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
1.1 Background of the Study
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.
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
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).
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).
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).
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).
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).