Performance Of Distribution Centers Research Articles

The impacts of inventory record inaccuracy and cycle counting on distribution center performance

Paper aims The objectives of this paper are: (i) to analyze the impacts of Inventory Record Inaccuracy (IRI) on picking productivity (PP), lost sales (LS), and warehouse capacity utilization (WCU) for different warehouses; (ii) verify if the Cycle Counting (CC) implementation is sufficient to reduce IRI and (iii) the number of CC operators to maintain warehouse performance. Originality A causal loop diagram in detail of the IRI and CC variable's effects in receiving and picking processes is developed. IRI is separated into ghost and hidden inventory to represent the actual effects in WCU, LS, and PP. Research method A dynamic system model is built. Simulations are performed for the five categories of warehouse performance with different numbers of CC operators. Main findings IRI impacts in all warehouses simulated, so managers should monitor IRI besides employee productivity. CC can reduce IRI in all types of warehouses. The number of CC operators to be implemented is different for each warehouse. Implications for theory and practice Several implications for theory and practice are discussed. For example, IRI impacts more ADV and BIC warehouses without CC implementation. In TYP, ADV, and BIC warehouses CC is sufficient to reduce IRI. In MO and DIS warehouses, other improvements are needed.

Re-Layout of Product Placement in Retail Industry to Minimize Order Picking Time with Group Technology Method

In retail industry, warehousing is the main activity. The warehouse can also function as a product distribution center (DC) to consumers or retailers. Distribution centers have an important role in supply chain management. DC performance is strongly influenced by many factors. One of them is picking order performance, which is a product taking operation from a certain storage place ordered by consumer. Total picking time is influenced by picker operator activity, which includes searching activity, traveling / transportation, taking, setting, and other activities/idle. The researched examined the possibility of redesigning the layout and structuring of products on the warehouse rack shelf so as to minimize picking time process. The method to be used in design is the group technology method. Based on the results in a retail industry in Bandung, the completion of the picking time is better by decreasing the average picking order completion time around 61.65% of the current system.

A bi-level stochastic optimization model for reliable supply chain in competitive environments: Hybridizing exact method and genetic algorithm

• Proposing two MINLP models for competitive supply chain using two-stage stochastic programming. • Utilizing a bi-level model to formulate the Stackelberg competition, where DCs are leaders and retailers are followers. • Discussing the effect of disruption risk on the performance of DCs. • Developing a hybrid metaheuristic method based on GA and exact solver to solve the problem. • Studying a real-life case from Alborz pharmaceutical company to show the potential applicability of the model. Managing supply chain operations in a reliable manner is a significant concern for decision-makers in competitive industries. In this article, two mathematical models considering competition and integrity in a three-echelon supply chain under uncertainty are proposed. The competition is formulated as a Stackelberg game such that the distribution centers have more power than the retailers. In the first model, decisions are made about the location and number of distribution centers (DCs), allocation of retailers, and the selling price of products. In the second model, based on the real world, the probability of risk and failure for the distribution centers are considered. Backup facilities should be established for unreliable facilities to meet the demands of retailers during disruption. To capture uncertainty, a two-stage stochastic approach is applied to model the problems. The first stage of the model belongs to the strategic planning and is not affected by randomness, while the second stage deals with tactical decisions depending on the realization of the first stage's random vector. In order to solve the problem, a hybrid genetic algorithm has been applied to large-scale problems. Numerical experiments have been conducted to assess the effectiveness of the proposed algorithm. Next, a sensitivity analysis is performed to recognize the most important parameters and evaluate the accuracy of our approach. Finally, to demonstrate the applicability of the model, the proposed model was implemented on the data of Alborz Pharmaceutical Company.

Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics

Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia.
 Objective: The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics.
 Methodology: A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system were analyzed to find options for improving the system.
 Conclusions: Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network.

A framework for measuring efficiency levels—The case of express depots

The efficiency and effectiveness in any distribution network is largely determined by the performance of depots in such a network. The purpose of this study is to develop a methodological framework to evaluate the performance of distribution centers of express companies. The framework is based on Data Envelopment Analysis and was validated on a set of 44 depots of a large express service provider situated in the United Kingdom. The analysis revealed that 31 depots out of 44 are efficient. Furthermore, statistical analyses identified four factors influencing the efficiency scores of express transportation depots. ► A methodological framework for measuring performance of express depots is proposed. ► A Data Envelopment Analysis (DEA) model for express depots is developed. ► Critical factors for performance: labor, subcontractors, population, and production. ► Unlike other studies no relation between depot efficiency and size was found.

Information Systems Utilization to Improve Distribution Center Performance from the Perspective of Task Characteristics and Customers

The primary purpose of this study was to identify common characteristics associated with the distribution center performance-leveraging activities. The second objective was to examine empirically the impact of information systems on the performance of distribution centers from the customer’s perspective. To address the primary aims, a questionnaire-based survey was conducted to determine distribution center performance-leveraging activities. Additionally, a second data collection tool was used to assess how strongly certain characteristics are related to a given activities. Finally, a third survey was used to collect data from their client organizations to examine the associations between information systems utilization and distribution center performance. The results suggest that information and data intensive and work procedure-related characteristics may be associated with overall distribution center performance. In addition, from customer’s perspective, information systems usage may improve performance in terms of order fulfillment rate, on-time delivery, and emergent order handling.

The effects of interdepartmental customer orientation on distribution center performance

Firms have begun to look internally for ways to increase external service quality. ANOVA is used to examine the effect of interdepartmental customer orientation on time, inventory, and customer service-based performance variables in distribution centers. Findings indicate that high interdepartmental customer orientation positively affects distribution center performance in terms of time-based performance measures and customer satisfaction. Interdepartmental customer orientation was found to have only a marginal affect on inventory performance. Implications of the current research for distribution centers and transportation managers are discussed along with limitations and opportunities for future research.

An integrated benchmarking approach to distribution center performance using DEA modeling

AbstractThis research proposes an integrated benchmarking framework illustrated in the context of a large supply chain system comprised of 102 distribution centers (DCs). We employ recent extensions of data envelopment analysis while addressing difficulties often associated with empirical data in real life settings. The study measures DC productivity in a large scale setting, evaluates and identifies DCs with consistent best performance using facet analysis, and detects performance trends using window analysis of 4 years’ data. This extensive evaluation of the empirical production frontier and of “role model” DCs provided very interesting insights for strategically managing operations. Our approach opens up possible new directions for examining supply chain DCs or other activities where explicit knowledge about the relationship between the inputs and outputs is not well known.