ABSTRACT
As the bar for service excellence keeps rising, especially in the
request of shorter lead times, higher service levels, lower costs and better
customer service support, the conventional models of spare parts inventory
control are increasingly becoming inadequate. Therefore, to tackle this
challenge, in this study, three novel models of spare parts inventory control
have been formulated, developed and packaged into a multi-model and
multi-purpose engineering computer software, called U-SPIC. Model 1 used
mathematical analysis to integrate 7 spare parts inventory policies together.
Model 2 integrated the same inventory policies of Model 1, using stochastic
simulation while Model 3 expanded Model 2 by considering bulk demand and supply
using stochastic simulation. Chi-square goodness of fit inference statistical
technique was employed in the preliminary design to check the reasonableness of
using Poisson distribution for the demands and it gave 86% success. Composite
stepwise two dimensional graphical representations of the models were
formulated, which captured the stochastic demands and stepwise state
transitions. The inverse transform algebraic method was applied for the
generation of random numbers while next event method was used for the time
advancement of the simulation clock. Traces and structured walk through were
utilized for debugging the stochastic simulation models. Batch mean method was
used in determining the confidence intervals of the simulation models, with 105
days run time and 100 replications each. The developed models results were
validated with the case study (ANAMMCO) software package called IDIS which uses
standard (r;Q) inventory policy. Beyond that, the models results were compared
via an extensive simulation approach. 19 sensitivity parameters were varied in
the study, where at each instance of variation, the behaviour of the fill rate
of demands, as well as backorders (i.e. with regard to its average number, mean
response time and maximum queue length) were analysed. On the average a saving
of 18.51% demands in comparison with the conventional models was found, which
indeed will result in huge cost savings in absolute terms. Beyond that, the
insights from these models will increase the overall efficiency of spare parts
inventory control.
CHAPTER ONE
INTRODUCTION
1.1 Spare
Parts Inventory Control - Meaning
To establish a common understanding, ‘Spare
parts’ refers to the parts requirement for keeping both owned
equipment/machine or service needs of customers in healthy operating condition
by meeting repair and replacement needs imposed by breakdown and preventive
maintenance. The term spare parts in this study therefore, is used to connote
both spare parts and service parts as applied to a firm handling both internal
and external spare and service needs. On the other hand, ‘Inventory Control’
refers to the management of the supply, storage and accessibility of
items, in this case spare parts, in order to ensure an adequate supply without
excessive supply.
Spare parts inventory models differ
substantially from regular inventory models. The key reason for this difference
is that spare parts provisioning is not an end in itself, but a means to
guarantee up-time of equipment. With respect to spare parts inventory, the
customer’s sole interest is that his systems are not down due to lack of spare
parts because equipment downtime is lost production capacity.
1.1.1 Large
Revenue and Investment on Spare Parts Inventory
In today’s
technological environment, the importance of after-sales service which
basically concerns the use of spare parts for maintenance purposes, is high.
Lost revenues due to disservice are enormous. Not only is after-sales service
valuable as a competitive advantage for manufacturers and service providers,
direct revenues in this service are also remarkably high. Companies that
provide the after-sales service have to invest a lot on spare parts inventory.
In 2006, Koudalo1 investigated revenues of spare parts in the
service business over a period of one year, and he reports combined revenues of
more than $1.5 trillion. Flint2 stated that the world’s spare
parts inventory in the aviation industry in 1995 amounted to $45 billion at
that time. Any means to downsize this stock, without decreasing customer
service, would be more than welcomed by the aviation industry. Also in other
industries, large amounts of money are invested in spare parts inventory and
this has increased over the years. Heather3 reported that the
spare parts market of U.S. represents $700 billion and 8 percent of the U.S.
gross domestic product. Many manufacturers find that profit margins for
services can top 40 percent, whereas margins for finished goods top out at
around 13 percent. Cohen et al4 and AberdeenGroup5
also report that profitability in service is much higher than profitability for
initial products. Because of these large amounts of money involved, savings of
a few percent only constitute large cost savings in absolute terms.
The above indicates that the control of
spare parts for after-sales service deserves substantial corporate attention,
which is even more true, since customer requirements have tightened.
AberdeenGroup5 indicates that 70% of the respondents in its
study have seen service response times as required in service level agreements shrinking
to 48 hours or less, and Koudalo1 states that customers keep
raising the bar for service excellence by requesting shorter lead times, higher
service levels, lower costs, and better customer service support.
1.1.2 Overview
of the Case Study
The first insight on the importance of
spare parts inventory control by the researcher was made while carrying out
another study, Okonkwo6, on stochastic queueing behaviour of
vehicles in a maintenance workshop which eventually resulted in the development
of a computer software: Ugoo Multi-Purpose Computer Qeueuing Model Simulator
(Ugoo MC-QMS).
