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The objective of this paper is to investigate the impact of products' characteristics on the performance of three distribution strategies: traditional warehousing (TW), cross-docking pick by line (XDPL) and cross-docking pick by store (XDPS).

Based on a case study of an FMCG “Fast Moving Consumer Goods” company and a major French retailer, we empirically analyse the impact of the products' characteristics on the performance of the three distribution strategies. We consider a three-echelon supply chain composed of one supplier DC, one retailer DC and multiple retailer stores. The inventory at each echelon is controlled according to an order-up-to (OUT) level policy. The demand is forecasted by means of a single exponential smoothing method. A sensitivity analysis is also conducted to analyse the impact of the supply chain parameters on the comparative performance of the strategies when the parameters' values deviate from the empirical base case.

The empirical investigation shows that the use of XDPL results leads to an increase in the supply chain total cost, whereas XDPS reduces the cost. Moreover, we show that for a service-level target, cross-docking strategies should be selected for products with low variability, high shelf space, low value and short lead-time. For an inventory reduction target, these strategies should be selected for products with high demand volume. We also propose a managerial framework for choosing the right strategy for each product.

This paper fills a gap in the literature by presenting empirical results based on a real business case of a multi-echelon supply chain. Both cost and service are used to evaluate the performance of the strategies.

Our work has the limitation to ignore the transportation cost implications when selecting the right distribution strategy. Hence, including such cost in the analysis would constitute an interesting extension of this work. Moreover, our empirical analysis represents a practical rich context that makes the scope for transferability of findings learned from this article substantial. However, for the generalisability of the findings, larger datasets in the retail supply chain would be interesting to consider

This paper seeks to apply a framework of collaborative planning, forecasting and replenishment (CPFR) to develop a procurement model for agricultural products.

Considering the biological nature, seasonality and perishable characteristics of agricultural raw materials and products, the paper revises the CPFR reference model. Then, the paper constructs a n‐tier CPFR procurement model by extending a two‐echelon supply chain to a multi‐echelon supply chain and incorporating upstream suppliers in the supply chain. Moreover, the concept of collaborative transportation management (CTM) is integrated into the n‐tier CPFR procurement model. Finally, a case study is analysed and the efficacy of the proposed model is also validated.

The finding suggests that CPFR approach is applied in the procurement of agricultural products. The case results show that the service level is increased and inventory variance is reduced. The proposed model can thus improve the accuracy of forecasting and reduce inventory losses.

The paper offers a useful insight into procurement of agricultural products. The proposed model is a useful development for the agricultural industry in implementing CPFR in the future.

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

The purpose of this paper is to provide a simulation modelling framework to examine the behaviour of a serial make-to-stock (MTS) manufacturing system under the influence of various uncertainties. Further, the study analyses effect of propagation uncertainties from lower to upper stream of supply chain.

System dynamics modelling approach has been adopted for modelling and analysing the behaviour of a serial MTS manufacturing system under the influence of different uncertainties such as demand, supplier acquisition rate, raw material (RM) supply lead time, processing time and delay due to machine failure. The backup supply strategy has been proposed to mitigate the adverse effect of the RM supply uncertainty.

The effect of variations of various factors on the performance of a MTS manufacturing supply chain in measured through various performance measures like work-in-progress (WIP) inventory, backlog and RM shortage at both manufacturer’s and supplier’s end. The benefit of adopting backup supply strategy under RM supply uncertainty is demonstrated.

This work is limited to analysis of a serial MTS manufacturing system dealing with a single product having two machines only. The study can be easily extended to a more complex system with multiple machines, lines and products.

A simple simulation framework has been proposed to analyse the effect of various uncertainties on the performance of a MTS manufacturing system. The managers can simulate complex systems using simulation approaches to generate if-then scenarios to gain insight into practical problems and formulate strategies to mitigate adverse effect of uncertainties at various level of supply chain.

The study analyses behaviour of MTS manufacturing system under the effect of various uncertainties operating simultaneously in the system. A backup supplier strategy is proposed to improve the service level at the customer’s end through improving service level at the supplier’s end. Similarly, effective strategies can be tested with the proposed simple model to reduce the effect of uncertainty at different levels of the supply chain.

Supply chain management is a major issue in many industries as firms realize the importance of creating an integrated relationship with their suppliers and customers. Managing the supply chain has become a way of improving competitiveness by reducing uncertainty and enhancing customer service. This paper analyzes various issues important to supply chain management and provides broader awareness of supply chain principles and concepts. The role of planning and coordination in complex integrated systems and information technology to synchronize the supply chain is described in a framework that creates the appropriate structure and installs proper controls in the enterprise and other constituents in the chain.

Using a phenomenon known as the bullwhip effect, the authors explore why additional uncertainty in the marketplace can create severe disruptions in global supply chains (GSCs). The purpose of this paper is to analyze related risks in regional vs GSCs during low and high levels of uncertainty. The authors propose and discuss a number of potential implications alongside some tactics that may help mitigate disruptions in some cases before they become terminal problems for the supply chain sustainability.

Monte Carlo simulation is used to generate the conditions of uncertainty and various scenarios that may emerge to challenge GSCs. Vensim software is utilized as a tool for simulation purposes. The authors considered scenarios applicable to manufacturing and retail sectors specifically because of storability property of goods.

