An investigation of the desired levels of investment and amplification of service levels in a multi-echelon new and reparable item inventory system

The purpose of this thesis is to structure a periodic review model and investigate the dynamic response for a multi-echelon new and reparable inventory system

Developing Inventory Policy for Aircraft Spare Parts using Periodic Review Model

This research is conducted to develop inventory policy of aircraft consumable spare parts which are needed on aircraft maintenance activity. In this research, we used periodic review model to determine the optimal policy of aircraft spare parts inventory. By using the periodic review model, we find optimal period of inventory review and maximum level of inventory.  The optimal decision is determined based on the minimum total cost. We have classified consumable spare parts using ABC method to categorize them based on their dollar contribution and demand frequency. Therefore in this research, we focus on managing the inventory level for spare parts on class C. The result from this study shows that the proposed periodic review policy result in lower total inventory cost compared the the company policy. The proposed policy gives an average saving 35.38 %. Keywords: Inventory, spare part, periodic review, inventory review, maximum level, ABC method

Inventory System Design For PT Mechanical Electrical Provider

PT Mechanical Electrical Provider (an alias name, henceforth MEP) is a company that focuses on the telecommunications sector as a mechanical & electrical supplier. It was found that the company was still experiencing inventory problems, such as needing more goods and excess goods from its inventory system. Therefore, this study aims to analyze the inventory problems and provides recommendations for the inventory system design. This study uses a qualitative approach by interviewing five officers and staffs in the Procurement Division. Next, this study uses fishbone analysis to find the causes of the problems faced by PT MEP. The Fishbone diagram shows several causes of the inventory problems: people, equipment, method, and material causes. This study uses the quantitative approach by applying the ABC Classification to determine the classification of each item and the best way to control it. There are 6 A-class items, 14 B-class items, and 21 C-class items pada PT MEP. From the ABC classification, A-class products use the Continuous Review Model (Q-system) calculation for an optimal amount and the right time of replenishing inventory. For class B and C classification, the design suggests a Periodic Review Model (P-system) to find the optimal amount for refilling using the difference between the target inventory and the amount of inventory at the time of checking. Through the new system, this research expects a more optimal inventory so that the company can reduce the cost losses incurred so far. Keywords: ABC classification, Continuous Review Model (Q-system), Fishbone Diagram, Periodic Review Model (P-system), Telecommunicatio

Approximate Order-up-to Policies for Inventory Systems with Binomial Yield

This paper studies an inventory policy for a retailer who orders his products from a supplier whose deliveries only partially satisfy the quality require- ments. We model this situation by an infinite-horizon periodic-review model with binomial random yield and positive lead time. We propose an order- up-to policy based on approximating the inventory model with unreliable supplier by a model with a reliable supplier and suitably modified demand distribution. The performance of the order-up-to policy is verified by com- paring it with both the optimal policy and the safety stock policy proposed in Inderfurth & Vogelgesang (2013). Further, we extend our approximation to a dual-sourcing model with two suppliers: the first slow and unreliable, and the other fast and fully reliable. Compared to the dual-index order- up-to policy for the model with full information on the yield, the proposed approximation gives promising results

Developing a closed-form cost expression for an (R,s,nQ) policy where the demand process is compound generalized Erlang.

We derive a closed-form cost expression for an (R,s,nQ) inventory control policy where all replenishment orders have a constant lead-time, unfilled demand is backlogged and inter-arrival times of order requests are generalized Erlang distributedInventory control; Compound renewal process; Generalized Erlang distribution;

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy.Singapore-MIT Alliance (SMA

An aggregation-based approximate dynamic programming approach for the periodic review model with random yield

A manufacturer places orders periodically for products that are shipped from a supplier. During transit, orders get damaged with some probability, that is, the order is subject to random yield. The manufacturer has the option to track orders to receive information on damages and to potentially place additional orders. Without tracking, the manufacturer identifies potential damages after the order has arrived. With tracking, the manufacturer is informed about the damage when it occurs and can respond to this information. We model the problem as a dynamic program with stochastic demand, tracking cost, and random yield. For small problem sizes, we provide an adjusted value iteration algorithm that finds the optimal solution. For moderate problem sizes, we propose a novel aggregation-based approximate dynamic programming (ADP) algorithm and provide solutions for instances for which it is not possible to obtain optimal solutions. For large problem sizes, we develop a heuristic that takes tracking costs into account. In a computational study, we analyze the performance of our approaches. We observe that our ADP algorithm achieves savings of up to 16% compared to existing heuristics. Our heuristic outperforms existing ones by up to 8.1%. We show that dynamic tracking reduces costs compared to tracking always or never and identify savings of up to 3.2%

Comparative Study of Periodic Review Policy and IoT Enabled Policy for the Domestic Waste Management

“Innovation is the difference between leaders and followers” said the famous Apple‟s CEO, Steve Jobs. The technological advancement was and will always be exploited into delivering a higher quality of life for communities. The Internet of Things (IoT) technology is not excluded from this fact. Nowadays, many countries are building smart cities that are equipped with smart traffic control, environmental monitoring and public safety. Smart waste management is an emerging initiative in this matter. This thesis addresses the application of the IoT in the waste collection systems. It assesses the Periodic Review Policy and the IoT Enabled Policy of waste collection systems. A model was developed for each system using Anylogic software. Each model performance was tested using six different waste generation scenarios and seven collection policies. The performance analysis of the models was based on the economic, environmental and citizen satisfaction measures. The results of this research showed that each collection model achieved good performance in a specific scenario. The three times per week periodic review policy performed the best for high waste generation scenarios whereas the 70% threshold IoT enabled policy was the best for low waste generation scenarios