Simulation modelling for food supply chain redesign; integrated decision making on product quality,sustainability and logistics

Food supply chains are confronted with increased consumer demands on food quality and sustainability. When redesigning these chains the analysis of food quality change and environmental load of new scenarios is as important as the analysis of efficiency and responsiveness requirements. Simulation tools are often used for supporting decision-making on 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. We propose a new integrated approach towards logistics, sustainability and food quality analysis, and implement the approach by introducing a new simulation environment, ALADIN?. It 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.     

Impact of modelling approximations in supply chain analysis – an experimental study

This paper presents a study of the comparison of the quality of results obtained at different levels of detail using a supply chain simulation. Analysis of supply chain is typically carried out using aggregated information to maintain the level of complexity of the simulation model at a manageable level. Advances in simulation have provided the ability to build comprehensive (detailed), modular models. The quantitative effect of detailed modelling on the corresponding analysis is investigated in this paper. A three-echelon supply chain is analysed using simulation models of varying levels of detail. Using each of these models, four sets of intensive experiments are performed. The first experiment intends to test whether the supply chain dynamics themselves depend on the modelling accuracy that represents the supply chain. The second and third experiments are conducted to test whether the effectiveness of the strategies employed to reduce the supply chain dynamics vary depending on the type (different detail) of model representing the supply chain. In the fourth experiment, statistical techniques are employed to identify which modelling aspect has the most influence on the supply chain dynamics. It is found that the approximations used in modelling, such as delays and capacity, have more impact on the outcome of supply chain analysis than end customer demand. Evidence that both the basic problem (supply chain dynamics) and the solution (strategy to reduce the dynamics) are greatly influenced by the modelling accuracy are presented.     

Modelling a complex supply chain: understanding the effect of simplified assumptions

The benefits of coordinating activities and consolidating distribution points in supply chains are well highlighted and intuitively logical. However, the impact of these decisions on the overall performance of a complex supply chain may not be as obvious as usually perceived. This study models a relatively complex supply chain and evaluates the impact of simplifying demand and lead time assumptions under various supply chain configurations. Of particular interest is the investigation of the effect of risk pooling and the synchronization of production cycles in a multi-level multi-retailer supply chain under the influence of various parameters such as batch size, delivery frequency and ordering cycle. This study highlights the extent of complicated interaction effects among various factors exist in a complex supply chain and shows that that the intricacy of these effects can be better understood with a simulation model.     

An Extended Kalman filter for collaborative supply chains

Supply chains can often be complex due to the large mesh of interconnected suppliers, manufacturers, distributors and customers. Recent advances in communication technologies can help participants collaborate across a supply chain. However, the huge amount of data generated can impede effective decision-making, particularly since some data may be incomplete or have errors. Inaccurate estimates of the state of the supply chain system can lead to incorrect decisions, with consequent adverse effects on product availability, lead times and inventory levels. What would be beneficial in overcoming this problem is an approach to obtain a better state estimation of the supply chain system. The paper aims to address this issue by proposing an approach that combines an extended Kalman filter with a network approach that models the supply chain as an abstraction. This approach is termed Augmented Trans-Nets and has several potential advantages: multiple participants in a supply chain can be modelled without undue complexity; and different considerations can be examined, such as cost and lead time. Furthermore, by using this approach, it is relatively straightforward to achieve an improved system estimation, which can help in managing the supply chain effectively.     

Underestimating the bullwhip effect: a simulation study of the decomposability assumption

We investigate the assumption of decomposability as it pertains to modelling the bullwhip effect in multi-stage supply chains. Decomposing a multi-stage supply chain into a set of node pairs, each of which can be efficiently represented with a two-stage model, is a common modelling technique when analysing the bullwhip effect in supply chains. This approach depends on the validity of the decomposability assumption since most supply chains are coupled systems that are a logical fit for singular, or ‘monolithic’, multi-stage models. We utilise a simulation study to compare decomposition-based supply-chain models with monolithic models and determine if decomposition modelling significantly alters the predicted severity of the bullwhip effect. We find decomposition-based models exhibit a significantly lower level of bullwhip effect than monolithic models of the same supply chain. The systematic underestimation of the bullwhip effect by decomposition-based models indicates that the assumption of decomposability is flawed. Our study also confirms previous work showing the significant benefit of using actual, instead of approximate, lead-time demand information. We discuss implications for supply-chain modelling, supply-chain design, and data collection.     

