Simulation modelling for food supply chain redesign*
Food supply chains are confronted with increased consumer demands for food quality and sustainability. When redesigning these chains the analysis of food quality change and environmental load for new scenarios is as important as the analysis of efficiency and responsiveness requirements. Simulation tools are often used to support decision making in the supply chain (re)design when logistic uncertainties are in place building on their inherent modelling flexibility. Mostly the underlying assumption is that product quality is not influenced by or does not influence chain design. Clearly this is not true for food supply chains as quality change is intrinsic to the industry. In this chapter we discuss specific characteristics and modelling requirements of food supply chains. We propose a new integrated approach towards logistics sustainability and food quality analysis and implement the approach by introducing a new simulation environment ALADIN™. This embeds food quality change models and sustainability indicators in discrete event simulation models. A case example illustrates the benefits of its use relating to speed and quality of integrated decision making but also to creativity in terms of alternative solutions
Previous chapters show that the management of food supply chains has become a complex task and that support for decisions is needed. They make clear how, for example, product assortments seem to grow without bound, product life cycles have decreased and consumer demands for product freshness and food safety have increased on the one hand, and have become more unpredictable at the product level on the other hand. This has resulted in a demand for short lead times and high delivery frequencies for small batches. Hence very flexible production organisation and supply chains are required to realize this performance, while keeping costs in line with company standards.
In promoting and building a flexible food supply chain, organizations need to understand the way competition is changing. Future competitiveness will depend on companies’ abilities to join supply chains and effectively participate in them. Effective participation strongly relies on mutual coordination of activities and sharing of information. Ultimately, performance of the food supply chain is measurable via the acceptance of the end product by the consumer. Their acceptance builds on a combination of availability, price, quality and food safety, sustainability and other qualitative attributes. The highest added value for all participants can only be achieved when these aspects are optimized at the chain or network level. This is especially true for realizing a high quality, safe and sustainable product that possesses integrity. In these respects chain performance strongly depends on decisions made by chain actors and their interactions. Coordinated efforts should and can be facilitated by efficient logistics systems and exchange of information between chain/network participants. In particular, it is a great challenge effectively and efficiently to realize the coordination and information processes in chains, in the dynamic institutional context that they are in.
In order effectively and efficiently to develop and assess improvements in the food chain design in order to manage its increased complexity, one needs decision support tools. Typically, such decision support systems build on mathematical tools and simulation software. In this chapter we will focus on simulation modelling to support food supply chain redesign. The specific challenge being addressed here is to embed food quality models and sustainability issues together with logistics processes in discrete event simulation models, in order to facilitate an integrated approach towards logistic, sustainability and product quality analysis of food supply chains. We hypothesize that integrated decision making will result in overall better decisions compared to disciplinary decision making that takes only one of these aspects into account. The key contribution of discrete event simulation lies in its capability to model and trade off elementary uncertainties underlying product quality and chain logistics, as well as their interaction.
This chapter is organized as follows. First, Section 19.2 describes the essential characteristics of a food supply chain network (FSCN), in terms of the parties involved, process and product characteristics and alternative redesign strategies. Section 19.3 discusses the appropriateness of simulation and pitfalls in the modelling process. In Section 19.4, we highlight the requirements of model capabilities that are essential for successful FSCN simulation and we review existing modelling tools. Next, in Section 19.5, we will introduce the main features of a decision support tool specifically designed for modelling food supply chains, called ALADIN™. In Section 19.6, we will present a case study to illustrate the applicability and potential of integrated decision making for FSC redesign. We will summarize our main conclusions and highlight directions for future research in Section 19.7 and conclude with some main sources of further information.
In recent years, western European consumers have become more demanding about food attributes such as quality, integrity, safety, sustainability, diversity and the associated information services. At the same time, companies in the food industry are acting more and more on a global scale. This is reflected by company size, increasing cross-border flows of livestock and food products, and international cooperation and partnerships. Global competition, together with advances in information technology, have stimulated partners in the food industry to pursue a coordinated approach to establishing more effective and efficient supply chains, supply chain management (SCM). In line with the Global Supply Chain Forum (Lambert and Cooper, 2000), we define SCM as the integrated planning, coordination and control of all logistic business processes and activities in the supply chain in order to deliver superior consumer value at less cost to the supply chain as a whole, while satisfying the requirements of other stakeholders (e.g. the government or non-governmental organizations, NGOs) in the wider context of the total supply chain network (van der Vorst and Beulens, 2002). SCM should result in the choice of a supply chain scenario, that is, an internally consistent view on how a supply chain should be configured in terms of the choice of partners from the total supply chain network and the way their mutual activities of supply, production and distribution of goods are coordinated. Clearly, this is not an easy task, because of a great variety of policies, conflicting objectives, and the inherent uncertainty of the business environment (Alfieri and Brandimarte, 1997).
The design of food supply chains (FSCs) is further complicated by an intrinsic focus on product quality (van der Vorst and Beulens, 2002; Luning and Marcelis, 2006) and demand for environmental sustainability (Hagelaar et al., 2004; Srivastava, 2007). The way in which food quality is controlled and guaranteed in the network is of vital importance for chain performance.
