We consider a single-product inventory system that serves multiple demand classes, which differ in their shortage costs or service level requirements. We assume a critical-level control policy, and show the equivalence between this inventory system and a serial inventory system. Based on this equivalence, we develop a model for cost evaluation and optimization, under the assumptions of Poisson demand, deterministic replenishment lead-time, and a continuous-review (Q, R) policy with rationing. We propose a computationally-efficient heuristic and develop a bound on its performance. We provide a numerical experiment to show the effectiveness of the heuristic and the value from a rationing policy. Finally, we describe how to extend the model to permit service times, and to embed within a multi-echelon setting.

This thesis studies a single item periodic review inventory problem with stochastic demand, random price and quotation cost. It differs from the traditional inventory model in that at the beginning of each period, a decision is made whether to pay the quotation cost to get the price information. If it is decided to request a price quote then the next decision is on how many units to order; otherwise, there will be no order. An (r, S1, S2) policy with r < S2, S1 <= S2 is proposed for the problem with two prices. It prescribes that when the inventory is less than or equal to r, the price quotation is requested; if the higher price is quoted, then order up to S1, otherwise to S2. There are two cases, r < S1 or S1 <= r. In the first case, every time the price is quoted, an order is placed. It is a single reorder point two order-up-to levels policy that can be considered as an extension of the (s, S) policy. In the second case, S1 <= r, it is possible to “request a quote but not buy” if the quoted price is not favorable when the inventory is between S1 and r. Two total cost functions are derived for the cases r < S1 <= S2 and S1 <= r < S2 respectively. Then optimization algorithms are devised based on the properties of the cost functions and tested in numerical study. The algorithms successfully find the optimal policies in all of the 135 test cases. Compared to the exhaustive search, the running time of the optimization algorithm is reduced significantly. The numerical study shows that the optimal (r, S1, S2) policy can save up to 50% by ordering up to different levels for different prices, compared to the optimal (s, S) policy. It also reveals that in some cases it is optimal to search price speculatively, that is with S1 < r, to request a quote but only place an order when the lower price is realized, when the inventory level is between S1 and r.

This thesis studies a single item periodic review inventory problem with stochastic demand, random price and quotation cost. It differs from the traditional inventory model in that at the beginning of each period, a decision is made whether to pay the quotation cost to get the price information. If it is decided to request a price quote then the next decision is on how many units to order; otherwise, there will be no order. An (r, S1, S2) policy with r < S2, S1 <= S2 is proposed for the problem with two prices. It prescribes that when the inventory is less than or equal to r, the price quotation is requested; if the higher price is quoted, then order up to S1, otherwise to S2. There are two cases, r < S1 or S1 <= r. In the first case, every time the price is quoted, an order is placed. It is a single reorder point two order-up-to levels policy that can be considered as an extension of the (s, S) policy. In the second case, S1 <= r, it is possible to “request a quote but not buy” if the quoted price is not favorable when the inventory is between S1 and r. Two total cost functions are derived for the cases r < S1 <= S2 and S1 <= r < S2 respectively. Then optimization algorithms are devised based on the properties of the cost functions and tested in numerical study. The algorithms successfully find the optimal policies in all of the 135 test cases. Compared to the exhaustive search, the running time of the optimization algorithm is reduced significantly. The numerical study shows that the optimal (r, S1, S2) policy can save up to 50% by ordering up to different levels for different prices, compared to the optimal (s, S) policy. It also reveals that in some cases it is optimal to search price speculatively, that is with S1 < r, to request a quote but only place an order when the lower price is realized, when the inventory level is between S1 and r.

A unique approach is developed for evaluating Human Capital (workforce) requirements. With this approach, new ways of measuring personnel availability are proposed and available to ensure that an organization remains ready to provide timely, relevant, and accurate products and services in support of its strategic objectives over its planning horizon. The development of this analysis and methodology was established as an alternative approach to existing studies for determining appropriate hiring and attrition rates and to maintain appropriate personnel levels of effectiveness to support existing and future missions. The contribution of this research is a prescribed method for the strategic analyst to incorporate a personnel and cost simulation model within the framework of Human Resources Human Capital forecasting which can be used to project personnel requirements and evaluate workforce sustainment, at least cost, through time. This will allow various personnel managers to evaluate multiple resource strategies, present and future, maintaining near “perfect” hiring and attrition policies to support its future Human Capital assets.

Title: Inventory Control of WEEE (Waste of Electronic and Electrical Equipment) Reverse Logistics in parts of China  --The HEA (household electrical appliances) manufacturers’ perspective   Background: With economic development, the requirement of public for enterprises and products has become increasingly rational. Price is no longer the only consideration of public, they also pay attention to other factors, such as energy conservation. The manufacturers face enormous challenges because of the late start of products recycling in China. So enterprises start to build their own recycling logistics system in order to have more competitive for themselves.   Purpose: The purpose is to describe the methods of inventory controls in the case HEA manufacturers and the problems of inventory controls, find out what problems exsist in the case manufacturers, then analyze what are the origins of these issues for HEA manufacturers as well as propose how these issues can be alleviated, and what methods would be suitable. By solving these research questions, the thesis tries to offer some suggestions about inventory control improvement not only to the cases, but also wider to the whole HEA manufacturers in China.   Method: Multiple - case study as research method has been applied. Specifically, two case companies, Chinese HEA manufacturers, have been selected. Two telephone focused interviews combined with open-ended interviews have been conducted with two related managers. The empirical evidence has been analyzed by using with-in case study and cross-case analysis method, then model analysis is applied.     Results, conclusion: First, the methods are not good enough in the two case companies, a new model is built to help inventory control in the case companies. Second, from external and internal perspective, the origins to cause the problems are a lot, but the main causes are environmental factors, and in busy seasons of these companies. Besides, for doing reverse logistics is a capital costing job, none of the enterprises would like to step in. and reverse logistics is in an uncertainty environment, doing so need to make sure everything clearly and orderly, or costs will be a large amount. The best way of improving inventory control of WEEE reverse logistics in China is that the enterprises standing together to restore the orders.   Limitations and drawbacks: For one thing, there are not enough previous studies references in China, this brought some difficulties of supporting the view points in the thesis. Two empirical cases are not persuasive enough to represent the whole China due to the limitation of the authors’ knowledge and the huge area of China. Nevertheless, the ideal model in model analysis is not that complicated, which means, for more complex problems and processes, the model would be lame.