A decision support framework for global supply chain modelling: an assessment of the impact of demand,supply and lead-time uncertainties on performance

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.     

Performance analysis of conjoined supply chains

This research is concerned with the performance behaviour of conjoined supply chains, which typically arise in web-based retail. In particular, five performance measures belonging to three performance measure classes were used to study the performance effects of various operational factors on conjoined supply chains. The study is accomplished via experimental design and simulation analysis, and the results suggest the effects of the various factors on supply chain performance and identify the nature of the relationships among these factors and overall supply chain performance.     

Safety stock placement problem in capacitated supply chains

Today's highly competitive business environment forces supply chain managers to maintain high service levels while keeping inventory-related costs as low as possible. Therefore, placing the right amount of safety stock at the right places in the supply chain is an important aspect of effective inventory management. This safety stock placement problem, for which some solution strategies have been proposed in the case of uncapacitated supply chains, becomes much more complicated when, in addition to the variability of the demand, capacity constraints also come into play. In this paper we propose a model to locate safety stocks in a capacitated supply chain with the objective of maintaining the required service level. The underlying relationships linking excess capacity, demand variability, and service levels are analysed to gain deeper understanding of the safety stock placement problem in capacitated supply chains. Based on these relationships a solution approach for the problem is proposed and is tested with Monte Carlo simulation.     

Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches

Supply chains are becoming more lengthy and complex due to globalisation and vertical integrations. In this context, adopting proactive approaches is needed for dealing with changing risks and vulnerabilities for securing supply chain systems. Supply chain risks are interlinked and thus, one mitigation strategy can reduce many of other supply chain risks. For example, aggregate or pooling demand reduces forecast risks, capacity risks and inventory risks. Also, some of the risk mitigation strategies have negative influences over certain supply chain risks as adding capacity has a negative influence on capacity risks. Twelve major supply chain risk categories and 21 risk mitigation strategies with typical focus on electronics manufacturing supply chains have been identified. A combination of grey theory and digraph-matrix methodologies has been used for quantifying various supply chain risk mitigation strategies and this approach is not seen in literature till date. The proposed model was also tested taking a case study of an Indian electronics manufacturing company. Obtained results were also subject to sensitivity analysis. The net positive influence values of risk mitigation strategies proposed in this research could effectively be used by top management for ascertaining their risk mitigation strategies for better management of supply chains as a whole.     

A multi-objective optimisation approach for order management: incorporating activity-based costing in supply chains

Most of the supply chain order management decision making models proposed in the literature are based mainly on the material flow and capacity constraints without any consideration to the profitability factor. In this paper, we develop a multi-objective mixed-integer programming (MIP) model which considers profitability in order to effectively manage order acceptance decisions in supply chains, subject to capacity constraints by using activity-based costing (ABC). While there are a numbers of decision-making models in literature integrating ABC in supply chains, this study expands the previous models with a more customer-oriented approach. The proposed model fulfils a desirable amount of orders completely and accepts selective number of orders partially with an objective of minimising the amount of residual capacity and increasing the profitability.     

Supply chain configuration with co-ordinated product,process and logistics decisions: an approach based on Petri nets

Supply chain configuration lends itself to be an effective means to deal with product differentiation and customisation throughout a supply chain network. It essentially entails the instantiation of a generic supply chain network to specific supply chains in accordance with diverse customer requirements. The lynchpin of supply chain configuration lies in the co-ordination of product, process and logistics decisions in relation to a variety of customer orders. This paper aims to provide modelling support to supply chain configuration. The ultimate goal is to assist companies to form appropriate supply chains with the most added-value to customer order fulfillment. A formalism based on coloured Petri nets is developed for configuring supply chains. System models are built upon the coloured Petri nets and used to incorporate product and process concerns into the supply chain configuration process. An industrial case study is reported to illustrate the potential of the coloured Petri net modelling formalism and the built system models for supply chain configuration.     