Also, apart from being a performance measure of its own, product quality is directly related to other food attributes like integrity and safety. Recently, more attention has been given to sustainability by introducing the notion of ‘green’ SCM, that is, ‘the set of SCM policies held, actions taken, and relationships formed in response to concerns related to the natural environment with regard to the design, acquisition, production, distribution, use, reuse, and disposal of the firm’s goods and services’ (Zsidisin and Siferd, 2001). Within the context of FSCs the sustainability discussion focuses on the reduction of product waste, that is, products that have to be thrown away because the quality is not suitable any more (e.g. van Donselaar et al., 2006), number of miles a product has travelled before it reaches the consumers’ plate (so-called ‘food miles’) and all greenhouse gas emissions related to the business processes in the supply chain network (so-called ‘carbon footprint’) (Edwards-Jones et al., 2008). We conclude that investments in FSC design should not only be aimed at improving logistics performance, but also at the preservation of food quality and environmental sustainability.
The food industry is becoming an interconnected system with a large variety of relationships. This is reflected in the market place by the formation of (virtual) FSCs via alliances, horizontal and vertical cooperation, and forward and backward integration (van der Vorst et al., 2005; see Fig. 19.1). Lazzarini et al. (2001) refer to a ‘netchain’ and define it as ‘a directed network of actors who cooperate to bring a product to customers’. In a FSCN more than one supply chain and more than one business process can be identified, both parallel and sequential in time. As a result, organizations may play different roles in different chain settings and therefore collaborate with differing chain partners, who may be their competitors in other chain settings. We can conclude that supply chain networks are complex systems owing to the presence of multiple (semi)-autonomous organizations, functions and people within a dynamic environment.
Fig. 19.1 Schematic diagram of a food supply chain network (van der Vorst et al., 2005).
A FSCN comprises organizations that are responsible for the production and distribution of vegetable or animal-based products. From a general perspective, we distinguish two main types. The first type is the FSCN for fresh agricultural products (such as fresh vegetables and fruit). In general, these chains may concern growers, auctions, wholesalers, importers and exporters, retailers and speciality shops and their logistics service suppliers. The main processes are the handling, (conditioned) storing, packing, transportation and trading of food products. Basically, all of these stages leave the intrinsic characteristics of the product grown or produced in the countryside unharmed, except for the product quality which depends on the environmental conditions in time. Over time, the product quality can either increase (e.g. ripening of fruits) or decrease, if harvested at a mature stage. The second type is the FSCN for processed food products (such as portioned meats, snacks, desserts, canned food products). In general, these chains comprise growers, importers, food industry (processors), retailers and out-of-home segments and their logistics service suppliers. In these chains, agricultural products are used as raw materials to produce consumer products with higher added value. Sometimes the consumer products are hardly perishable owing to conservation processes. This reduces the complexity of the FSC design significantly and largely eliminates the need for quality change models. This chapter focuses especially on those food products, either fresh or processed, that are subject to notable quality changes over time.
Bourlakis and Weightman (2004), Jongen and Meulenberg (2005) and van der Vorst et al. (2005) discuss a list of specific process and product characteristics of FSCNs that have an impact on the redesign process, including the following:
• keeping quality constraints for raw materials, intermediates and finished products, and quality decay while products pass through the supply chain. As a result there is a chance of product shrinkage and stock-outs in retail outlets when the product’s best-before dates have passed and/or product quality level has declined too much
Owing to these specific characteristics of food products, the partnership thoughts of SCM in FSCs have already received much attention over the past two decades. It is vital for industrial producers to contract suppliers to guarantee the supply of raw materials in terms of the right volume, quality, place and time. Furthermore, they coordinate the timing of the supply of goods with suppliers, to match capacity availability. Actors in FSCNs understand that products are subject to quality decay as they traverse the supply chain, while the degree and speed of decay may be influenced by environmental conditions. For example, exposing a batch of fresh milk, fruit or meat to high temperatures for some time will significantly reduce product keeping quality (shelf life). Supply chain coordination is essential to make appropriate decisions about food conditioning.
The literature suggests several strategic, tactical and operational redesign strategies to improve the efficiency and effectiveness of supply chain processes. An extensive literature review by van der Vorst and Beulens (2002) identifies a generic list of SCM redesign strategies to facilitate the redesign process and attain joint supply chain objectives:
• Redesign the roles and processes performed in the supply chain (e.g. reduce the number of parties involved, reallocate roles such as inventory control and eliminate non-value-adding activities such as stock keeping).
• Create information transparency (e.g. establish an information exchange infrastructure in the supply chain and exchange information on demand/supply/inventory or work-in-process, standardize product coding).
• Coordinate and simplify logistical decisions in the supply chain (e.g. coordinate lot sizes, consolidate goods flows, eliminate human intervention, introduce product standardization and modularization).
The above strategies address the general case of supply chain design. Specifically, for FSC we can add the redesign strategy to alter the time-dependent environmental conditions, under which products are (re)packed (e.g. using modified atmosphere packaging), stored and transported (e.g. using reefer containers), in order to improve food quality. This will result in longer shelf lives and, therefore, provide room for the introduction of innovative logistics concepts. Furthermore, emphasis should be put on redesigning processes in order to reduce greenhouse gas emissions and energy consumption; see Linton et al. (2007) for an overview of this subject.