M. Tech. (Industrial Engineering, Faculty of Engineering and Technology) Vaal University of Technology| / Implementing either periodic or continuous inventory review model within most manufacturing-companies-supply chains, as a management tool, incurs higher costs. These high costs affect the system flexibility which in turn affects the level of service required to satisfy customers. However, these effects are not clearly understood. This may be due to the fact that lead time and demand which are important input parameters of the manufacturing supply chain are not designed to be fully utilized under different and uncertain conditions such as seasonality, poor manufacturing, poor supplies and delivery performance, etc. Coming up with a hybrid inventory model which may combine, in some sense a continuous (r, Q) and a periodic (R, S) inventory review models can be useful in dealing with such problem. Therefore, more attention should be first devoted to formulating accurate models for lead time and demand that incorporate uncertainty. This study presents a simulation based approach that assesses the effect of uncertainty on the cost of implementing a continuous (r, Q), periodic (R, S) and hybrid inventory review models while considering appropriate constraint such as customer service and system flexibility. The stochastic representations of demand and lead time are proposed and used in the simulation models. Results reveal that under a unique situation, implementing a continuous (r, Q) inventory review model may cause manager to under-budget while the use of a periodic (R, S) inventory review models may lead to over budget and vice versa. Further investigation shows that the cost of implementing the hybrid inventory model, although higher at the beginning of operation, seems to be the most cost effective one over time. The result also reveal optimal re-order point path and optimal review interval path which when followed, should lead to optimal inventory cost path as demand and lead time fluctuate. Thus, a management guide is proposed that can be used by managers in making inventory decision.

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This thesis examines how to design a mathematical inventory model for a ”Fast Moving Consumer Goods”-company (FMCG-company), which determines the optimal reorder point and order quantity such that the average inventory cost is minimized. The thesis was made in collaboration with a ”Software as a Service”- company which provided the data containing information about the products and inventory management of one of their customers, a FMCG-company. The thesis first considers a basic EOQ-model, with constant demand rate, that suggests a reorder time and order quantity for the products. Since constant demand rate might be an unrealistic assumption for a FMCG-company, the thesis also considers a (R,Q)-model, where the demand was based on a forecast made by using the Holt-Winters model on previous sales history. The solutions were found by investigating the singular points and comparing them to the critical point. The thesis shows that the EOQ-model gives useful results for the most indemand products, while the reorder times for the less popular products are instead impractically high. The (R,Q)-model showed more stable solutions for all products and therefore proves to be a better inventory model for FMCG-companies, as expected. Simulations of the (R,Q)-model showed various inventory cases, where some showed a mismatch between the inventory level and the demand. The cases shows how demand planning can be applied for different products for when to consider changing inventory strategy or discontinuing products and how the orders can be made optimally. / Detta examensarbete undersöker hur en matematisk lagermodell kan utformas för ett ”Fast Moving Consumer Goods”-företag (FMCG-företag), som bestämmer den optimala beställningspunkten och orderkvantiteten så att den genomsnittliga lagerkostnaden minimeras. Examensarbetet gjordes i samarbete med ett ”Software as a Service”-företag som tillhandahöll data innehållandes information om produkter och lagerhantering hos en av deras kunder, ett FMCG-företag. Avhandlingen behandlar först en grundläggande EOQ-modell, med konstant efterfrågan, som föreslår en återbeställningstid och orderkvantitet för produkterna. Eftersom att en konstant efterfågan kan anses vara ett orealistiskt antagande för ett FMCG-företag, tar avhandlingen även upp en (R,Q)-modell, där efterfrågan baserades på en prognos gjord med hjälp av Holt-Winters-modellen på tidigare försäljningshistorik. Lösningarna hittades genom att undersöka de singulära punkterna och jämföra dem med den kritiska punkten. Avhandlingen visar att EOQ-modellen ger användbara resultat för de mest efterfrågade produkterna medan beställningstiderna för de mindre populära produkter är ofta opraktiskt höga. (R,Q)-modellen visade mer stabila lösningar för alla produkter och visar sig därmed vara en bättre lagermodell för FMCGföretag, som förväntat. Simuleringar av (R,Q)-modellen visade olika fall, där vissa visade en obalans mellan lagernivån och efterfrågan. De olika fallen visar hur efterfrågeplanering kan tillämpas för olika produkter för när man ska överväga att ändra lagerstrategi eller avveckla produkter och hur beställningarna kan göras optimalt