Structure dynamics control approach to supply chain planning and adaptation

Many of the existing methods and models of supply chain planning and control consider only one supply chain structure and assume it's more or less static in nature. In practice, supply chains have a multi-structural semantics and those multiple interrelated structures (organisational, functional, informational, financial, topological, technological, product, and energy structures) are dynamic and subject to many planned and disturbance-based changes. These changes are tackled by control activities which make planning an adaptive process. Although the supply chain optimal planning domain for static structures has been extensively investigated, the domain of planning and control of supply chains with structure dynamics merits more systematic and critical attention. In this study, an original form of supply chain representation as a dynamic system with changing multi-structural characteristics is developed. The structure dynamics control is a dynamic interpretation of the supply chain (re)synthesis process and aims at both advancing the supply chain (re)planning domain and enlarging the scope of the supply chain analysis domain that is currently rather limited. The developed approach is based on an optimal program control theory, active modelling objects, and structural–mathematical concept blended with mathematical programming. In this paper, we describe its basic features and exemplify possible applications and extensions regarding real planning and control problems faced by international supply chains in recent years.     

On the quantification of operational supply chain resilience

Operational disruptions impact a supply chain’s ability to match supply and demand. To remain competitive, supply chains need to be resilient and thus capable of rapidly and effectively recovering from operational disruptions. Supply chain resilience is inherently multidimensional, as it spans across multiple tiers, and thus is difficult to quantify. Extant research has measured the transient response through a single-dimension or single-organisation as a proxy for operational resilience. Whilst this greatly simplifies the analysis, it is also potentially misleading, as an erroneous selection of metric(s) may lead to an inaccurate evaluation of the transient response. This research extends the understanding of operational resilience via quantitative evaluation of multiple transient response measures across multiple tiers; the objective being to construct a multidimensional, multi-echelon operational supply chain resilience metric. The study utilises disruptions as experimental inputs for a serial supply chain simulation model; results are obtained for individual measurements of the transient response across multiple supply chain tiers. Analysis indicates that individual dimensions of resilience can adequately explain the transient response at the single-firm level, whilst aggregation of multiple resilience dimensions across multiple tiers has greater capacity to holistically capture the performance response to supply chain disruptions.     

多供应链间的Nash和Stackelberg博弈决策模型

以N条包含一个制造商和一个零售商的单链式供应链为研究对象,分别讨论了多供应链间Nash博弈和Stackelberg博弈问题,对N条供应链间博弈均衡解进行了分析和求解,证明了N条供应链博弈下均衡解的存在性和唯一性。在实例分析中,以两条供应链为例,比较了不同博弈框架下定价策略和系统利润的差异。结果表明,对于供应链1来说,链间Nash博弈时的零售商利润、制造商利润和供应链利润均大于链间Stackelberg博弈情形,也即无论从零售商、制造商还是整条供应链的角度,他们均乐于放弃作为领导者的先动优势;对于供应链2来说,情况则相反,也即无论从零售商、制造商还是整条供应链的角度,他们均乐于作为追随者发挥后动优势。     

A multi-agent system for chemical supply chain simulation and management support

Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.     

Synchronized strategy to minimize vehicle dispatching time:a real example of steel industry

Time compression in supply chains is a crucial aspect involved in the integration of warehousing and transport operations in the manufacturing industries.Supply chain flows could be interrupted due to many sources of delays that lead to additional time in dispatching process and reduction in customer service level.The problem considered in this paper consists of long waiting times of loading vehicles inside the plant.This work presents a simulation-based study to minimize vehicle dispatching time in a steel wire plant.Value stream map is developed to present a system perspective of processes involved in the overall supply chain.Process activity mapping is completed to provide a step by step analysis of activities involved in the vehicle dispatch process.A simulation model is developed for the system and a new model is proposed to improve the delivery performance by minimizing vehicles' waiting time.     

Exploring nonlinear supply chains: the dynamics of capacity constraints

While most supply chain models assume linearity, real production and distribution systems often operate in constrained contexts. This article aims to analyse the consequences of capacity limits in the order-up-to replenishment policy with minimum mean squared error forecasting under independently and identically distributed random demand. Our study shows that the impact of this nonlinearity is often significant and should not be ignored. In this regard, we introduce the concept of a settling capacity, which informs when our knowledge from a linear analysis is a reasonable approximation in a nonlinear context. If the available capacity is less than the settling capacity, the nonlinear effects can have a significant impact. We compare the Bullwhip Effect and Fill Rate in constrained contexts to well-established results for linear supply chains. We reveal the capacity limit acts as a production smoothing mechanism, at the expense of increasing inventory variability. We proceed to analyse the economic consequences of the capacity constraint and show that it can actually reduce costs. We provide an approximate solution for determining the optimal capacity depending on the demand, the unit costs and the lead time.     

