to identify relevant system scope issues. Results show that research on SFLM has been progressively developing according to the needs of the food industry. However, the intrinsic characteristics of food products and processes have not yet been handled properly in the identified studies. The majority of the works reviewed have not contemplated on sustainability problems, apart from a few recent studies. Therefore, the study concludes that new and advanced quantitative models are needed that take specific SFLM requirements from practice into consideration to support business decisions and capture food supply chain dynamics