Selecting food process designs from a supply chain perspective

The food industry can convert agro-materials into products using many alternative process designs. To remain competitive, companies have to select the design leading to the best supply chain performance. These designs differ in the technologies used and the product portfolio produced. Additionally, characteristics, such as seasonal production and quality decay of food products, lead to specific requirements regarding processing, transportation and storage. The importance of these characteristics of the food industry on process design selection is investigated using sugar beet processing as an illustrative case. The characteristics are included in a multi-period, multi-product location-allocation model. The model shows that a supply chain perspective leads to changes in process design selection. The design with the best portfolio value and processing costs does not lead to the best supply chain performance. This shows the importance of a chain perspective to avoid sub-optimization in food process design selection.

[1]  Ashim K. Datta,et al.  Toward Computer-Aided Food Engineering: Mechanistic Frameworks for Evolution of Product, Quality and Safety During Processing , 2016 .

[2]  Michael Bourlakis,et al.  Food Supply Chain Management , 2004 .

[3]  Dilip Chhajed,et al.  Applications of location analysis in agriculture: a survey , 2004, J. Oper. Res. Soc..

[4]  Dirk Cattrysse,et al.  A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS) , 2015, Eur. J. Oper. Res..

[5]  Gonzalo Guillén-Gosálbez,et al.  Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty , 2012 .

[6]  Angappa Gunasekaran,et al.  A decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks , 2012, Decis. Support Syst..

[7]  Ignacio E. Grossmann,et al.  Enterprise‐wide optimization: A new frontier in process systems engineering , 2005 .

[8]  Alberto Bertucco,et al.  Production of phenolic and glucosinolate extracts from rocket salad by supercritical fluid extraction: Process design and cost benefits analysis , 2016 .

[9]  Francisco Saldanha-da-Gama,et al.  Facility location and supply chain management - A review , 2009, Eur. J. Oper. Res..

[10]  R. van der Noll,et al.  Prijzige suiker: de prijsopbouw en prijsontwikkeling van suiker , 2013 .

[11]  Giuseppe Vignali,et al.  Comparative Life Cycle Assessment of hot filling and aseptic packaging systems used for beverages , 2015 .

[12]  Jacqueline M. Bloemhof,et al.  Hybrid optimization and simulation to design a logistics network for distributing perishable products , 2015, Comput. Ind. Eng..

[13]  R. C. Kolfschoten,et al.  Opportunities for small‐scale biorefinery for production of sugar and ethanol in the Netherlands , 2014 .

[14]  William Faulkner,et al.  A multidisciplinary decision support tool for evaluating multiple biorefinery conversion technologies and supply chain performance , 2014, Clean Technologies and Environmental Policy.

[15]  Andrew Higgins,et al.  Opportunities for value chain research in sugar industries , 2007 .

[16]  Svatopluk Henke,et al.  Model of a sugar factory with bioethanol production in program Sugars , 2006 .

[17]  Robert Beauregard,et al.  Designing logistics networks in divergent process industries: A methodology and its application to the lumber industry , 2006 .

[18]  George Ioannou.  Streamlining the supply chain of the Hellenic sugar industry , 2005 .

[19]  David L. Woodruff,et al.  Pyomo: modeling and solving mathematical programs in Python , 2011, Math. Program. Comput..

[20]  Renzo Akkerman,et al.  Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges , 2010, OR Spectr..

[21]  Mosen Asadi,et al.  Beet-Sugar Handbook , 2006 .

[22]  van der,et al.  Sustainable production of food , 2014 .

[23]  Paul van Beek,et al.  An application of mixed-integer linear programming models on the redesign of the supply network of Nutricia Dairy & Drinks Group in Hungary , 2002, OR Spectr..

[24]  Nilay Shah,et al.  Multi-scale process and supply chain modelling: from lignocellulosic feedstock to process and products , 2011, Interface Focus.

[25]  Rafiqul Gani,et al.  Optimal design of a multi-product biorefinery system , 2011, Comput. Chem. Eng..

[26]  Raad Yahya Qassim,et al.  Selection of postharvest technology routes by mixed-integer linear programming , 1997 .

[27]  B Jonathan Moncada,et al.  Design strategies for sustainable biorefineries , 2016 .

[28]  A. Beulens,et al.  Identifying sources of uncertainty to generate supply chain redesign strategies , 2002 .

[29]  Gürkan Sin,et al.  Integrated business and engineering framework for synthesis and design of enterprise-wide processing networks , 2012, Comput. Chem. Eng..

[30]  Remko M. Boom,et al.  Concepts for further sustainable production of foods , 2016 .

[31]  Eleftherios Iakovou,et al.  Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy , 2014 .

[32]  Gonzalo Guillén-Gosálbez,et al.  Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes , 2010, Comput. Chem. Eng..

[33]  J.G.A.J. van der Vorst,et al.  A Sustainability Driven Methodology for Process Synthesis in Agro-Food Industry , 2015 .

[34]  Antonia Tamborrino,et al.  Plant innovation in the olive oil extraction process: A comparison of efficiency and energy consumption between microwave treatment and traditional malaxation of olive pastes , 2015 .

[35]  Mahmoud M. El-Halwagi,et al.  Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives. , 2014 .