Distribution Requirements Planning Research Articles

IMPLEMENTASI METODE DISTRIBUTION REQUIREMENT PLANNING UNTUK MEMINIMALKAN BIAYA DISTRIBUSI DI PT DBM CABANG PURWOKERTO

Distribution is one of the most important things to increase customer satisfaction. However, distribution is often a problem for companies. The wider the marketing area owned by the company, the more complex the problems that arise in the distribution schedule. PT DBM's Purwokerto branch also faced a similar situation, where distribution activities were not well coordinated, resulting in lost sales, accumulation of goods in each warehouse and swelling storage costs. Therefore, an appropriate method is needed to schedule distribution activities to each warehouse, so as to reduce distribution costs. The method used in this research is Distribution Requirement Planning. This method is effective for reducing distribution costs. The results showed that by using the Distribution Requirement Planning method, distribution costs were lower than the company's distribution method. Distribution costs using the company distribution method amounted to Rp. 1,371,391, while distribution costs using the Distribution Requirements Planning method amounted to Rp. 1,040,501, resulting in a cost reduction of 24.13%.

A joint location-distribution-inventory model for a multi-echelon supply chain network considering multi-sourcing, drop-shipping, and lateral transhipment

Competitive pressure in the global marketplace requires enterprises to establish efficient distribution planning and inventory management in a supply chain. This study proposes a framework to optimise distribution and inventory decisions, integrating decisions of opening or closing facilities in a multi-echelon supply chain network under non-stationary demand. The model adopts distribution requirements planning (DRP) and considers flexible distribution strategies, including multiple sourcing, drop-shipping, and lateral transhipment to minimise the total expected cost. The original stochastic model has been transformed into an equivalent deterministic mixed-integer linear programming model by using the sample average approximation (SAA) technique. Numerical experiments are conducted to validate the model and its practical applications. The model that adopted multiple flexible distribution strategies outperforms the models that adopted a single distribution strategy. This study is useful for enterprises who sell products through their physical stores and online stores on e-commerce platforms or company private websites.

Distribution planning for multi-echelon networks considering multiple sourcing and lateral transshipments

Nowadays, due to the increasing complexity of business environment, especially demand uncertainty, supply chain managers need to establish more-effective sourcing and distribution strategies to ensure high customer service and low stock costs. To overcome this challenge multi-echelon network structures and alternative distribution strategies such as lateral transshipments and multiple sourcing should be considered in inventory optimisation models. In this article, we propose a scenario-based modelling approach to solve a two-stage multi-echelon inventory optimisation problem with a non-stationary demand. The model is based on a distribution requirements planning (DRP) approach and minimises the expected total cost that is composed of the fixed allocation, inventory holding, procurement, transportation, and back-ordering costs. Alternative inventory optimisation models, including the lateral transshipment strategy and multiple sourcing, are thus built, and the corresponding stochastic programmes are solved using the sample average approximation method. Through a numerical investigation conducted with several generated instances and an empirical investigation based on the case of a major French retailer’s distribution network, we show the substantial benefit of lateral transshipments and multiple sourcing in reducing the expected total costs of the distribution network.

Logistics Processes Management In Supply Chain

The aim of the article was to develop a model for managing logistics processes in the supply chain. The use of process analysis and integration of logistics processes in the supply chain enabled the continuity of power in the process of supply and in the production process and it also enables the effective and efficient distribution process management. Basing on the developed map of logistics processes author of the article described the place in the process where one can apply optimization methods of quantity and quality. By using statistical quality control it was possible to shorten the duration of the adoption process of delivery to the warehouse, thereby lowering the cost of quality control. Integration of supply processes and production logistics in the supply chain enabled the continuity of power of parts in the production process, however thanks to the combination of methods DRP (Distribution Requirements Planning), Kanban, VMI (Vendor Managed Inventory) and CPFR (Collaborative Planning, Forecasting and Replenishment) we obtain the effective and efficient distribution process management.

PERANCANGAN APLIKASI SUPPLY CHAIN MANAGEMENT PADA DISTRIBUSI AYAM POTONG PT. NUJ / MTS DENGAN MENGGUNAKAN METODE DISTRIBUTOR REQUIRMENT PLANNING (DRP)

A company will be faced with problems relating to the distribution system. Thisproblem arises because consumers are in geographically dispersed locations, this resulted in the importance of holding inventories at several location. PT.Nuj / Mts is the distribution company, this company manufactures the product. PT.Nuj / Mts not yet have a good distribution planning. Planning distribution company run by less effective and has few weaknesses. Given these problems, then conducted research with the method of Distribution Requirements Planning (DRP) in the hope to be distributing the product from factory to each of city in an optimal distribution. Distribution Requirements Planning is a method to handle the procurement of supplies in a multi-echelon distribution network. The purpose of the Distribution Requirements Planning (DRP), which make a planning and scheduling activities of a good distribution, so that success in meeting customer demand will be more optimal, increase performance in fulfilling orders in a timely and appropriate amount so that distribution costs can be reduced minimized. Ofthe results, themethod ofdistribution planningcompany, the Total Cost of distribution including product demand data, product price, booking fees, storage fees, shipping costs and lead time data. By using DRP method, in order be expected to more profitable company in the future. Keywords : Distribution, Distribution Requirements Planning (DRP)

Design and Analysis of the Third-Party Logisitics Management System Based on J2EE

The analysis and design ideas of third party logistics management information systems were introduced based on the research for a company in Beijing, China. With distribution requirements planning ( DRP ) system, data sharing can be realized effectively and are transparent to the existing system with scalability. The third-party logistics information management system developed based on this proposal has been put into operation, and solved the resource sharing problem inside a logistics company.