A synchronized strategy to minimize vehicle dispatching time: a real example of steel industry

Time compression in supply chains is a crucial aspect involved in the integration of warehousing and transport operations in the manufacturing industries. Supply chain flows could be interrupted due to many sources of delays that lead to additional time in dispatching process and reduction in customer service level. The problem considered in this paper consists of long waiting times of loading vehicles inside the plant. This work presents a simulation-based study to minimize vehicle dispatching time in a steel wire plant. Value stream map is developed to present a system perspective of processes involved in the overall supply chain. Process activity mapping is completed to provide a step by step analysis of activities involved in the vehicle dispatch process. A simulation model is developed for the system and a new model is proposed to improve the delivery performance by minimizing vehicles’ waiting time.     

Generalized queueing network analysis of integrated supply chains

Supply chain networks are formed from complex interactions between several companies whose aim is to produce and deliver goods to the customers at specified times and places. Computing the total lead time for customer orders entering such a complex network of companies is an important exercise. In this paper we present analytical models for evaluating the average lead times of make-to-order supply chains. In particular, we illustrate the use of generalized queueing networks to compute the mean and variance of the lead time. We present four interesting examples and develop queueing network models for them. The first two examples consider pipeline supply chains and compute the variance of lead time using queueing network approximations available in the literature. This analysis indicates that for the same percentage increase in variance, an increase at the downstream facility has a far more disastrous effect than the same increase at an upstream facility. Through another example, we illustrate the point that coordinated improvements at all the facilities is important and improvements at individual facilities may not always lead to improvements in the supply chain performance. The existing literature on approximate methods of analysis of forkjoin queueing systems assumes heavy traffic and requires tedious computations. We present here two tractable approximate analytical methods for lead time computation in a class of fork-join queueing systems. Our method is based on the results presented by Clarke in 1961. For the case where the 'joining' servers of the queueing system are of the type D/N/1, we present an easy to use approximate method and illustrate its use in evaluating decisions regarding logistics (for instance, who should own the logistics fleet-the manufacturer or the vendor?) and computing simple upper bounds for delivery reliability, that is the probability that customer desired due dates are met.     

Pricing and ordering decisions of two competing supply chains with different composite policies: a Stackelberg game-theoretic approach

In today’s global highly competitive markets, competition happens among supply chains instead of companies, as the members of supply chains. So, the partners of the chains seek to apply efficient coordinating strategies like discount, return, refund, buyback, or the other coordinating policies to abate the operation costs of the chains and subsequently increase market shares. Hence, because of the importance and application of these strategies in the current non-exclusive markets, in this study, we introduce different composite coordinating strategies to enhance the coordination of the supply chains. Here, we consider two competing supply chains where both chains launch the same product under different brands to the market by applying different composite coordinating strategies. Each supply chain comprises one manufacturer and a group of non-competing retailers where the manufacturer receives raw materials from an outside supplier and transforms them into a finished product; then, the products are sold to the retailers to satisfy the demands of market. In the first chain, a composite (QFF) policy, which is the combination of quantity and freight discount, as well as free shipping quantity policies, are considered between upstream and downstream members while in the second one, different composite polices are considered between upstream and downstream members such that the supplier offers a composite policy, as the first chain, to the manufacturer and the manufacturer proposes a composite (QPR) policy, which is the combination of quantity discount and partial-refund customer return policies, to the retailers. The main objective of the paper is to determine the optimal selling prices and the order quantities of the manufacturer and the retailers in each chain in presence of different composite coordinating strategies. A Stackelberg game-theoretic approach is employed between the members of each chain where the manufacturer is a follower and the retailers are leaders. The concavity of profit functions is proved. Finally, the applicability of the models is justified by presented numerical examples. Moreover, the effects of these strategies on the decisions of the chains’ partners are examined.