A PERISHABLE PRODUCT INVENTORY SYSTEM OPERATING IN A RANDOM ENVIRONMENT

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SUPPLY CHAIN MANAGEMENT

Background

A new era has dawned in Supply Chain Management with the advent of globalization. This has led to increased competition and in order to achieve and sustain competitive advantage, companies must be able to respond quickly to customer demand and deliver a high level of customer service. The need for companies to be flexible and to be able to customize their products is also becoming more important. This added pressure on supply chains, coupled with global deregulation, is encouraging many companies to move the sourcing of components and low-value added operations offshore, to lower cost countries (Ross, 2003) – this result in supply chains which increase in distance and complexity.
With global markets and suppliers, companies need to have a supply chain that is lean on inventory and responsive to customer demand. To ensure an efficient supply chain, all aspects of such a supply chain need to be monitored continually and inputs need to be managed in order to anticipate any uncertainty in supply, demand and cost and to ensure that appropriate contingencies are in place.
According to Lakahl et al (2001) companies must concentrate on their core competencies to help sustain competitive advantage. Non-strategic activities that can be performed more effectively by a third party need to be externalized. A company’s core competencies depend heavily on its resources and how they are utilized and if a company is able to develop and allocate resources in a way, which creates more value for customers than their competitors can, it creates a sustainable competitive advantage. A superior supply chain strategy maximizes the value added by internal activities while developing solid partnerships leading to high value external activities.
Supply chain management is plagued with conflicting objectives and supply chain managers must make appropriate tradeoffs to ensure optimal functioning of the supply chain. Traditionally inventory was used to ensure compliance with customer demand and to guard against uncertain delivery lead times. Economies of scale is another reason for inventory accumulation – fixed costs are lowered by producing or ordering in large quantities, transportation discounts can be achieved and it guards against uncertainties. The problem with high inventories however is that capital is tied up and high inventory holding costs is incurred. The inability to meet customer demand, in turn, leads to lost profits and in the long run, possibly the loss of clients. Thus the trade off between customer satisfaction and inventory holding costs is one of the most important decisions that a supply chain manager has to make.
The problem of providing customer satisfaction under conditions of demand variability is usually addressed with safety stock. In the literature, safety stock are considered from the traditional inventory theory viewpoint and it fails to address key features of realistic supply chain problems such as multiple products sharing multiple production facilities with capacity constraints and demand originating from multiple customers. Safety stock levels are dependant on factors such as probabilistic distributions of demand, the demand-capacity ratio as well as the dependence of overall customer satisfaction levels on meeting demands for several different products produced at the same facility (Jung et al, 2004).
In order to manage the supply chain, a supply chain manager needs accurate, timely information. To produce corporate planning solutions, one, or a combination of enterprise planning methods are used, these include manual processes, proprietary planning solutions, Enterprise Resource Planning (ERP) and Advanced Planning and Scheduling (APS).
To support the increasingly complex analysis associated with extended supply chains, decision support tools have to lead key strategic, tactical and operational decisions at every stage of the supply chain. These tools have to provide insight into the tradeoffs that have to be made among alternative strategies regarding, for example, site location, transportation strategies, inventory strategies, resource allocation and supply chain operations (Padmos et al, 1999). In addition to this, these tools and the methods that they employ need to take the uncertainties that are characteristic of supply chains (e.g. demand uncertainty), into consideration.
The objective is to have a supply chain were all participants act as if they are part of one entity in an effort to maximize the timely arrival of good quality raw material, minimum lead times and minimum reasonable inventory – this will contribute to a “seamless supply chain” (Kerbache & Smith, 2004).

