Optimization of Inventory Holding Cost Due to Price, Weight, and Volume of Items

The inventory carrying cost has been assumed uniform for all products in an organization or a warehouse. This assumption is not valid for a diversified range of items in an organization or warehouse. This paper tested this hypothesis of variations in inventory holding costs in a warehouse in two industries based on the physical nature and the price of products. It is found that organizations with a wide variety of products need to calculate the inventory holding cost for each item (SKU) rather than using an average percentage cost of inventory. Inventory holding costs of items in two different organizations were calculated based on the various factors, including the actual cost of space due to the voluminous nature of the items with their existing inventory policies. A variation in inventory holding costs for each item was observed. The variation was small for an organization with homogeneous input costs, and it was large for a multi-product organization. The overall savings in the inventory holding cost due to adjusting the inventory policies through this methodology was found to be about 3%, which is significant for a big organization. This analysis will affect the decision the determining inventory carrying cost, inventory policies (e.g., stocking levels), and pricing policies (e.g., quantity discounts) for retail organizations.

The Inventory Routing Problem with Demand Moves

In the Inventory Routing Problem, customer demand is satisfied from inventory which is replenished with capacitated vehicles. The objective is to minimize total routing and inventory holding cost over a time horizon. If the customers are located relatively close to each other, one has the opportunity to satisfy the demand of a customer by inventory stored at another nearby customer. In the optimization of the customer replenishments, this option can be included to lower total costs. This is for example the case for ATMs in urban areas where an ATM-user that wants to withdraw money could be redirected to another ATM. To the best of our knowledge, the possibility of redirecting end-users is new to the operations research literature and has not been implemented, but is being considered, in the industry. We formulate the Inventory Routing Problem with Demand Moves in which demand of a customer can (partially) be satisfied by the inventory of a nearby customer at a service cost depending on the quantity and the distance. We propose a branch-price-and-cut solution approach which is evaluated on problem instances from the literature. Cost improvements over the classical IRP of up to 10% are observed with average savings around 3%.

Supply Chain Planning Problem Considering Customer Inventory Holding Cost Based on an Improved Tabu Search Algorithm

This paper studies Inventory location routing problem in supply chain distribution network planning under vendor inventory management while considering customer inventory holding cost. In order to minimize the total cost of supply chain, an optimization model is established and an improved tabu search algorithm is used to solve the problem. From the analysis, it shows that the total cost decreases as the total vehicles capacity increases, and the maximum utilization of alternative vehicles and the minimum cost of the system don’t occur at the same time in some cases.

A SYSTEM DYNAMICS MODEL TO DETERMINE THE VALUE OF INVENTORY HOLDING COST

Traditionally, Inventory Holding Cost (IHC) is assumed to be a combination of several costs and determined by the summation of these cost components. Several authors have suggested that the value of IHC ranges between 12-50% of the procurement cost of an item. However, due to the absence of a generally acceptable methodology, many practitioners still determine this percentage based on estimates, benchmarks and intuition. Giving considerations to this reality, a mathematical model to determine the value of IHC using systems dynamics approach was developed. IHC was viewed holistically to identify relevant quantities, their interactions (static or dynamics), behaviour and consequences. A Causal Loop Diagram (CLD) was developed to establish the relationship among these quantities. Thereafter, CLD was transformed into a Flow Diagram (FD). FD was used to formulate a set of systems dynamics equations to obtain IHC. The interaction among fraction of goods ordered per month (FOM), fraction sold per month (FSM) and fraction damaged per month (FDM) was simulated to obtain percentage values of IHC. The value of IHC obtained from the model and simulation analysis ranges between 22.58-25.39% of the item held in stock. Based on these results, it is concluded that the developed model can be used for simulation and system analysis of the holding cost component of an inventory system under different contextual settings.

Multiproduct Economic Lot Scheduling Problem with Returns and Sorting Line

This work studies a hybrid manufacturing–remanufacturing system with a sorting line and disposal. In particular, it models a company that collects used product, remanufactures returned products that have been evaluated as suitable to be recovered, and manufactures new products to satisfy customer demand. Specifically, the system is modeled as a multilevel inventory system, with three types of stock (used products inventory, recoverable inventory, and serviceable inventory), each characterized by an inventory holding cost, and three limited capacity resources: a sorting line, which enables the company to distinguish those returns that are remanufacturable from those that are not; a remanufacturing line to carry out operations on sorted remanufacturable returns; and a manufacturing line to produce new products in order to satisfy customer demand. Each resource is characterized by a setup cost, as well as a constant production rate, while each type of stock is associated with an inventory holding cost. The aim of the paper is to develop a model for the considered production system in order to minimize the setup and inventory holding costs. In particular, the objective is to evaluate the behavior of a controllable disposal rate with the minimization of the total cost function, by considering the effect on the remanufacturing and manufacturing lines.

Optimisasi Perencanaan Produksi Pupuk Menggunakan Firefly Algorithm

In Indonesia, there are many farmers as a livelihood because of fertile soil for agriculture and the demand for food. Production planning is the important part of managing cost spent by the company. In production planning, there are many constraints which have to be satisfied such as the number of productions, the number of workers, and the number of inventory. In previous research, constrained optimizations have been solved by exact method or heuristic method. In this research, production planning optimization will be solved by Firefly Algorithm (FA). FA works as a behavior of Firefly. One of firefly behavior used is less bright firefly will move toward brighter firefly. The simulation results show that FA method can find an approaching optimal solution of production planning like production cost, worker cost, and inventory holding cost satisfying the constraints of the number of productions, workers, and inventory.

DEKOMPOSISI TRADED SPREAD SAHAM DI BURSA EFEK INDONESIA: ORDER PROCESSING COST, INVENTORY HOLDING COST, DAN ADVERSE SELECTION COST

Stock market performance can be viewed from various aspects. One of this aspect is the amount of traded spread. The greater of traded spread can be caused by a lack of trading system and disclosure. Therefore, the decomposition of traded spread is important for the study. This research was conducted to develop a model of traded spread and prove that: the order processing cost, inventory holding cost, and adverse selection cost are the component of traded spread. To achieve these objectives, the stock price and order flow observed during the years 2007 - 2008 and using the purposive sampling as the sampling technique. Samples were observed at 1.782 and using trade indicators to determine whether the transactions undertaken based on the ask price or bid price. The data were analyzed using the multiple regression. The research indicated that: First, the model of traded spread can be used to explain the decomposition of traded spread. Second, the contribution of order processing cost is 29 per cent, the contribution of inventory holding cost is 68 per cent, and the contribution of adverse selection cost is 3 percent. Third, the contribution of adverse selection cost is relatively low when compared with the contribution of real spread. Furthermore, the results of this study can be used to determine the policies in reducing of traded spread.