Regional supply chains, as opposed to global ones, are more stable and reliable (less risk of disruption) during low and high levels of uncertainty. During uncertain times, upstream suppliers are at greater risk in GSCs. Firms must make strategic decisions that will secure its supply chain functionality and assess the likelihood of such events since many firms entered emerging markets.

Building on internalization theory, it shows that risk and survival are components of decision making that are further complicated by supply chains now operating globally in emerging markets. The paper demonstrates with simulation that GSCs are riskier than regional supply chains in low and high levels of uncertainty, particular as it relates to the bullwhip effect. It also provides recommendations about supply chain restructure and investments in communication improvements to reduce the bullwhip effect in the supply chain.

The purpose of this paper is to develop a framework for mapping the Indian Public Distribution System (PDS) using multi-agent system (MAS). The entire PDS supply chain from purchase to the distribution is mapped in detail by integrating stages of PDS supply chain.

Literature related to PDS, food grain supply chain (FGSC) and MAS is reviewed and critically assessed. Based on this a framework is proposed which will help in improving functioning of PDS.

The PDS has many shortcomings arising from its complex structure and practices which are used to implement it. The authors propose an MAS to model it in which each entity will be modelled as an agent. The authors propose two stages of supply chain. First stage models the processes from procurement to storage of food grain and second stage model the distribution process.

This paper will be of interest to the policy makers and decision makers involved in the PDS by providing the shortfalls in the system and also suggesting a method to model the PDS based on practices of food supply chains.

This paper provides the decision makers in the PDS, a framework to model and assess the entire supply chain. This will help them in effective implementation of the PDS and also improve in the areas of concerns which are pointed the study.

The purpose of this paper is to provide a decision-making tool achieving robust supply flow by incorporating strategic stock and contingent sourcing in mitigating minor and major disruptions.

The authors consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive due to built-in volume flexibility. In order to incorporate the randomness associated with disruptions and the available capacity during response time in the decision-making stage, the authors present a multi-stage robust optimization (RO) model. The design problem is to determine optimal strategic stock level and response speed of volume-flexible back-up supplier in order to achieve a robust supply flow.

The results show that the quality of optimal solution is improved by considering the randomness associated with available capacity. In addition, incorporating congestion effects allows identifying the appropriate level of supply chain responsiveness, thus improving the overall performance.

The novelty of the proposed model is the consideration of both strategic stock and volume flexibility in maintaining a robust supply performance while incorporating response capability and congestion effects.

The purpose of this paper is to propose a novel risk assessment approach that considers the inter‐relationship between supply chain risks and the structure of network at the same time. To reduce the impact of the supply chain risk and enhance the flexibility of transportation route finding during the product delivery, the authors propose a way to model the risk propagation and how to integrate it with the supply chain network using Bayesian Belief Network (BBN). The key risk indicators (KRI) of each vertex and edge of the supply chain network which are measured or computed by the proposed approach can be utilized to develop the optimal transportation route in the execution phase.

BBN is utilized to illustrate the relations among supply chain risks which may take place in a certain vertex. To apply the BBN to the supply chain network, the authors develop the framework to integrate BBN and the supply chain network by using the general functions that describe the characteristics of the risk factors and inter‐relationships between vertices.

By using the proposed risk assessment and dynamic route‐finding approach, it is possible to reduce the unexpected cost from the supply chain risk and overcome the limitations of previous risk management strategies which focus on developing counter plans and assume the independency of supply chain risks.

The proposed approach describes how to develop KRI‐BBN to model the risk propagation and to integrate the KRI‐BBN and supply chain network. The KRIs directly measured or computed by KRI‐BBN in real time can be utilized to alternate supply chain execution plans such as inventory management, demand management and product flow management. Transportation problem considering risk is developed to show how to apply the proposed approach and numerical experiments are conducted to prove the cost effectiveness.

The contribution of this paper lies in the way of developing KRI‐BBN to assess the supply chain risk and modelling of the risk propagation by integrating KRI‐BBN with supply chain network. With the proposed risk assessment approach, it is able to alternate the transportation route to minimize the unexpected cost and transportation cost simultaneously.

During a crisis situation, a poultry supply chain is faced with high variations on fresh chicken meat demand and has therefore to simultaneously manage excessive shelf-life stocks (in case of falling demand) and external purchases due to inventory shortages. In this case, the production plan is often established according to non-accurate sale forecasts which require ongoing adjustment. The paper aims to discuss these issues.

By using system dynamics, the paper developed a model of the French poultry supply chain during a given avian influenza crisis period. The authors compared exponential smoothing forecasting method to a word-of-mouth diffusion model which makes sense in a sanitary crisis context.

An interesting result shows a complex relationship between the sanitary risk (which increases according to the slaughtered chicken's volume and storage time) and the additional external purchases (in case of low production generated by an insufficient forecasting launched 40 days before customer orders).

Additional costs which vary over time are required for further assumptions testing.

The paper proposes to use a forecasting model which is not currently used by the professionals during a sanitary crisis period. This model is able to simulate an internal dissemination of a call for boycott of meat products (cf. negative word-of-mouth spread).

The problem is how to maintain a less risky but significant buffer size to respond to a supply chain coping with both changes in customers’ demand and instability in production capacity.