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Abstract
ACKNOWLEDGEMENTS

CHAPTER 1: INTRODUCTION
1.1 SUPPLY CHAIN MANAGEMENT Background
1.1.2 Literature Review of Supply Chain Optimization
1.2 INVENTORY OPTIMIZATION
1.2.1 Inventory Management
1.2.2 Inventory Optimization in Software Applications
1.2.3 i2 Technology Seven Step Approach
1.3 INVENTORY MODELS
1.3.1 Types of Inventory Models
1.3.2 Single Product Inventory Systems
1.3.3 Multi-product Inventory Systems
1.3.4 Perishable Product Inventory
1.3.5 Random Environment
1.3.6 Deteriorating Inventory
1.3.7 Techniques Used in the Study of Inventory Models
1.3.8 Measures of System PerformanceAnalysis
CHAPTER 2: A PERISHABLE PRODUCT INVENTORY SYSTEM OPERATING IN A RANDOM ENVIRONMENT
2.1 INTRODUCTION
2.2 ASSUMPTIONS AND NOTATION
2.2.1 Assumptions
2.2.2 Notation
2.3 AUXILIARY FUNCTIONS
2.3.1 Function
2.3.2 Function
2.3.3 Function
2.3.4 Function
2.4 INVENTORY LEVEL
2.5 LIMITING DISTRIBUTION OF THE INVENTORY LEVEL
2.6 MEASURES OF SYSTEM PERFORMANCE
2.6.1 Mean Number of Replenishments
2.6.2 Mean Number of Demands
2.6.3 Mean Number of Perished Items
2.7 COST ANALYSIS
2.8 TOTAL SALE PROCEEDS
2.9 THE TOTAL COST OF REPLENISHMENT
2.10 NUMERICAL ILLUSTRATION
2.10.1 Analysis of Measures of System Performance
2.10.2 Analysis of Probability Distributions
2.11 CONCLUSION
CHAPTER 3: A SINGLE PRODUCT PERISHING INVENTORY MODEL WITH DEMAND INTERACTION
3.1 INTRODUCTION
3.2 ASSUMPTIONS AND AUXILIARY FUNCTION
3.2.1 Function
3.2.2 Function
3.2.3 Function
3.3 MEASURES OF SYSTEM PERFORMANCE
3.3.1 Mean Number of Re-orders
3.3.2 Mean Number of Demands for a Particular Product Which is Satisfied by the same Product
3.3.3 Mean Number of Lost Demand
3.3.4 Mean Number of Demands of Product 1 Being Substituted By Product 2.88
3.3.5 Mean Number of Units Deteriorated From Product 1 and Transited as Product
3.3.6 Mean Number of Product 2 Perished and Removed From the Inventory
3.3.7 Mean Number of Replenishments
3.3.8 Mean Number of Replenishments
3.3.9 Mean Number of Units Scrapped From the Inventory
3.4 COST ANALYSIS
3.5 NUMERICAL EXAMPLE
3.6 CONCLUSION
CHAPTER 4: TWO-COMMODITY CONTINUOUS REVIEW INVENTORY SYSTEM WITH BULK DEMAND FOR ONE COMMODITY
4.1 INTRODUCTION
4.2 MODEL DESCRIPTION
4.3 TRANSIENT ANALYSIS
4.4 Steady State Analysis
4.5 REORDERS AND SHORTAGES
4.5.1 Reorders
4.5.2 Shortages
4.5.3 Expected Cost
4.6 NUMERICAL ILLUSTRATIONS
4.7 CONCLUSION
CHAPTER 5: A SUBSTITUTABLE TWO-PRODUCT INVENTORY SYSTEM WITH JOINT-ORDERING POLICY AND COMMON DEMAND
5.1 INTRODUCTION
5.2 MODEL ASSUMPTIONS AND NOTATION
5.2.1 Assumptions
5.2.2 Notations
5.3 AUXILIARY FUNCTIONS
5.3.1 Function()ri jt φ
5.3.2 Functio ()rl ht
5.3.3 Function()ri j t ψ
5.3.4 Function () ri jp t
5.4 MEASURES OF SYSTEM PERFORMANCE
5.4.1 Mean Number of Replenishments
5.4.2 Mean Number of Re-orders Placed
5.4.3 Mean Number of Lost Demands
5.4.4 Mean Number of Units Replenished
5.4.5 Distribution of the Inventory Level
5.5 COST ANALYSIS
5.6 NUMERICAL ILLUSTRATION
5.7 CONCLUSION
REFERENCES