Before we discuss the specific modelling requirements for FSCNs in Section 19.4, we will first discuss the appropriateness of modelling and simulation for food supply chain design. Furthermore, we will present elementary principles of modelling and steps in the simulation study.
It is rarely feasible to experiment with the actual system, because such an experiment would often be too costly or too disruptive to the system, or because the required system might not even exist. For these reasons, it is usually necessary to build a model as a representation of the real system and to study it as a surrogate for the real system. Pidd (1999) defines a model as follows: ‘A model is an external and explicit representation of part of reality as seen by the people who wish to use that model to understand, to change, to manage, and to control that part of reality in some way or another’. A model is a convenient world in which one can attempt to change things without incurring the possible direct consequences of such action in the real world. In this sense, ‘models become tools for thinking’ (Pidd, 1999).
Law and Kelton (1991) distinguish alternative ways in which a system might be studied (Fig. 19.2). Physical models refer, for example, to cockpit simulators or miniature super tankers in a pool. Mathematical models represent a system in terms of logical and quantitative relationships that are manipulated and changed to see how the model reacts and thus how the actual system would react, if the mathematical model is valid.
Fig. 19.2 Ways of studying a system (adapted from Law and Kelton, 1991).
To study a system of interest, we often have to make a set of assumptions about how it works. These assumptions are used to constitute a model that in turn is used to try to gain some insight into the behaviour of the corresponding system. If the relationships that compose the model are simple enough, it may be possible to use analytical methods (such as algebra, probability theory or linear programming) to obtain exact information about questions of interest. In analytical models the relationships between the elements of the system are expressed through mathematical equations. Silver et al. (1998) state that if mathematical models are to be more useful as aids for managerial decision making, they must be more realistic representations of the problem; in particular, they must permit some of the usual ‘givens’ to be treated as decision variables. Moreover, such models must ultimately be in an operational form such that the user can understand the inherent assumptions, the associated required input data can be realistically obtained and the recommended course of action can be provided within a relatively short period of time. However, most real-world systems, including food supply chains, are too complex to allow for analytical modelling and these models are preferably studied by means of simulation (Law and Kelton, 1991).
Simulation is a powerful tool that is applied frequently. The popularity of simulation has increased with the increase in computer power, development of sophisticated software and decrease in computer costs. This does not mean that simulation is the appropriate tool in each situation (Kettenis and Van der Vorst, 2007).
An advantage of simulation related to experimenting with the real-world system is that the speed of the simulation may be faster than real time. For example to perform a one-day simulation of a post office will take only a few seconds. Another advantage of simulation is in the effort, time and cost involved in studying alternative system designs. Finally, we mention the possibility of visualizing simulation models in terms of the (dynamic) logic adopted and estimated system performance. Typically, visualization may be helpful in verification and validation of models, next to fostering creativity in solution finding and credibility among problem owners (Bell et al., 1999). The disadvantages of simulation may be in expensive and time consuming modelling efforts. Table 19.1 provides an overview of advantages and disadvantages of simulation.
(adapted from Law and Kelton, 2000)
Figure 19.3 shows the steps that will compose a typical, sound simulation study and the relationships between them according to Banks et al. (1996) and Law and Kelton (1991). A simulation study is not a simple sequential process. As the study proceeds and a better understanding of the system of interest is obtained, it is often desirable to go back to a previous step. The first validation step concerns the involvement of people who are intimately familiar with the operations of the actual system. In the second validation step, pilot runs can be used to test the sensitivity of the model’s output to small changes in an input parameter. If the output changes greatly, a better estimate of the input parameter must be obtained. Furthermore, if a system similar to the one of interest currently exists, output data from pilot runs of the simulation model could be compared with those obtained in reality.
Law and Kelton (1991) and Davis (1993) identify a number of generic pitfalls that can prevent successful completion of a simulation study and are – 15 years later – still appropriate. First of all, a set of well-defined objectives and performance measures should be defined at the beginning of the simulation study which suit all parties in the supply chain. Furthermore, key persons should be involved in the project on a regular basis. Next to this, some degree of abstraction is usually necessary. Some parts may be left out of the model completely; others may be aggregated. The summarized characteristics of the aggregated parts must be checked against expert opinion to see if they represent the situation fairly. Finally, data must often come from different disparate locations; to ensure the success of the modelling effort, it is necessary to obtain sufficient commitment of resources to ensure accurate, useful data about each of the links in the supply chain.
In the previous sections we characterized FSCNs in terms of parties involved, processes, products and alternative design strategies, and we discussed the rationale for modelling and simulation processes. Let us now relate these characteristics to requirements to be set for models specifically used for FSC simulation. We distinguish between requirements of simulation modelling that address the general case of SCM and requirements that are specific to the food industry. As far as the general case is concerned, we build on earlier work (van der Zee and van der Vorst, 2005). An overview of these requirements is meant to (1) support a review of current tools for supply chain simulation and (2) structure our discussion of the new tool.