However, the primary motivation that
finally triggered off this research is an experience with the spare parts
complex of a leading motor assembling/manufacturing company in Nigeria. The
Anambra Motor Manufacturing Company (ANAMMCO) Enugu, Nigeria – This company is
a product of a joint venture of the Federal Government of Nigeria and
Daimler-Chrysler of Germany, and was commissioned in 1980. The spare parts
complex of ANAMMCO provides considerable after-sales service which is impacted
significantly by the spare parts control. The company has a very large spare
parts complex that stores and manages spares various models of Mercedes Benz
heavy duty vehicles. Specifically, besides the selling of vehicles, the spare
parts of various models of heavy duty vehicles listed below are stored and
managed by the company.
Trucks: MB-711,
MB-1418, MB-1520, MB-1518, MB-1720, MB-1620, MB-1718, MB-1634, MB-2423
Actros: MB-2031,
MB-2035, MB-3340, MB-4031
Axor: MB-1823
Buses: MB-712,
MB-812, MB-1721, MB-O400, MB-O500
The management of these models which is
complex was further complicated by the vast number of parts required in each
model. In fact, more than 30,000 active parts needed to be controlled. The
management of these parts can only be done with the aid of a computer, hence
the spare parts complex has a computerized spare parts inventory database. Each
of the parts that is supplied or replenished is continuously keyed into the computer
and the inventory stock parameters are updated automatically.
The company uses two software for its
inventory control. The first is the Electronic Parts Catalogue (EPC) which is
used to identify the part number of the spare parts. Once the engine and
chassis number is inputted, it invokes a dialogue box from where spare parts
section is selected and from the pull down menu, the particular spare part is
chosen. The software will search and pop up the part number of the spare part,
a 3-D AutoCAD drawing of the required part, a CAD drawing guide on how it can
be fixed into the vehicle and in some cases an alternative part to be used in
case the said part is out of stock. From the part number, the location of the
spare parts in the stock room is identified. The second software is Integrated
Dealer Importer System (IDIS). It is a software that determines the stock level
for each part in the stock complex. It has a database showing the orders and
replenishments that have been made. It also indicates when to replenish and the
quantity. It uses continuous review (r,Q) inventory policy. It should also be
noted that the complex observes the well
known A-B-C classification in its spare parts inventory.
The company faces two major demands of
spare parts from the complex, the first is demand from the maintenance section
of the company. The second is from the external customers that directly buy
spare parts from the complex for their personal use. Demand from the
maintenance section is as a result of spare parts demands for maintaining their
vehicles, for maintaining after-sales service of vehicles whose owners had
service level agreement with the company as well as those that just take their
vehicles to their maintenance workshop for either regular servicing or for
repairs when they have broken down completely.
Notwithstanding the fact that the
company’s inventory system is computerized, yet the computerized system does
not observe service differentiation through rationing and demand lead time.
However, in some exceptional cases, the company observes demand lead time
manually though, But, more than ever before, this method can no longer
withstand the challenges of modern standards of spare parts inventory control.
These standards have risen to such levels that it is difficult, if not
impossible to attain it by manual form of optimization.
Therefore, this study provides improved
models which when implemented, find solution to the company’s spare parts
network challenge. These models will not only provide immediate and significant
benefit to the company under study, but can be adapted to very many other
systems.
1.1.3 Introduction
to Service Differentiation
In spare parts
inventory, just as different customers may require different product
specifications, they may also require different service levels. For instance,
for a single product, different customers may have different stockout costs
and/or different minimum service level requirements or different customers may
simply be of different importance to the supplier by similar measures.
Therefore, it can be imperative to distinguish between classes of customers
thereby offering them different services. In this setting, different product
demands from different customers can no longer be handled in a uniform way.
This, in turn, gives rise to multiple demand classes and customer
differentiation.
In this system of multiple demand
classes the easiest policy would be to use different stockpiles for each demand
class. This way, it would be very easy to assign a different service level to
each class. Also the practical implementation of this policy would be
relatively easy and will require less mathematical analysis. But the drawback
of this policy is that there is no advantage from the so-called portfolio
effect. In other words, the advantage of pooling demand from different demand
sources together would no longer be utilized. Therefore, as a result of the
increasing variability of demand, more safety stock would be needed to ensure a
minimum required service level which in turn means more inventory.