Perbaikan Sistem Distribusi Dan Transportasi Dengan Menggunakan Distribution Requirement Planning (DRP) Dan Algoritma Djikstra (Studi Kasus : Depot Pertamina Tasikmalaya)

The competition in petroleum industry to meet customer requirements is very tight. Companies that are able to survive are a company that can meet consumer demand in a timely manner. Companies must pay attention to the smoothness of its distribution system so as to meet consumer demand for continuous and timely. The problems frequently encountered by the Petroleum Industry are a delay in delivery of fuel from depots to filling stations are located in the region such coverage Depot. This is caused by a number of previously unknown oil demands, the limited number of tank cars for fuel and lack of accuracy, adequacy determination of delivery routes fuel from depots to retail outlets. This paper is intended to help the Oil Industry to determine the fuel distribution system and the determination of delivery routes with the aim of minimizing shipping costs and delivery schedules to determine fuel from depots to retail outlets. The method used in this study is the Distribution Requirements Planning (DRP) to determine how the delivery of fuel from depots to retail outlets each with respect to the amount of the daily requirement of each pump.This research begins with forecasting demand for fuel at 81 gas stations for several future periods. Under the DRP method of forecasting at each gas station for one week ahead will be a need for fuel to be supplied by the Depot Tasikmalaya. So that the Depot can estimate the amount of fuel that must be sent to each retail outlets in the coming period. Furthermore Djikstra algorithm used to determine fuel delivery routes from depots to retail outlets. The selected route is a route with a shorter distance and minimum shipping costs. After that car assignment scheduling done by using rule-based scheduling longest travel time (Longest Processing Time), to obtain the finish time of fuel delivery is almost the same on every car is assigned.Use of the DRP method, assignment and scheduling and Djikstra algorithm, tank car is useful because it can estimate the needs of Tasikmalaya Depot gas station in the coming period so that the fuel supply depot in accordance with the requirements both in terms of quantity and time, can determine the route of delivery of fuel depots to gas stations with the shipping costs more low-cost, timely delivery time and workload tank car that is almost the same.

Comparison of supply chain replenishment strategies in a non-capacitated distribution system

This research considers inventory replenishment in a stochastic, non-capacitated, multi-echelon distribution system. Simulation is used to model Distribution Requirements Planning (DRP), reorder point and Kanban replenishment strategies. Additional experimental factors include demand seasonality, lot sizes, demand uncertainty and transit time uncertainty. Trade-off curves between inventory and delivery performance are generated. Statistical techniques, including ANOVA and regression, are then used to determine the relative dominance of the replenishment strategies by comparing the areas under the trade-off curves. Results show the methodology developed for analysis of the results works well in identifying both main and interaction effects. DRP performance is found to be the most robust, followed by that of reorder point and Kanban systems, respectively.

Approach of just-in-time distribution requirements planning for supply chain management

The distribution of finished goods to customers plays an important role in supply chain management. This paper introduces an approach that takes care of multi-warehouse and multi-retailer scenarios. In order to pull material through a supply chain effectively, we proposed a just-in-time distribution requirements planning system under the limited supply capacity. The aim is to establish an optimal distribution requirements planning model to minimize the total cost of manufacturing and transportation as well as the earliness/tardiness penalty in meeting retailer's requirements under limited warehouse capacity. Using mathematical deduction, the model can be translated into a linear programming problem and solved by a simplex procedure. The computational results have shown that the proposed approach can offer more effective distribution requirements plans.

Optimal antithetic weights for lognormal time series forecasting

The theory for estimating optimal weights for combining antithetic fitted values which contain antithetic (negatively correlated) errors, produced from lognormal historical time series, is presented. The functions which determine the optimal weights used to combine the lognormal and linear projection of the antithetic fitted values, are derived. The method reduces mean square fitted error, when tested on simulated lognormal and non-lognormal autoregressive series of varying order. Antithetic forecasting yields reductions in forecast mse (which otherwise increases with forecast horizon), up to about 50% in large-scale empirical validation tests applied to 111 time series. Scope and purpose Serial correlation in time series data causes a forecast error bias which varies with forecast origin. Antithetic forecasting corrects this problem. Especially critical applications include electric utility hourly load forecasting for economic dispatch, information feedforward in continuous process control, and forecasting for just in time manufacturing and distribution requirements planning (DRP) in global logistics. There has been a shift away from the static (EOQ) optimization approach to inventory management, and a movement towards a dynamic time phased DRP approach. Automated computerized forecasts are routinely updated as each time period elapses, so there is a continuously changing forecast origin. There is very little time between each new forecast. If the effect of the forecast origin is not removed, then we would need to re-evaluate the appropriateness of the forecasting model for each new forecast origin. That could be a major exercise in model building and selection, requiring the on-line involvement of an experienced forecasting expert. Even then a complete evaluation will be impossible in the allotted time. The antithetic forecasting procedure may be retrofitted to any forecasting model.