As stated, a typical supply chain involves multiple (semi)-autonomous parties, who may have several, possibly conflicting, objectives. Actions of one actor in the supply chain may influence product and/or process characteristics for the next actor. SCM requires, among others, the alignment of partner strategies and interests, high intensity of information sharing, collaborative planning decisions and shared IT tools. These requirements often represent major hurdles inhibiting the full integration of a logistics chain. Even when there is a strong partnership between logistics nodes, in practice there are potential conflict areas, such as local versus global interests, and a strong reluctance to share common information about production planning and scheduling, such as, for example, inventory and capacity levels (Terzi and Cavalieri, 2004). SCM requires trust and in-depth insight into each other’s processes, which is difficult, since the widely followed competitive model suggests that companies will lose bargaining power and therefore the ability to control profits, as suppliers or customers gain knowledge (Barratt and Oliveira, 2001).
The aforementioned characteristics make clear that active participation and cooperation of all parties are essential ingredients for the effective design of new supply chain network scenarios. This is even more so since the complexity of the system and the solution space in terms of the number of alternative chain scenarios is significant. Involvement is therefore not only a prerequisite for solution acceptance, but also fosters creative minds in finding alternative and possibly better solutions, building on each other’s expertise on specific chain operations. In order to facilitate an active involvement of decision makers in modelling and solution finding, high demands are set on model transparency and completeness. Transparency refers to the insight into model components and their workings, whereas completeness addresses a full overview of design parameters. This leads us to the following requirements for simulation model design (van der Zee and van der Vorst, 2005):
1. Model elements and relationships: Supply chains assume an integrated approach to physical transformation, data processing and decision making. Especially, the allocation of control policies to specific chain members and relationships, such as hierarchy and coordination, deserve explicit attention as decision variables. This requires the explicit notion of actors, roles, control policies, processes and flows in the model.
2. Model dynamics: The control of dynamic effects within the supply chain, as reflected in for example stock levels and lead times, is an important issue given the many parties involved. Therefore, the logistics of control, that is the timing and execution of decision activities, should be explicit. This requires the ability to determine the dynamic system state, calculate the values of multiple performance indicators at all times and, even more important, allocate performance indicators to the relevant supply chain stages.
3. User interface: The active and joint participation of the problem owners, that is, the supply chain partners, in the simulation study is required for two reasons (Hurrion, 1991; McHaney and Cronan, 1998; Bell et al., 1999; Robinson, 2002). First, as a means of creating trust in the solution and among the parties involved, so there is a better chance of acceptance of the outcomes of the study. Second, the quality of the solution may be improved. This refers to model correctness as well as the performance of the chain scenario. Clearly, it is almost impossible for the analyst to have all relevant information on chain dynamics. Therefore, the domain-related contribution of the problem owner in terms of alternative solutions is vital to the success of the project. Given the foreseen role of the problem owners, an explicit choice and representation of decision variables that appeals to their imagination is important. This boils down to visibility and understanding of all supply chain processes in the model, see point 1 above – Model elements and relationships.
4. Ease of modelling scenarios: The execution of ‘what if’ analysis should be transparent, given the complexity of the supply chain, the large number of conceivable scenarios and the wishes and requirements of the problem owners. This concerns both the choice of building blocks and the time required for tailoring, and adapting them to the right format for model adoption. Another demand is model reuse, because of the combination of volatile business environments and the major modelling effort required. Reusable models may help to increase the speed of modelling and analysing alternative scenarios, while reducing costs of decision support.
Next to the general requirements of modelling, additional, more specific, requirements for modelling FSCs should be mentioned. Here we will address the issue of modelling food quality and environmental sustainability, being prime performance indicators for FSCs.
1. Model elements and relationships: Modelling food quality assumes the presence of attributes of model elements that, next to logistics cost and service aspects, express the actual product status on quality. Methods must also be defined for modelling quality decay owing to progress in time and environmental conditions. In turn, attribute values of food and their foreseen behaviour may be an input to dedicated (proactive) control policies for operating the supply chain, being responsive to, for example, the (estimated) best-before date. Clearly, quality preservation is a major issue in FSCN, which can be improved via the use of sophisticated environmental conditioning techniques (in transport and warehousing) and a reduction in lead times. Of course these new techniques and supply chain processes should be evaluated for energy use and environmental load to guarantee sustainability. Model elements should incorporate these specific characteristics of FSCN, especially the keeping quality constraints for products and the occurrence of quality decay while progressing through the supply chain under specific environmental conditions.
2. Model dynamics: Food quality tends to be a continuous variable. Here we consider the process of its decay at discrete moments in time. This assumes an event-related ‘inspection’ of relevant food attributes. Finally, the model should be able to deal with the aspect of uncertainty. FSCNs deal with biological products that are not homogeneous in product quality and yield (see Section 19.2). Control policies in the model should be able to distinguish between batches with different characteristics and make (logistical) decisions based on this information. This will allow for the concept of ‘quality controlled logistics’ (van der Vorst et al., 2007).
Recall that our choice of discrete event simulation is motivated by the type of problem studied, that is the design of FSCs, which are (1) characterized by uncertainties in product quality and logistics as well as their interaction, and (2) evaluated for logistic costs and service, product quality and sustainability.
