One vendor-one retailer in vendor managed inventory problem with stochastic demand

One of the basic problems in supply chain operation is lack of information exchanges related to inventory between vendor and retailer. Vendor managed inventory (VMI) provides a good approach to handle this problem. VMI has been proven to reduce cost and improve customer service level. This research aim is to develop a VMI model for the system with one vendor and one retailer to minimise the total system cost. The model is developed for (t, q) policy where the retailer’s cycle time is fixed. Due to the complexity nature of the model, simulation-optimisation using genetic algorithm is employed to determine the decision variables which are the retailer’s lot size, the vendor’s lot size, and the number of replenishments in a vendor cycle. Numerical experiments are conducted to show how the proposed model works. Sensitivity analysis is also conducted to understand the effects of some input parameters

Adoption of web-based order-processing systems: experiences from tier-1 suppliers in the automotive supply chain

This research studies automobile industry suppliers in Thailand, who have implemented VMI using web-based order-processing technology. It examines their motivations and barriers for implementing the technology, and identifies the enabling factors with greatest impact on the outcomes of such implementation. Data was obtained from 70 tier-1 suppliers of the two biggest automobile assemblers in Thailand. Exploratory factor analysis was used to construct motivation, enabling factors, difficulties/barriers and outcomes of web-based order-processing technology implementation. Multiple linear regression was then used to examine the relationships between enabling factors and operational outcomes. The results show that improving customer responsiveness and the need to retain competitive advantage rather than cost-based orientation were the primary drivers of implementation of web-based order processing. This case provides insights to enable successful implementation by organisations that are planning to deploy the technology, particularly in the early stages of technological diffusion in emerging economies

Economic evaluation in decision models: a critical review and methodological propositions

International audienceDecision models of industrial management articles are often based on an economic criterion to find the proposed solution. They use economic parameters that are generally imported from the firm cost accounting system. When cost information is not adapted to the decision, the obtained solution of the model may be invalid. In this article, we deal with a critical literature review to report the methodological problems encountered in industrial management articles vis-à-vis the used costs. Finally we suggest methodological propositions to be kept in mind by authors when they are using costs in decision models

Practices for strategic capacity management in Malaysian manufacturing firms

While the notion of manufacturing capabilities is a long-standing notion in research on operations management, its actual implementation and management has been hardly researched. Five case studies in Malaysia offered the opportunity to examine the practice of manufacturing managers with regard to strategic capability management. The data collection and analysis was structured by using the notion of Strategic Capacity Management. Whereas traditionally literature has demonstrated the beneficial impact of an appropriate manufacturing strategy on the business strategy and performance, the study highlights the difficulty of managers to set the strategy, let alone implementing it. This is partly caused by the immense pressure of customers in these dominantly Make-To-Order environments for SMEs. Current concepts for manufacturing capabilities have insufficiently accounted this phenomenon and an outline of a research agenda is presented

A Flexible Logistics Distribution Hub Model considering Cost Weighted Time

The delivery time of order has become an important fact for customers to evaluate logistics services. Due to the diverse and large quantities of orders in the background of electronic commerce, how to improve the flexibility of distribution hub and reduce the waiting time of customers becomes one of the most challenging questions for logistics companies. With this in mind, this paper proposes a new method of flexibility assessment in distribution hub by introducing cost weighted time (CWT). The advantages of supply hub operation mode in delivery flexibility are verified by the approach: the mode has pooling effects and uniform distribution characteristics; these traits can reduce overlapping delivery time to improve the flexibility in the case of two suppliers. Numerical examples show that the supply hub operation mode is more flexible than decentralized distribution operation mode in multidelivery cycles

VMI-type Supply Chains: a Brief Review

The primary purpose of this paper is to highlight for the research community and practitioners the various aspects of using VMI-type supply chains in today’s business environment as well as a number of directions for future studies. In this regard, fifty articles published in major international journals, beginning in 1995, which contribute to the VMI-type supply chains are reviewed via a systematic review methodology. Our findings show there is an incremental growth in employing of VMI strategies in logistic and supply chains. This paper characterizes the design aspects required to configure and establish a VMI-type supply chain in the industry including demand pattern, number of products, contract type between two parties, and profit sharing scheme. Moreover, the current gaps on the current state of VMI-type supply chain in literature are highlighted in last section of this paper that may motivate future studies

