A systematic literature review of modelling approaches and implementation of enabling software for supply chain planning in the food industry

Figures & data

Figure 1. Supply chain planning & navigation framework.

Table 1. Common functionalities of APS modules for SCND, S&OP and PP&S.

Module	Functionalities
SCND (Davies et al., 2002)	Determination of product strategy: Includes number and main characteristics of products and markets to be served. Determination of manufacturing strategy: Includes number and location of plants, sourcing strategy, investment decisions and supplier selection. Determination of logistics strategy: Includes number, locations and echelons of distribution centers, sourcing strategy and investment decisions. Determination of procurement strategy: Includes number of suppliers and selection of suppliers. Determination of investment/divestment decisions: Includes in-/outsourcing, acquisitions/mergers and new technology introduction.
S&OP (Ivert & Jonsson, 2010; Wagner, 2002)	Demand planning module comprises: Statistical forecasting: Assist the planner in making estimations derived from historical data. Incorporation of judgmental factors: To correct and improve statistical forecast (e.g. by consensus of experts). Collaborative/consensus-based decision process: Assures that input for the demand planning process can be collected from all involved departments. Accuracy measurement: Accuracy measures such as the Mean Absolute Percentage Error (MAPE), the Mean Absolute Deviation (MAD) or the Mean Squared Error (MSE) can be used to track and evaluate forecast accuracy.Supply network planning module comprises: Creation of unrestricted operations plan: Calculation of net demand considering inventory and comparison of production quantities with available capacities. Bottleneck resolution: In case of bottlenecks, automated generation of a feasible plan (e.g. by building up inventory, using overtime and outsourcing or scheduling additional shifts).
PP&S (Akabuilo et al., 2011; Software Advice, 2022)	Dynamic lot-sizing: Definition of the quantity of an item to manufacture in a single production run. Automated scheduling: Algorithm-based scheduling and sequencing of production orders. Manual scheduling: To correct and improve production schedules by the input of production managers etc. Shop floor control: Comprises methods and systems to prioritize, track, and report against production orders and schedules. Rescheduling of orders: Enabled by drag & drop functionality in an interactive planning board.

Figure 2. Flow diagram of paper selection and inclusion/exclusion process.

Figure 3. Distribution of papers over time.

Figure 4. Contributions classified by author.

Figure 5. Contributions classified by academic journal.

Table 2. Example models for SCND.

Paper	Product	Country	Method
Hosseini-Motlagh et al. (2019)	Wheat	Iran	Stochastic programming
Aras and Bilge (2018)	Snacks	Turkey	MILP
Musavi and Bozorgi-Amiri (2017)	Perishable food	-	MILP
Mohammed and Wang (2017)	Meat	UK	Multi-objective robust possibilistic programming
Colicchia et al. (2016)	Chocolate	Italy	MILP
Khalili-Damghani et al. (2014)	Seafood	Iran	MILP
Reiner and Trcka (2004)	Pasta	-	Simulation
Wouda et al. (2002)	Dairy	Hungary	MILP

Table 3. Example models for S&OP/demand planning/supply network planning.

Paper	Product	Country	Method
Nemati et al. (2017)	Dairy	Iran	MIP
Sel et al. (2015)	Yoghurt	-	MILP & heuristic
Liu and Nagurney (2012)	Perishable food	-	Algorithm
Ahumada and Villalobos (2011)	Bell peppers & vine ripe tomatoes	Mexico	MILP
Rong et al. (2011)	Bell peppers	-	MILP
Higgins et al. (2006)	Sugar	Australia	MILP & heuristics
Takey and Mesquita (2006)	Icecream	Brazil	LP
Ioannou (2005)	Sugar	Greece	LP

Table 4. Example models for PP&S.

Paper	Product	Country	Method
Wari and Zhu (2016)	Icecream	-	MILP
Bilgen and Dogan (2015)	Dairy	-	MILP
Kilic et al. (2013)	Flour	-	MILP
Amorim et al. (2012)	Perishable food	-	MIP & MINLP
Wauters et al. (2012)	-	-	Algorithm
Guan and Philpott (2011)	Dairy	New Zealand	Stochastic quadratic model & algorithm
Doganis and Sarimveis (2008)	Yoghurt	Greece	MILP
Lütke Entrup et al. (2005)	Yoghurt	-	MILP

Table 5. Research papers on APS implementation in the food industry.

Paper	Method	Objective
Jonsson and Ivert (2015)	Survey among Swedish manufacturing companies from different industries (including food & beverage)	Determine the impact of different master production scheduling methods on company performance
Zago and Mesquita (2015)	Case study of a dairy company	Examine the benefits of using an APS module for S&OP and determine success factors for the implementation of an APS module
Ivert et al. (2011)	Three case studies of manufacturing companies (including a food and a brewery company)	Investigate problems encountered in the different phases of implementation projects of software tools to support tactical production planning
Vlckova and Patak (2011)	Interviews with managers from four companies (including one company from the food industry)	Investigate demand planning practices and the use of software to support demand planning
Rudberg and Thulin (2009)	Case study of a company from the farming & food industry	Examine how master planning can be enabled by an APS module
Jonsson et al. (2007)	Three case studies (including two cases from the food industry)	Examine the use and perceived impact of the application of APS modules for strategic network planning and master production scheduling
Brown et al. (2001)	Case study of a company producing cereals and convenience food	Examine the effects of using software supporting tactical and operational SCP

Figure 6. Adapted TAM.

Davies, R., Diepeveen, N., Diks, E., & Vloemans, V. (2002). How to get the most out of your supply chain. In An overview of APS systems in the consumer products manufacturing and process industry. Report of Deloitte Consulting.

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