In the food science literature, much attention has been paid to food quality change modelling and the development of time temperature indicators (TTI) to monitor the temperature conditions of food products individually throughout distribution (Taoukis and Labuza, 1999; Schouten et al., 2002a,b; Tijskens, 2004). Typically, next to biological variations, food quality is determined by time and environmental conditions (such as temperature, humidity and the presence of contaminants), see Fig. 19.4. Environmental conditions may be influenced by, for example, the type of packaging, way of loading and the availability of temperature conditioned transportation means and warehouses. Figure 19.4 shows an idealized pattern for product decay for a particular perishable product. Typically, realistic values, shown in the figure as individual measurements (+, x, o), deviate from this pattern to some extent. This uncertainty follows from, among others, biological variations (see above) and non-homogeneous conditioning. For example, temperature distributions within a batch of food products tend to be nonuniform as it tends to be warmer in the core.
Fig. 19.4 Example of idealized food quality decay as a function of time for alternative temperature conditions for a specific product (+, × and show significant outliers measured in a laboratory test) (Schouten et al., 2002a).
The use of time-dependent quality information in the design of perishable inventory management systems is gaining increasing attention from researchers (e.g. van Donselaar et al., 2006). However, using this information in the design of distribution systems is only sparingly addressed in the literature. We only found one reference in literature; Giannakourou and Taoukis (2003) consider the potential of a TTI-based system for optimization of frozen product distribution and stock management using Monte Carlo simulation techniques. TTI-responses are translated to the level of product deterioration, at any point in the distribution system, which enables the classification of products according to their remaining keeping quality (shelf life). Their results indicate that the number of rejected products in the market can be minimized using a TTI-based management system based on least-shelf-life-first-out (LSFO), in which products with the closest expiration date are advanced first.
Many types of models have been developed to support supply chain design (Min and Zhou, 2002; Gunasekaran, 2004; Meixell and Gargeya, 2005; Kleijnen, 2005). Kleijnen and Smits (2003) distinguish four simulation types for SCM: (1) spreadsheet simulation, (2) system dynamics (SD), (3) discrete event dynamic system simulation (DEDS) and (4) business games. They conclude that the question to be answered determines the simulation type needed; SD provides qualitative insights, whereas DEDS simulation quantifies results and incorporates uncertainties. Games can educate and train users. In many cases, discrete event simulation is a natural approach for supporting supply chain network design, as their complexity obstructs analytical evaluation, see for example Ridall et al. (2000) and Huang et al. (2003). Discrete event simulation tools, however, tend to stress logistics analysis rather than product quality or sustainability.
In the past, many simulation tools for supply chain analysis have been developed. Van der Zee and van der Vorst (2005) present a literature review in which they assess the modelling characteristics of these packages, given the previous requirements of FSC modelling. They conclude that current simulation approaches cannot fully cope with the demands on model and tool design for supply chain analysis. They mention an important shortcoming of available tools concerning the modelling of supply chain decision making. In line with earlier findings in the field of manufacturing (see for example, Mize et al., 1992; and Karacal and Mize, 1996), they conclude that decision makers control rules and their interactions are mostly ‘hidden’. A reason for this may be the analyst’s choice of building blocks, which does not appeal to supply chain partners. Further, control elements may be dispersed throughout the model, being associated with various building blocks or with the time-indexed scheduling of events. Also, they may simply not be visualized. The ‘hiding’ of control is surprising as control structures are intrinsic to supply chains. This implicit modelling harms realism, as well as harming modelling flexibility and modularity. Essentially, the implicit modelling of decision making in simulation analysis can be traced back to the (implicit) reference models underlying simulation tool libraries and the analyst’s activities in model building. As far as the embedding of food quality models in discrete event simulation models is concerned, we could find no examples in literature.
The challenge being addressed in this chapter is to embed food quality models and sustainability issues together with logistics processes in discrete event simulation models, in order to facilitate an integrated approach towards logistic, sustainability and product quality analysis of FSCs. We hypothesize that integrated decision making will result in overall better decisions compared to disciplinary decision making when taking only one of these aspects into account. A key contribution of discrete event simulation lies in its capability to model and trade off elementary uncertainties underlying product quality and chain logistics, as well as their interaction. Our focus in this paper is on exploiting this flexibility by the development of a simulation environment for FSC modelling, rather than specific models, like the aforementioned model by Giannakourou and Taoukis (2003). Typically, such an environment allows a variety of models to be built to evaluate a wide range of FSC issues, such as the incorporation of new chain actors, use of innovative and sustainable transport modes, consolidation practices and concepts like vendor managed inventory, whilst taking relevant uncertainties into account.
The foreseen advantages of the integrated approach would be in the speed and quality of decision making about FSC design. Decision speed may increase as many iterations may be avoided following from the separate consideration of food quality and chain logistics. But, probably more important, the quality of solutions may be improved as more and other innovative scenarios may be tested, following on from a total performance overview. One of those innovative scenarios is, for example, using quality information proactively to direct distribution processes to profitable markets, also called ‘quality controlled logistics’ (see van der Vorst et al., 2007).