Retail Inventory Control Strategies

Despite using computerized merchandise control systems in retail, the rate of stockouts has remained stagnant. The inability to satisfy customer needs has caused a loss of 4% in potential revenue and resulted in dissatisfied customers. The purpose of this qualitative multiple case study was to explore cost-effective inventory control strategies used by discount retail managers. The conceptual framework that grounded the study was chaos theory, which helped identify why some business leaders rely on forecasting techniques or other cost-effective strategies as an attempt to prevent stockouts. The target population was comprised of discount retail managers located throughout northeast Jacksonville, Florida. Purposeful sampling led to selecting 6 retail managers who successfully demonstrated cost-effective inventory control strategies for mitigating stockouts. Data were collected through face-to-face semistructured interviews, company websites, and company documents. Analysis included using nodes to identify similar words and axial-coding to categorize the nodes into themes. Transcript evaluation, member checking, and methodological triangulation strengthened the credibility of the findings. Five themes emerged: (a) internal stockout reduction strategies, (b) external stockout reduction strategies, (c) replenishment system strategies, (d) inventory optimization strategies, and (e) best practices for inventory control. This study may contribute to positive social change by improving inventory management, which may reduce demand fluctuations in the supply chain and reduce logistics costs in the transportation of freight thereby leading to improved customer satisfaction

An engineering approach to improving hospital supply chains

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 130-132).This thesis explores supply chain management practices that have been implemented, and have improved supply chains in industries outside of healthcare. The presented supply chain practices have been selected because they have the potential to improve efficiency, reduce costs and improve patient safety within hospitals. Due to the diverse nature of hospitals and a unique product profile, there is no "one size fits all" supply chain solution that can be implemented. Therefore, product specific characteristics are discussed that can be used by hospitals in order to develop segmentation policies. Supply chain best practices from outside of the healthcare industry are presented for each category of segmented products. The culmination of this thesis is the presentation of a supply chain that will enable the hospital to significantly reduce inventory storage space, on hand inventory value, and time spent by nurses managing inventory. The proposed supply chain model is patient specific and involves the delivery of items from an offsite warehouse directly to the patient's bedside. In order to successfully implement a new supply chain solution in a hospital setting, change management is a critical part of the process. Methods are presented that have resulted in successful implementations of complex systems within hospitals. Three areas must be considered when managing change in this type of setting, the healthcare environment, the hospital's internal management and operational aspects of the hospital supply chain. Using simulation models, we show that implementation of the proposed supply chain for appropriately segmented products will result in significant supply chain cost savings and boost the revenue.by Scott Hsiang-Jen Cheng and Graham J. Whittemore.M.Eng.in Logistic

ERP Selection at Custom Fabrics: A Teaching Case

As firms try to replace outdated legacy systems and modernize their information systems, they seriously consider implementing Enterprise Resource Planning (ERP) packages. However, the firm selecting an ERP vendor is faced with several difficult issues besides the typical issues of software vendor selection, and the process is fraught with risks. Such issues include: the degree of fit of the package with existing business processes, the possible restructuring of the business, and the degree of business integration. In addition, the typical factors include vendor selection criteria as well as the management of the selection process. In this case, Custom Fabrics wrestled with many of the issues either implicitly or explicitly. The case describes many of the objective and subjective processes Custom Fabrics went through. In spite of much care and attention to the process, what is interesting is that in the end Custom Fabrics reversed its original decision

Data Science in Supply Chain Management: Data-Related Influences on Demand Planning

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and efficient decisions. Yet, the accuracy of these metrics is dependent on the proper specification and parameterization of models and measurements. Thus, demand planners need to step away from a black box approach to supply chain data science. Utilizing paired weekly point-of-sale (POS) and order data collected at retail distribution centers, this dissertation attempts to resolve three conflicts in supply chain data science. First, a hierarchical linear model is used to empirically investigate the conflicting observation of the magnitude and prevalence of demand distortion in supply chains. Results corroborate with the theoretical literature and find that data aggregation obscure the true underlying magnitude of demand distortion while seasonality dampens it. Second, a quasi-experiment in forecasting is performed to analyze the effect of temporal aggregation on forecast accuracy using two different sources of demand signals. Results suggest that while temporal aggregation can be used to mitigate demand distortion\u27s harmful effect on forecast accuracy in lieu of shared downstream demand signal, its overall effect is governed by the autocorrelation factor of the forecast input. Lastly, a demand forecast competition is used to investigate the complex interaction among demand distortion, signal and characteristics on seasonal forecasting model selection as well as accuracy. The third essay finds that demand distortion and demand characteristics are important drivers for both signal and model selection. In particular, contrary to conventional wisdom, the multiplicative seasonal model is often outperformed by the additive model. Altogether, this dissertation advances both theory and practice in data science in supply chain management by peeking into the black box to identify several levers that managers may control to improve demand planning. Having greater awareness over model and parameter specifications offers greater control over their influence on statistical outcomes and data-driven decision