Search results

Fashion retail companies typically exhibit short life-cycles, high volatility and low predictability. Therefore, their success is largely determined by the organisation’s flexibility and responsiveness. The purpose of this paper is to present a methodology to facilitate inventory control to minimise both shortages and excess inventory for a multi-product, multi-period finite time horizon inventory problem by using statistical and stochastic analysis.

The proposed methodology operates in two phases: the first phase consists on determining an aggregate plan (AP) that will be used for monitoring the behaviour of the items during the time horizon. This plan is obtained by statistically analysing historical data related to sales and inventory shortages and is used to determine a demand forecast during the time horizon that allows to handle with potential disruptions derived from real demand variations. Finally, supply replenishment policies are defined to facilitate the monitoring process during the second phase. For the second phase, the behaviour of real demand for every item is captured into a database and compared against its projected demand (from the AP). If needed, adjustments are made in the procurement of future deliveries to reduce the probability of having shortages and/or excess inventory.

A case study in a Mexican fashion retail company was conducted to assess the performance of the methodology. Results indicate that shortage in early periods can be reduced totally for certain products while, for others, the reduction is about 90.5 per cent. In addition, the incomes of the company were increased over 57 per cent.

Even when the success of the methodology has been shown, cultural and behavioural factors were not considered. An extensive study is suggested to determine if these factors should be included to enhance the performance of the methodology.

A case study of a Mexican fashion retail company was conducted to assess the performance of the proposed methodology. The methodology is easy to implement and effectively and quickly responds to disruptions in the demand and it also significantly reduces the level of shortages while increasing sales and revenue for the company.

This paper proposes a methodology that is able to anticipate product’s behaviour from early weeks. Additionally, replenishment policies allow to quickly adjust future orders to guarantee the availability of items and minimise overstock.

The purpose of this paper is to investigate the opaque inventory information disclosure strategy for an online retailer who sells two substitutable products to customers in two selling periods.

The authors develop a two-period model where an online retailer sells two substitute products with two inventory composition structures to maximize profits. The authors investigate the optimal inventory disclosure decision from both ex post and ex ante perspectives. Sensitivity analysis is performed to investigate the effects that discount rate, transaction cost and the probability of agreeable inventory situation have on the equilibrium disclosure outcome. The authors also consider risk-averse customers and horizontally differentiated products to highlight the robustness of our results.

The authors find that the online retailer will choose the opaque information disclosure when attempting to increase revenue and reduce the mismatch of supply and demand in both ex post and ex ante inventory information conditions. Comparing with ex post disclosure strategies, ex ante opaque disclosure is optimal in a larger price region, and the total revenues gap between opaque disclosure and complete disclosure gradually increase as discount rate, transaction cost or the probability of agreeable inventory situation decreases. Furthermore, strategic customers may tend to be risk neutral when faced with opaque inventory information in a two-period sales setting.

This current paper is the first paper to study the online retailer's inventory information disclosure strategy in two selling periods. Moreover, this paper presents the conditions under which the online retailer should share complete or opaque inventory information with customers to maximize the online retailer's total revenues.

There is usually a considerable shortage of resources and a lack of accurate data about the demand amount in a post-disaster situation. This paper aims to model the distribution and redistribution of relief items. When the new data on demand and resources become available the redistribution of previously delivered items may be necessary due to severe shortages in some locations and surplus inventory in other areas.

The presented model includes a vehicle routing problem in the first period and some network flow structures for succeeding periods of each run. Thereby, it can produce itineraries and loading plans for each vehicle in all periods when it is run in a rolling horizon manner. The fairness in distribution is sought by minimizing the maximum shortage of commodities among the affected areas while considering operational costs. Besides, equity of welfare in different periods is taken into account.

The proposed model is evaluated by a realistic case study. The results show that redistribution and multi-period planning can improve efficiency and fairness in supply after the occurrence of a disaster.

This paper proposes an operational model for distribution and redistribution of relief items considering the differences of items characteristics. The model integrates two well-known structures, vehicle routing problem with pickup and delivery and network flow problem to take their advantages. To get more practical results, the model relaxes some simplifying assumptions commonly used in disaster relief studies. Furthermore, the model is used in a realistic case study.

The purpose of this paper is to manage the demand uncertainty considered as lower and upper levels for a medium-scale industrial distribution planning problem in a biobjective inventory routing problem (IRP). In order to achieve this, the grey system theory is applied since no statistical distribution from the past data and incomplete information.

This study is investigated with optimizing the distribution plan, which involves 30 customers of 12 periods in a manufacturing company under demand uncertainty that is considered as lower and upper levels for a biobjective IRP with using grey demand parameters as a grey integer programming model. In the data set, there are also some missing demand values for the customers. So, the seven different grey models are developed to eliminat the effects on demand uncertainties in computational analysis using a piece of developed software considering the logistical performance indicators such as total deliveries, total cost, the total number of tours, distribution capacity, average remaining capacity and solution time.

Results show that comparing the grey models, the cost per unit and the maximum number of vehicle parameters are also calculated as the new key performance indicator, and then results were ranked and evaluated in detail. Another important finding is the demand uncertainties can be managed with a new approach via logistics performance indicators using alternative solutions.

The results enable logistics managers to understand the importance of demand uncertainties if more reliable decisions are wanted to make with obtaining a proper distribution plan for effective use of their expectations about the success factors in logistics management.