Starting from the above observations of the needs, available means and opportunities for FSC design, we propose a new simulation environment, named ALADIN™ (Agro-Logistic Analysis and Design INstrument). ALADIN™ concerns a library of building blocks for simulation modelling and builds on the discrete event simulation tool Enterprise Dynamics™. Next to basic building blocks for modelling FSC infrastructures (producers, distributors etc), and flows of goods, information and so on, its library embeds food quality models. To show the potential of integrated decision making for FSC design, we discuss a case study concerning the import of pineapples from Ghana to the Netherlands.
In this section we introduce the simulation environment ALADIN™. After a general characterization of the tool, we discuss it in some detail being guided by the classification of demands of simulation modelling for FSCs, see Section 19.4.
ALADIN™ is a visual interactive simulation environment building on the logistics suite of the object oriented simulation package Enterprise Dynamics™ (ED). It concerns a library of generic building blocks for modelling FSCs and their behaviour. The choice of underlying concepts is largely based on the modelling framework proposed in our earlier work (van der Zee and van der Vorst, 2005; van der Zee, 2006). Food quality models for a range of fresh products are embedded in this library. They relate food quality to food logistics in terms of time and choice of resources for food production, transportation and storage.
ALADIN™ is based on three key concepts: agents, jobs and flows. Agents represent supply chain network entities (such as planners, retail outlets, producers and distribution systems) as autonomous objects that are assigned decision making intelligence. All chain activities are defined as jobs, including activities related to decision making. Where physical jobs result in goods, control jobs result in job definitions for agents in the controllers’ domain of control. Flow items (also called business entities) constitute the movable objects within a supply chain. We include four types of flow items in the modelling framework: product flows, information flows, resources that facilitate the transformation processes (assignment of capacity) and job definitions. Job definitions specify a job in terms of, for example its input, processing conditions and the agents to whom the resulting output should be sent. By introducing a demand controller in ALADIN™, physical and information and control layers can be separated (Fig. 19.5). In this way, model transparency is increased, as discussed in Section 19.3.
In ALADIN™, specific agents have been developed, see Table 19.2. Supply chain network models are composed of a reusable set of software components (building blocks, called ‘atoms’ in ED) that represent agents (with multiple inputs and outputs), their control policies (e.g. inventory policies, routing policies) and their interaction protocols, that is message types that regulate the flow of information, goods and cash. Besides these supply chain building blocks, ALADIN™’s core consists of quality change models. These models describe quality behaviour, for example botrytis in strawberries or weight loss of bell peppers, under specified conditions (temperature, relative humidity, modified atmosphere, etc). They incorporate parameters that reflect stochastic biological variations in product quality change and are developed by experts in laboratory experiments under controlled conditions (see Schouten et al., 2002a,b).
|Production unit||Food factory or a grower, who produces products with biological variation in quality and quantity (seasonality)|
|Transportation unit||Climate controlled truck or vessel with specific temperature and modified atmosphere settings and related energy use and CO2 emission per unit|
|Storing and distribution unit||Warehouse or retail outlet with specific climate control characteristics and related energy use and CO2 emission per unit|
|Demand unit||Marketplace with demand for products with specific shelf lives, colours, etc|
|Food product||Specific food product (e.g. pepper, cut vegetable) with its specific quality decay model, related to the settings of environmental conditions in time|
|Demand controller||Explicit modelling of information flow and decision-making activity that activates the goods flow|
Alternative designs for perishable product supply chains (see the redesign strategies in Section 19.2.3) can be simulated, visualized and analysed. ALADIN™ adds the indicators of product quality or product freshness (remaining keeping quality and product waste) and energy use and CO2 emissions to classical performance indicators such as transportation costs, stock levels and delivery reliability (e.g. Gunasekaran et al., 2000). In this way, ALADIN™ helps the decision maker to trade off logistics costs and service (product quality, sustainability and availability), when assessing specific (re)designs of the FSC.
Model dynamics is realized by job execution. We capture the dynamic behaviour of the chain processes by modelling the FSC as a network of agents, jobs and flows with precedence relationships; the jobs can be triggered by multiple causes and have outcomes and processing times that depend on the entities processed and available resources. This includes the calculation of (variations in) product quality aspects (such as weight, colour and firmness) related to the specific conditions, to which the products have been exposed, and sustainability indicators.
In our choice of concepts we tried to adopt basic logistic terminology and developed a library of recognizable building blocks, starting from experiences of several industrial projects. This includes an explicit representation of supply chain coordination in terms of decision makers, their activities and their mutual tuning of activities, also see Section 19.4.2.
ALADIN™ has been successfully applied in several case studies in which new supply chain scenarios have been evaluated. For example, we compared alternative distribution systems (e.g. warehousing, cross docking and different transport modes under different environmental conditions) for the export of fresh products such as peppers and tomatoes. Furthermore, new ordering policies for fresh products have been evaluated, in which a balance is sought between stock-outs and product waste (shrinkage) in retail outlets. ALADIN™ visualizes and quantifies the consequences of design choices for the remaining shelf life of the product and the level of environmental load. In order to illustrate the advantages of integral decision making and the capabilities of ALADIN™ in somewhat more detail, we discuss one of the case studies in the next section.
