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The purpose of this paper is to understand the effect of batching on bullwhip effect in a model of multi‐echelon supply chain with information sharing.

The model uses the system dynamics and control theoretic concepts of variables, flows and feedback processes and is implemented using iThink^® software.

It has been seen that the relationship between batch size and demand amplification is non‐monotonic. Large batch sizes, that when combined in integer multiples can produce order rates that are close to the actual demand, produce little demand amplification, i.e. it is the size of the remainder of the quotient that is the determinant. It is further noted that the value of information sharing is greatest for smaller batch sizes, for which there is a much greater improvement in the amplification ratio.

Batching is associated with the inventory holding and backlog cost. Therefore, future work should investigate the cost implications of order batching in multi‐echelon supply chains.

This is a contribution to the continuing research into the bullwhip effect, giving supply chain operations managers and designers a practical way into controlling the bullwhip produced by batching across multi‐echelon supply chains.

Previous similar studies have used control theoretic techniques and it has been pointed out that control theorists are unable to solve the lot sizing problem. Therefore, system dynamic simulation has been applied to investigate the impact of various batch sizes on bullwhip effect.

The purpose of this paper is to analyze the effect of batching on bullwhip effect in a model of multi‐echelon supply chain with information sharing.

The model uses the system dynamics and control theoretic concepts of variables, flows, and feedback processes and is implemented using iThink^® software.

It has been seen that the relationship between batch size and demand amplification is non‐monotonic. Large batch sizes, when combined in integer multiples, can produce order rates that are close to the actual demand and produce little demand amplification, i.e. it is the size of the remainder of the quotient that is the determinant. It is further noted that the value of information sharing is greatest for smaller batch sizes, for which there is a much greater improvement in the amplification ratio.

Batching is associated with the inventory holding and backlog cost. Therefore, future work should investigate the cost implications of order batching in multi‐echelon supply chains.

This is a contribution to the continuing research into the bullwhip effect, giving supply chain operations managers and designers a practical way into controlling the bullwhip produced by batching across multi‐echelon supply chains. Economies of scale processes usually favor large batch sizes. Reducing batch size in order to reduce the demand amplification is not a good solution.

Previous similar studies have used control theoretic techniques and it has been pointed out that control theorists are unable to solve the lot sizing problem. Therefore, system dynamic simulation is then applied to investigate the impact of various batch sizes on bullwhip effect.

Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.

The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.

A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.

Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

The aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.

Two mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.

The results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.

Although integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

The purpose of this paper is to determine whether man-hour efficiency of picking is affected by the use of batch preparation, compared to preparation of one kit at a time. This paper focuses on small kit preparation areas.

This paper is based on two experiments that were performed at a vehicle assembly plant and then analysed quantitatively.

The results provide a strong indication of the advantages associated with batch preparation, in terms of man-hour efficiency.

The fact that the effects identified during the experiments are substantial, over 20 per cent reduction of average time per picked component in Experiment 1 and 7 per cent in Experiment 2, indicates that the option of batch picking holds potentials for large cost reduction and should be considered when kit preparation systems are designed.

Limited research has dealt with the design of kit preparation systems, thus leaving considerable knowledge gaps. Previous research dealing with batch picking focuses on other environments than kitting and on large picking areas where batching can reduce walking distances. In contrast, the current paper focuses on small picking areas, which are common in industrial kitting applications. This paper provides a considerable contribution by demonstrating improvements in time efficiency that batch preparation can offer to small picking areas in addition to larger areas. The discussion also provides a basis for future research, which could focus on aspects other than time efficiency, such as the quality of kit preparation, and variables that might moderate the effect of batching.

This paper identifies, configures and analyses a solution aimed at increasing the efficiency of in-store picking for e-grocers and combining the traditional store-based option with a warehouse-based logic (creating a back area dedicated to the most required online items).

The adopted methodology is a multi-method approach combining analytical modelling and interviews with practitioners. Interviews were performed with managers, whose collaboration allowed the development and application of an empirically-grounded model, aimed to estimate the performances of the proposed picking solution in its different configurations. Various scenarios are modelled and different policies are evaluated.

The proposed solution entails time benefits compared to traditional store-based picking for three main reasons: lower travel time (due to the absence of offline customers), lower retrieval time (tied to the more efficient product allocation in the back) and lower time to manage stock-outs (since there are no missing items in the back). Considering the batching policies, order picking is always outperformed by batch and zone picking, as they allow for the reduction of the average travelled distance per order. Conversely, zone picking is more efficient than batch picking when demand volumes are high.

From an academic perspective, this work proposes a picking solution that combines the store-based and warehouse-based logics (traditionally seen as opposite/alternative choices). From a managerial perspective, it may support the definition of the picking process for traditional grocers that are offering – or aim to offer – e-commerce services to their customers.

