Home Project-material MODELING A DYNAMIC SUPPLY CHAIN MANAGEMENT SYSTEM FOR EEDC OF NIGERIA

MODELING A DYNAMIC SUPPLY CHAIN MANAGEMENT SYSTEM FOR EEDC OF NIGERIA

Dept: INFORMATION TECHNOLOGY File: Word(doc) Chapters: 1-6 Views: 12

Abstract

EEDC is charged with the primary role of generating, transmitting, distributing and marketing electricity in Nigeria at a regulated and un-privatized environment. The causes of the problems were traced to factors like demand unpredictability, parts indigenization, high service levels, large investments on parts and a lot of items in the inventory. These inventory items are used to service the generation facilities, and so are critical to the continued operation of the company. The company may want to release some capital from its inventory investment by reducing stock levels. In addition, manual ways of handling inventory has failed to cope with factors like stochastic demands, better service levels, and shorter lead times and providing perfect heuristics for Inventory-related decision making. The researcher’s aim and objective of this study is to model a dynamic supply chain management system for EEDC of Nigeria. The methodology adopted for this research work is the Structured Syst

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

EEDC is charged with the primary role of generating, transmitting, distributing and marketing electricity in Nigeria at a regulated and un-privatized environment (Onohaebi, (2009). The company operates a maintenance-like environment which focuses on providing constant support for the operation of a single unit, plant or component (or a fleet or group of components), and ensuring that operational requirements are achieved (Tsakatikas and Rooij, 2018). Plagued with many complications, the least is the broad range of products, which makes the problem of determining right-sized inventories more difficult and also the absence of an effective maintenance culture.

Essentially, EEDC must maintain a balance between the plant availability and output against the electricity demand and the plant’s capacity through a better understanding of spare parts demand. This would be of potential improvement because if the company can understand when it will need parts, then the company can plan accordingly to promote just-in-time delivery and minimal inventory (Power and Ramesh, 2017).

The Nigerian power transmission network is characterized by prolonged and frequent outages, outages like planned outages and forced outages (Oluwole, 2012) which can be associated with aging equipment/defects (leading to frequent conductor/ jumper cuts, frequent earth faults resulting from reduction in overhead clearance and refuse burning, circuit breaker problems), lightning, wind, birds/animals, vandalization, accidents and poor job execution by contractors. The study revealed that the existing transmission network is characterized by poor maintenance and over aged leading to the collapse of several spans; and that prolonged and frequent outages are phenomena in the transmission networks (Tsakatikas and Rooij, 2018).

Again, most of the transmission lines are very long and fragile leading to frequent conductor cuts which gives rise to high voltage drops and power losses in the network. Onohaebi, (2009) proffered solutions which include carrying out a study to identify all weak areas in the network with a view to strengthen the network, carrying out planned and routine maintenance on the network to reduce the incident of collapsed spans, others include addition of more substations into the network to assist in the reduction of long lines and improve the voltage profiles of the network, and promptly rectifying faults and energizing all the lines to reduce the incidence of vandalization. Batarda, (2018) examined voltage collapse on the Nigerian National Grid. They maintained that voltage instability and collapse contribute to large extent system collapse or blackouts and it is one of the major concerns for today’s electric power system operations (Arobieke and Oni, 2012).

Customers are more demanding, requiring greater choice, quality, value for money and timely delivery. This implies greater concern for inventory control on the part of companies and service providers. Inventory control is based on acquiring, storing and managing the inventory in such a manner that stock is always available to cater for contingencies, maximize profit and minimize wastage, and avoid disservice to customers (Power and Ramesh, 2017). However, inventory levels are affected by customer service expectations, demand uncertainty, and the flexibility of the supply chain, which employing strategies to obtain optimal balance between these three extremes makes for a better company in terms of reduction in disservice and customer dissatisfaction (Onohaebi, 2009). EEDC currently holds in excess parts inventory used to service the generation facilities. The ways of handling its inventory has failed to cope with factors like stochastic demands, better service levels, and shorter lead times and providing perfect heuristics for inventory-related decision making (Onohaebi, 2009).

Therefore it is imperative to accurately forecast spare parts requirements and to optimize existing inventory policies using significant decision support (Power and Ramesh, 2017). The complex factors that enhanced the criticality of spare parts in companies can be expressed in questions such as: Are demands classified? What is the resultant effect of backorders on the demand classes? Are the customers differentiated? Are the demands and replenishment lead times stochastic or deterministic? And ultimately what does the company stand to gain, with the development of such models? The practical application of adopting a coordinated model-driven decision support approach for spare parts inventory management and control throughout the entire supply chain has the potential to simultaneously reduce service parts inventory levels and improve parts availability at all times (Onohaebi, 2009). In strong terms, the model was seen to be implemented in power generation, transmission, and distribution companies and other industries to serve the following purposes: To curb the incessant power outages by managing the inventory in a way that repair, replacement and maintenance demands are met. To have the ability to check the criticality of spare parts with the service level expected by a particular demand class (gold, silver, bronze) as well as the average number of backorder and the fill rate of those demands (Power and Ramesh, 2017).

The operating realities of Power Holding Company of Nigeria (EEDC) informed this work. The focus is to provide continuous review for service differentiated demand classes and threshold clearing mechanism using a discrete event simulation. Note also that the service differentiation here involves three demand classes which are classified as gold, silver and bronze. The study did not employ mathematical/analytical approach rather it uses the simulation approach to build a decision support model. The scope of this study did not include the analysis of costs i.e. analyzing the carrying, holding and stock-out costs. The simulation approach employed in this study is used to check the average number of backorders and the fill rate for demand classes (Onohaebi, 2009).

Electricity is one of the most important value-added commodities to the modern human society. Its importance is heightened by its becoming an integral part of the social and economic achievements. In recent years, electricity comes as a panacea to the use of petroleum products in transportation sector. Electric vehicles are now technically feasible and economically viable, and various governments have announced the specific dates to eliminate the use of petroleum based vehicles (Power and Ramesh, 2017).



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