To illustrate the added value of an integrated analysis of alternative FSC designs, we consider a case study concerning the import of pineapples from Ghana to the Netherlands. In this case two import supply chain scenarios have been compared for logistics costs, product quality decay, energy use and CO2 emissions. First we will consider the background of the case and the scenarios that were chosen for further analysis. Next we consider the data collection and modelling process. We conclude with a discussion of the simulation results and a brief evaluation of the contributions made by ALADIN™ in modelling and analysing alternative supply chain scenarios.
The market for fresh pineapple in Europe is increasing; European consumers demand ready-to-eat products with a sweet taste and golden colour. The import of fresh pineapples to the Netherlands predominantly from Ghana, Costa Rica, Ivory Coast and South Africa amounts to about 35 tonnes on a yearly basis, although almost 75% of that is redistributed mainly to Germany and Russia. Pineapples intended for shipping are harvested when green, while those intended for immediate eating are harvested in the semi-ripe state and those intended for canning in the ripe state. Only sound fruit may be approved for transport; pineapples require particular temperature, humidity/moisture and ventilation conditions. Intact pineapples can be kept for several weeks, whereas cut pineapple has a much more restricted shelf life. Based on discussions with two product experts (who have performed studies on the keepability of cut pineapples under specific laboratory conditions, see Tijskens 2004) and participating chain partners, a generic quality decay model was developed in order to estimate the quality decay of cut pineapple (see Fig. 19.6). This model uses the yeast concentration as limiting quality attribute, starting after cutting the whole pineapple.
It can be seen that the keepability of cut pineapple varies from 6 to 9 days at a fixed temperature of 4 °C. This is a result of biological variation in the initial quality of the product. Biological variation within the same batch causes differences in the initial quality of cut pineapple, such that different packages of cut pineapple may have a different pattern of quality decay at the same temperature. Each package of cut pineapple is provided with a guaranteed best-before date (BBD) at a maximum storage temperature of 4 °C, which is equal to ‘the current date + 6 days’.
Many fresh pineapples reach the Netherlands by costly air transport. This is motivated by the fact that, so far, alternative ways of transportation, like over sea, have resulted in significant quality decay and product shrinkage, owing to lengthy transportation times. Major developments in quality preservation via the use of modified atmosphere packaging and sophisticated chilling techniques, however, challenge Dutch importers and retailers to reconsider their means of transportation. Could transport by sea now be an option, to reduce overall chain costs? A project group including all supply chain members identified several alternative FSC designs of which we will discuss two for illustrative purposes. These scenarios are (see Fig. 19.7):
To measure the effectiveness and efficiency of alternative designs, the project team formulated three key performance indicators for this FSC: the distribution costs along the supply chain (we only focus on transport and warehousing and leave out the costs of the cutting process), the energy and emissions during distribution (regarding emissions only CO2 emissions are considered, where 73 g CO2 is calculated per MJ direct energy use) and the product quality when arriving at the retail store. This last factor is measured by three sub-indicators:
• the remaining keepability of the product at a storage temperature of 4 °C according to the expert model in Fig. 19.7. In other words, for how long will the yeast concentration still be acceptable?
In order to be able to model the scenarios, chain data was collected using document analyses and experts interviews (see Tables 19.3 and 19.4). Table 19.3 shows all distribution activities (transport and storage) from harvest to retail outlet for the air chain. For each activity data are collected about the duration, temperature, cost, direct energy use and emissions. Each supplied batch triggers the activities represented in the tables. Table 19.4 presents data for the sea chain as far as they are different from those of the air chain.
*Retail outlets are ordering every day, while produce is supplied only once per three days. This implies that some pineapples are stored for a longer time than others.
At the time of the research there were six flights per week from Ghana, each distributing 160 kg of cut pineapples. By sea transport, only two shipments per week were taking place, each distributing 1200 kg intact pineapples. Note that 2.5 kg intact pineapple gives about 1 kg cut pineapple. Therefore, both scenarios are comparable in volume, because in each scenario 960 kg cut pineapple is supplied to the retailer.
Evaluating the two scenarios on the defined performance indicators required modelling and analysis of several supply chain scenarios. We did so using ALADIN™. Note that in the project much more complex scenarios were evaluated; the two presented here are just for illustration purposes.
We modelled the supply chain, applying some of the reusable building blocks and designed scenarios by setting the model elements, for example, applying air transport versus transport by ship. Alternative designs of the product supply chains were simulated, visualized and analysed. By changing the environmental conditions to which the pineapples are exposed (e.g. by using new packaging materials or conditioned reefer containers) we could simulate the impact of changes in the distribution system on keeping quality and sustainability indicators. Applying new logistical concepts changes the control and product flows which have an impact on costs and, via a change in the duration or processes, changes the keeping quality of the pineapples and the environmental load.
Table 19.5 presents an overview of the main model outcomes based on the data and assumptions described before. It shows that from a cost and sustainability perspective, the sea chain provides the best results, when looking at product quality the air chain performs slightly better. Note that the interpretation and weighting of the outcomes of the study is left to the decision makers. Simulation will not provide this answer. Further decision support may come from alternative techniques, like multi criteria analysis (see, for example, Quariguasi Frota Neto et al., 2008). We do not discuss these techniques here.