Dynamic distribution planning techniques: progressive logistics optimization for manufacturers

Discusses new distribution planning techniques which take advantage of state‐of‐the‐art software packages. Compares and contrasts new planning techniques against traditional distribution requirements planning (DRP). Describes how new solutions can truly optimize operations, and provide feasible plans to run the company. Concludes that in order to compete in today’s manufacturing environment, the walls of the factory must be knocked down.

Integrated Logistics Systems with Distribution Resources Planning

Planning systems are only as good as their interface with the distribution function. If distribution is having an adverse effect on the overall logistics system, then we have a “systems gap”. What is then needed is the use of distribution requirements planning (DRP), a powerful method with which to integrate the logistics systems of organisations.

Distribution Requirements Planning: A Generalised System for Delivery Scheduling in a Multi‐Sourcing Logistics System

Distribution requirements planning (DRP) is one of the scheduling methods used in logistics systems. A generalised version is presented of a DRP system designed to enhance scheduling flexibility of currently used DRP systems and to deal with multi‐sourcing trans‐shipment problems in a multi‐echelon logistics system. The required information inputs, capabilities and advantages of this generalised DRP system are described in detail. Finally, the future research direction to improve the adaptability of DRP in distribution systems is discussed.

A heuristic algorithm for managing inventory in a multi-echelon environment

Abstract The proper management of finished goods inventory in a multi-echelon environment is an extremely difficult problem to solve. Optimization approaches for solving this problem are intractable, and currently available heuristic techniques have serious deficiencies. Pull systems and independent demand based push systems do not adequately deal with the lumpy demand caused by the dependent relationships of stocking locations in a multi-echelon environment. Although distribution requirements planning (DRP) can be modified to handle uncertainties by adding safety stock, the process of deriving demands at lower echelons implicitly assumes deterministic conditions. In addition, no heuristic method directly considers the capacity of transportation and storage resources, or includes transportation costs. The incorporation of these additional complexities is left to the discretion of management. This study introduces a new heuristic algorithm that addresses these additional complexities. The algorithm is an improvement heuristic that can be implemented as an add-on module to a DRP system. At the core of this heuristic are two search routines ( Savings and Change ) for improving an initial solution determined by the DRP explosion process. We demonstrate this heuristic algorithm with two simple problems to provide some insight concerning its operation. The heuristic algorithm is then tested against an alternative rolling horizon mixed-integer linear programming (MILP) procedure that solves each linkage between locations optimally for each planning horizon. The example scenario used in this research consists of four distribution centers ordering from two regional warehouses, which in turn order from a central warehouse. Computer simulation is used to compare the two alternative procedures with rolling horizons and demand uncertainty. The performance of the procedures is compared for total costs, customer service, and the number of orders placed by locations within the control of the model. Multivariate analysis of variance (MANOVA) techniques are used to analyze these performance measures. The experimental results suggest that the heuristic algorithm performs extremely well when compared to the MILP based procedure. Demand uncertainty is found to have a significant effect on customer service performance, but safety stock can be added to distribution centers in actual applications to control this situation. In addition, a qualitative comparison between the MILP approach and the heuristic algorithm in this study suggests that the introduction of demand uncertainty has the effect of reducing the experimental differences between the two techniques. This result suggests that the heuristic algorithm presented in this research works best (relative to the MILP approach) in the actual environments for which it is intended. This study is of considerable value to managers concerned with the management of finished goods in a multi-echelon environment. It represents an initial step toward the development of a heuristic algorithm that incorporates additional real-world complexities and is tractable for realistically large problems. The findings from this study provide encouragement that similarly designed heuristics can be implemented within multi-echelon inventory control systems in the future.

Replenishment Analysis in Distribution Requirements Planning*

ABSTRACTMost research on lot sizing has been for the case of a manufacturing system. In this paper, analogous issues are studied for a distribution network. Specifically, we consider the choice of shipment quantities within distribution requirements planning (DRP). A simulation model of DRP in a multi‐echelon, rolling‐schedule environment is used to examine, in conditions of both certain and uncertain demand, the performance of five lot‐sizing rules.We conducted a full‐factorial experiment in which four additional parameters were varied: distribution network structure (two options), demand distribution (three options), forecast error distribution (three options), and ordering cost (three values), as suggested by the consulting study which motivated our research.We found that for DRP, contrary to the “shop floor” wisdom on MRP, the choice of lot‐sizing method can be important. Generally the Silver‐Meal and Bookbinder‐Tan heuristics were significantly better than the other methods.