Shukla et al. (2012) proposed a signature and index to detect and measure rogue seasonality in supply chains, but which, however, were not effectively validated. The authors have sought to investigate rogue seasonality using control theory and realistic multi echelon systems and rigorously validate these measures, so as to enable their application in practice. The paper aims to discuss these issues.

Frequency domain analysis of single echelon and simulated four echelon Beer game system outputs are used in the investigation, with the simulation incorporating realistic features such as non-linearities from backlogs and batching, hybrid make to order-make to stock ordering system and the shipment variable. Lead time, demand process parameters, ordering parameters and batch size are varied in the simulation to rigorously assess the validity of the index.

The signature based on the cluster profiles of variables, specifically whether the variables cluster together with or away from exogenous demand, was validated. However, a threshold for the proportion of variables that could be clustered with exogenous demand and the system still being classified as exhibiting rogue seasonality, would require to be specified. The index, which is derived by quantifying the cluster profile relationships, was found to be a valid and robust indicator of the intensity of rogue seasonality, and which did not need any adjustments of the kind discussed for the signature. The greater effectiveness of the frequency domain in comparison to time for deriving the signature and index was demonstrated.

This work enables speedy assessment of rogue seasonality in supply chains which in turn ensures appropriate and timely action to minimize its adverse consequences.

Detailed and specific investigation on rogue seasonality using control theory and Beer game simulation and rigorous validation of the signature and index using these methods.

This study aims to propose a new genetic algorithm for solving supply chain scheduling and routing problem in a multi-site manufacturing system. The main research question is: How is the production and transportation scheduled in a multi-site manufacturer? Also the sub-questions are: How is the order assigned to the suppliers? What is the production sequence of the assigned orders to a supplier? How is the order assignment to the vehicles? What are the vehicles routes to convey the orders from the suppliers to the manufacturing centers? The authors’ contributions in this paper are: integration of production scheduling and vehicle routing in multi-site manufacturing supply chain and proposing a new genetic algorithm inspired from the role model concept in sociology.

Considering shared transportation system in production scheduling of a multi-site manufacturer is investigated in this paper. Initially, a mathematical model for the problem is presented. Afterwards, a new genetic algorithm based on the reference group concept in sociology, named Reference Group Genetic Algorithm (RGGA) is introduced for solving the problem. The comparison between RGGA and a developed algorithm of literature closest problem, demonstrates a better performance of RGGA. This comparison is drawn based on many test problems. Moreover, the superiority of RGGA is certificated by comparing it to the optimum solution in the small size problems. Finally, the authors use real data collected from a drug manufacturer in Iran to test the performance of the algorithm. The results show the better performance of RGGA in comparison with obtained outputs from the real case.

The authors presented the mathematical model of the problem and introduced a new genetic algorithm based on the “reference group” concept in sociology. Robert K. Merton is a sociologist who presented the concept of reference groups in society. He believed that some people in each society such as heroes or entertainment artists affect other people. The proposed algorithm uses the reference group concept to the genetic algorithm, namely, RGGA. The comparison of the proposed algorithm with DGA and the optimum solution shows the superiority of RGGA. Finally, the authors implement the algorithm in a real case of drug manufacturing and the results show that the authors’ algorithm gives better outputs than obtained outputs from the real case.

One of the major objectives of supply chains is to create a competitive advantage for the final product. This intension is only achieved when each and every element of the supply chain considers customers’ needs in every function of theirs. This paper studies scheduling in the supply chain of a multi-site manufacturing system. It is assumed that some suppliers produce raw material or initial parts and convey them by a fleet of vehicles to a multi-site manufacturer.

This paper seeks to construct a model for inventory management for multiple periods. The model considers not only the usual parameters, but also price quantity discount, storage and batch size constraints.

Mixed 0‐1 integer programming is applied to solve the multi‐period inventory problem and to determine an appropriate inventory level for each period. The total cost of materials in the system is minimized and the optimal purchase amount in each period is determined.

The proposed model is applied in colour filter inventory management in thin film transistor‐liquid crystal display (TFT‐LCD) manufacturing because colour filter replenishment has the characteristics of price quantity discount, large product size, batch‐sized purchase and forbidden shortage in the plant. Sensitivity analysis of major parameters of the model is also performed to depict the effects of these parameters on the solutions.

The proposed model can be tailored and applied to other inventory management problems.

Although many mathematical models are available for inventory management, this study considers some special characteristics that might be present in real practice. TFT‐LCD manufacturing is one of the most prosperous industries in Taiwan, and colour‐filter inventory management is essential for TFT‐LCD manufacturers for achieving competitive edge. The proposed model in this study can be applied to fulfil the goal.