The study is the first in terms of the application of grey models in a biobjective IRP with using interval grey demand data. Successful implementation of the grey approaches allows obtaining a more reliable distribution plan. In addition, this paper also offers a new key performance indicator for the final decision.

The development of a decision support tool for the single‐period inventory problem is presented. The support tool allows a consideration of the following factors: empirical frequency distributions, theoretical probability distribution functions and managerial probability estimates of total demand over the period; piece‐wise linear (possibly discontinuous) cost functions. Such functions allow for the possibility of “fixed cost” elements and/or “economies/diseconomies” of scale and account for most, if not all, of the purchase cost, holding cost, salvage revenue and shortage cost functions that arise in practice; choice of performance measures; and “what‐if” analysis on the problem parameters. The support tool, which uses the Monte Carlo simulation option of Visual IFPS/Plus, is transparent and constructively simple and thus readily facilitates understanding, acceptance and implementation by management. The support tool runs under the Windows operating system and has easy access to appropriate spreadsheet and word‐processing packages for further processing and report presentation.

This paper aims to introduce, conceptualize and demonstrate a software tool named “Blockchain-Enabled Beer Game” (BEBG) for familiarizing the application of blockchain in inventory management, one of the critical components of supply chain management.

This paper follows a methodology of design-based research and develops a software tool in the form of a role-play simulation game. The proposed game adopts the theme of the traditional beer distribution game to establish a blockchain-enabled scenario for inventory management. A decentralized application (DApp) was prototyped on the Ethereum blockchain to demonstrate the tool.

The proposed software tool is effective in teaching and training the application of blockchain in inventory management. While interacting with BEBG, players witness how each inventory-related transaction gets secured with blockchain. A basic understanding of the fundamentals of blockchain is a prerequisite for using this tool. BEBG is not self-explanatory, and an instructor is essential for assisting the players.

Software tools currently available to familiarize with blockchain technology cannot convey its practical applications. Addressing this gap, BEBG allows the users to experience the application of blockchain in inventory management. Academic institutions, especially business schools, can use this tool to teach the students the practical use of blockchain technology. Industries can adopt BEBG for training the employees. The research community can devise BEBG to infer the impact of blockchain in supply chain management.

This paper seeks to examine key factors within the control of store managers to optimizing inventory and store results.

This research integrates principles of action research and traditional research in a big box retail environment.

While this study confirms theories that link inventory to sales, merchandise selection, and technology, it emphasizes the role of people. Furthermore, it proves that different stores within same companies and different departments within same stores deliver different results due, mainly, to human factors – specifically, critical thinking, functional knowledge, and leadership.

This study does not address inventory assortment, space allocation, automatic replenishment, planograms design, technology, logistics, and other factors that may impact inventory but mostly outside the control of big box store managers.

This study proposes practical tools and ideas to optimizing inventory and business results in big‐box stores. It also serves as an example of extracting and verifying retail management theory from practice. As such, it benefits both practitioners and academics.

Innovative startups have begun a trend using laser sintering (LS) technology patents expiration, namely, by introducing LS additive manufacturing (AM) machines that can overcome utilization barriers, such as the costliness of machines and productivity limitation. The recent rise of this trend has led the authors to investigate this new class of machines in novel settings, including hub configuration. There are various supply chain configurations to supply spare parts in industrial operations. This paper aims to explore the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it.

This study quantitatively examines the feasibility of different AM-enabled spare parts supply chain configurations. Using cost data extracted from a case study, three scenarios per AM machine technology are modeled and compared.

Results suggest that hub production configuration depending on the utilized AM machines can provide economic efficiency and effectiveness to reduce equipment downtime. While previous studies have suggested the need for AM machines with efficiency for single part production for a distributed supply chain, the findings in this research illustrate the positive relationship between multi-part production capability and the feasibility of a hub manufacturing configuration establishment.

This study explores the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it. Although the existing body of knowledge contains research on production decentralization, research on various levels of decentralization is lacking. Using a real-world case study, this study aims to compare the feasibility of different levels of decentralization for AM-enabled spare parts supply chains.

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects.

Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed.

The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability.

The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society.

The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.

This paper aims to propose an integrated supplier selection, order allocation, transportation planning model, along with investment planning for corporate social responsibility (CSR), over a given multi-period horizon under uncertainty. Furthermore, a customer’s behavior to pay more money for items with CSR attributes is considered in the total market demand.

The objective functions, i.e. social value of purchasing, total profit (TP), total delivery lead-time, total air pollution, total water pollution and total energy consumption with regard to a number of constraints are jointly considered in a multi-product system. It is worth noting that operational- and sustainable-related parameters are usually vague and imprecise in this area. Therefore, this paper develops a new fuzzy multi-objective optimization model to capture this inherent fuzziness in critical data.

Through the numerical examples in the textile industry, the application of the model and usefulness of solution procedures are carried out. The numerical results obtained from the proposed approach indicate the efficiency of the solution algorithm in different instances. Moreover, the authors observe that social investment of the buyer, to stimulate market demand, can affect the TP and also involve the total contribution of suppliers in social responsibility.

This research work concentrates on providing a procurement and inventory model through the lens of sustainability to enable textile supply chain managers and related industries to apply the approach to their inventory control and supply management. Totally, the proposed methodology could be applied by many fabric buyers of textile industry tackling purchasing issues and attempting to perfect understanding of social supply chains.