Decision Support Systems for the Food Industry

Applications of Decision Support Systems (DSSs) in the food industry, and in particular the seafood industry, are discussed. The amount of data recorded in the food industry has increased greatly in the last decade, parallel to descending cost of data recording through automatization and computer systems. The data can be used to fulfill the demands of consumers that want information on their food products, such as origin, impact on the environment and more. By using traceability this flow of data can be used for decision support. Many fields within food processing can gain from using DSS. Such fields include for example lowering environmental impact of food processing, safety management, processing management and stock management. Research and development projects that the authors have taken part in and the following implementations of software solutions are discussed and some examples given of practical usage of DSS in the food industry as a result of such work.

[1]  S Benamara,et al.  Numerical simulation of air humidity distribution in a refrigerated truck enclosure. , 2007 .

[2]  A. Hollingsworth,et al.  Increasing retail concentration , 2004 .

[3]  Eldon A. Gunn,et al.  A simulation model for assessing fishing fleet performance under uncertainty , 1990, 1990 Winter Simulation Conference Proceedings.

[4]  Martin Hingley Response to comments on ‘Power to all our Friends? Living with imbalance in supplier–retailer relationships’ ☆ , 2005 .

[5]  Denis Flick,et al.  Analysis of use of insulating pallet covers for shipping heat-sensitive foodstuffs in ambient conditions , 2002 .

[6]  A. M. Foster,et al.  Experimental verification of analytical and CFD predictions of infiltration through cold store entrances , 2003 .

[7]  Gene Fliedner,et al.  CPFR: an emerging supply chain tool , 2003, Ind. Manag. Data Syst..

[8]  Pall Jensson Daily production planning in fish processing firms , 1988 .

[9]  Bart Nicolai,et al.  Analysis of the air flow in a cold store by means of computational fluid dynamics , 2000 .

[10]  James P. Womack,et al.  Lean Thinking: Banish Waste and Create Wealth in Your Corporation , 1996 .

[11]  E. Derens,et al.  Numerical modelling of the temperature increase in frozen food packaged in pallets in the distribution chain , 2000 .

[12]  H. B. Nahora,et al.  CFD model of the airflow , heat and mass transfer in cool stores , 2005 .

[13]  R. Paul Singh,et al.  PREDICTION OF TEMPERATURE IN FROZEN FOODS EXPOSED TO SOLAR RADIATION , 1987 .

[14]  L. Chao,et al.  Structured habitats and the evolution of anticompetitor toxins in bacteria. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Sudhir K. Sastry,et al.  Effect of Packaging Materials on Temperature Fluctuations in Frozen Foods: Mathematical Model and Experimental Studies , 1986 .

[16]  Sabah U. Randhawa,et al.  A decision aid for coordinating fishing and fish processing , 1995 .

[17]  Pall Jensson,et al.  Impact of the cost of the time resource on efficiency of economic processes , 2006, Eur. J. Oper. Res..

[18]  M. Hingley Power to all our friends? Living with imbalance in supplier-retailer relationships , 2005 .

[19]  Halldór Pálsson,et al.  Thermal performance of corrugated plastic boxes and expanded polystyrene boxes , 2009 .

[20]  Hartmut Stadtler,et al.  Supply Chain Management and Advanced Planning , 2000 .

[21]  T. Moe,et al.  Perspectives on traceability in food manufacture , 1998 .

[22]  Vineet Padmanabhan,et al.  Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect" , 1997, Manag. Sci..

[23]  Sue Johnson Supply Chain Management in the Lamb Industry: An Analysis of Opportunities and Limitations , 2005 .

[24]  D. Flick,et al.  Numerical and experimental study of airflow in a typical refrigerated truck configuration loaded with pallets , 2002 .

[25]  Pall Jensson A Simulation Model of the Capelin Fishing in Iceland , 1981 .

[26]  Stephen M. Disney,et al.  Production, Manufacturing and Logistics An integrated production and inventory model to dampen upstream demand variability in the supply chain , 2006 .

[27]  Mb Hasan,et al.  A mixed integer linear program for an integrated fishery , 2006 .

[28]  Johanna Småros Forecasting collaboration in the European grocery sector: Observations from a case study , 2007 .