When we look closer at the simulation results of the air chain, the following issues come to the prominence. Air transport is responsible for over 70% of all logistic costs. Energy use happens mostly during air transport (85%), but the open truck from harvest to producer also uses a lot of energy (10%). Looking at remaining keepability (according to the expert model) at the moment the products arrive at the retail outlet, the average is below 5 days, with a variation from less than 3.5 days to more than 5.5 days. The average remaining selling time (according to the BBD) at the time of arrival at the retail outlet is equal to 3.9 days. The 6 days cutting BBD seems to be realistic for this chain. A rather small percentage of all products − 5.9% on average, according to the expert model – has a keepability that is less than the remaining selling time according to the BBD. Typically, they reflect products with a bad initial quality.
Results for the sea chain indicate that sea transport is responsible for almost 60% of all logistic costs. Energy use occurs mostly during transport from grower to sea port (over 50%). At the moment the products arrive at the retail outlet, the average remaining keepability at 4 °C (according to the expert model) is about 4 days, with a variation from less than 3.5 days to more than 4.5 days. Setting the BBD at 6 days after cutting is not realistic in this case. Five days after cutting 10.6% of all products has a keepability which is less than the BBD-code indicates (based on 6 days).
Let us now address the role of ALADIN™ in FSC design, building on the experiences of the case study. First, the integration of facilities for modelling product quality, sustainability and product logistics provides an improved means of analysing FSCs. Instead of studying effects of alternative scenarios on product quality, sustainability and logistics using separate tools, a single tool suffices. More effective solutions may result from this approach, as interaction effects for logistics and product quality may be studied. The speed and quality of decision making clearly benefited from the integrated approach. Furthermore, the definition of model building blocks and relationships in line with the modelling framework proposed by van der Zee and van der Vorst (2005) and van der Zee (2006), resulted in transparent models. In this way they contributed to the communicative value of visual simulation models. A final remark concerns the need for screening candidate solutions. Typically, an FSC allows for configuring a multitude of alternative supply chain configurations. Using simulation for modelling all configurations may simply be too time consuming. We therefore advocate the use of a screening procedure for preselecting alternative configurations. This may include, for example, deterministic models, or expert consultation.
This chapter has dealt with modelling and simulation of food supply chain scenarios to facilitate redesign projects. The challenge addressed is to embed food quality models and sustainability indicators in discrete event simulation models, in order to facilitate an integrated approach towards logistic, sustainability and product quality analysis of FSCs. By introducing a new discrete event simulation tool, ALADIN™, which answers this challenge, we aim to provide a new and improved means of analysing and redesigning FSCs. Its core consists of the combination of reusable process building blocks and quality decay models that facilitate the modelling of FSCs. As such, it contributes to improved decision making with respect to FSC design. Specific strengths of the tool relate to:
• The integration of logistics, quality decay and sustainability modelling: The presence of these models makes it possible to use simulation in workshop settings as a transparent tool for trading off FSC performance with respect to all respective elements.
• The explicit modelling of control structures, building on an explicit modelling framework: Rather than relying on the implicit mental reference models of the analyst and the availability of standard building blocks in the library of Enterprise Dynamics™, new building blocks were developed in ALADIN™ to offer the analyst guidance in modelling specific FSCs. This provides communication via an explicit and well-defined notion of concepts, and helps to reduce the modelling efforts of the analyst, because of the possibilities for reuse of model classes, i.e. agents, flow items and jobs.
• The capabilities for more effective and efficient decision support of FSC design: The case example of a pineapple supply chain showed that the tool provides an integrated means for participants to generate transparency in the supply chain network and jointly develop and evaluate innovative supply chain scenarios.
Future research will focus on the further development of ALADIN™, extending the complexity of quality decay models and quality interaction effects of multiple products distributed together. Furthermore, in line with van der Zee and Slomp (2009) and van der Zee (2007) we are researching a promising possibility for using ALADIN™ as a basis for a simulation gaming and training tool. Such a tool should enable managers jointly to evaluate alternative decision making scenarios in FSCs using multiple performance indicators, such as costs, product quality and sustainability.
Simulation literature may be classified according to three perspectives: model building and coding for simulation, statistics for simulation and doing projects using simulation. Typically, the main focus of (course) books is tailored to one of these angles. Most books on model building and coding start by assuming the use of a specific simulation language. As a simulation language mostly sets its own standards on modelling and features, it is necessary to consider the choice of language prior to choosing a book, examples include Rosetti (2010), Al-Aomar et al. (2009) and Hauge and Paige (2004). A main entry for statistics of simulation is the work by Law and Kelton (2000) and Law (2007). It addresses all the main issues and supplies adequate references to address more specific questions. Whereas previous work tends to be of a rather technical nature, addressing model building and statistics, the work by Robinson (2004) starts from a project view of simulation use. Being non-software specific, it guides the reader through all the main stages in the simulation study by giving details of questions to be addressed and indicating ways of answering them. Further, we mention the book by Chung (2003). The case studies that are part of this work may be particularly helpful for projects in practice.
We thank Taylor and Francis for allowing us to use our article ‘Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics’, which was published in the International Journal of Production Research (van der Vorst, Tromp and van der Zee, 2009).
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*This chapter is largely based on an original article published in IJPR: van der Vorst, Tromp and van der Zee (2009), ‘Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics,’ International Journal of Production Research, 47:23, 6611–6631.