On the other side, one could simply use
the same pool of inventory to satisfy demand from various customer classes
without differentiating them. In this case, the highest required service level
would determine the total inventory needed and thus the inventory cost. The
drawback of this policy is that higher service level will be offered to the
rest of the demand classes, a deficiency that
would lead to increased inventory costs. Critical level policy essentially lies
between these two extremes. It requires complex mathematical analysis, but the
gains outweigh the task involved.
In the existing
practice, the company studied failed to exploit service differentiation (demand
classes) of the various customers. The company targets to achieve the maximum
of the service level requirements while considering the aggregated demand.
Moreover, the company does not recognize the possible demand lead times (the
difference between requested date and shipment date of the request) for lead
time orders. This study develops spare parts inventory models that recognize
the demand lead times, multiple demand classes, allow for providing
differentiated service levels through rationing, as well as optimizes the
generated policy parameters, notwithstanding the complex analysis that it
entails.
The complexities and the growing criticality of spare parts inventory
control in manufacturing and service operations are on the increase. Factors
like demand unpredictability, parts indigenization, high service levels, large
investments on and revenues from parts, the imperative to accurately forecast
spare parts requirements and to optimize existing inventory policies require
significant decision support. This decision support can only be achieved from
the results generated from more efficient novel decision models.
Unfortunately, many researchers from the
third world shy away from developing this type of models. Those who delve into
it limit themselves to the development of spare parts inventory control
database, using conventional models. These conventional models are increasingly
becoming ineffective in tackling spare parts inventory control problems. On the
other hand, the advanced countries that have done a lot of work with regards to
developing novel spare parts inventory control models have not been able to
integrate either the 7 spare parts inventory policies as was done in Models 1
and 2 of this study, or 9 spare parts inventory policy as was done in Model 3
of this study, in any of their developed models. The spare parts inventory
policies are listed in the objective of the study in section 1.3.
The
objective of the study embraces the following:
Ø
Development of a novel analytical model (Model
1)
This integrates 7 spare parts inventory
policies together. The policies are continuous review, one to one lot, service
differentiation and rationing, backordering, demand lead time, priority
clearing mechanism and bounded enumerative optimization.
Ø
Development of novel stochastic
simulation models (Models 2 and 3)
Model 2 integrates the same 7 spare
parts inventory policies of model 1 using stochastic simulation while model 3
expands model 2 by considering in addition to the to the policies of modal 2,
bulk demand and bulk replenishment of spare parts using stochastic simulation.
Ø
Showcasing new insight in the behaviour
of backorders of spare parts inventory This is with regards
to its maximum queue length, mean response time and average
number in the system.
Ø
Establishment of the magnitude of cost
savings
This is
done by the
application of service
differentiation
through
rationing and demand lead time.
Ø
Formulation of composite graphical
representations of the models
This
is for pedagogical purposes.
Ø
Proposal of the models to the Management
of ANAMMCO
This
is for possible interfacing with their already existing computer spare parts
inventory model.
Ø
Uploading the active software to the
internet
This
is for easy subscription, access and run, from any part of
the world.
The envisaged significance of this study
is laid on its applied nature mainly. That is, the output results from these
models have foreseeable potentialities for immediate practical applications to
the on-going challenge of achieving above 99% service level at minimum stock.
Specifically, the models can easily be
applied in spare parts inventory control Industries/Companies and even
Institutions, for the following purposes:
1.
The models can be applied to the management
of the spare parts inventory system, requiring both preventive and breakdown
demands of spare parts.
2.
Industries that have contractual agreements
for servicing machines/vehicles/airplanes with some of its customers can also
find the models very useful for the management of its inventory. An example is
an airline industry that has contractual agreement with its major and minor
airlines of differentiated service levels.
3.
Spare parts inventory systems that do not
recognize both service differentiation and positive demand lead time can
equally make use of these models. What is required is just to remove the
service differentiation and demand lead time by setting critical service level
and demand lead time to zero value, accomplished through pressing few clicks on
the graphical user interface of the multi-model software package.
4.
Managing inventories for spare parts of
equipments of different criticality can make use of the models. In this case
the equipment criticality will determine the service level.
5.
The building blocks that will be provided in
this study can be adapted to solving other real-life spare parts inventory
control problems.
6.
Finally, it can be very useful as an
effective and interesting spare parts inventory pedagogical tool, both in the
academic and commercial Institutions.
1.5 Scope and Limitations
The study of the operating realities of
the Spare Parts Complex of Anambra Motor Manufacturing Company Limited
(ANAMMCO) informed this work.
The thrust was on the continuous review
inventory models, which in any case are better than periodic review for spare
parts inventory. For effective control, continuous review models require companies
whose inventory database systems are computerized.
The study did not set out to develop
database inventory models but the decision-support models using mathematical
and simulation approaches. These models will be compatible with the case study
inventory databases and indeed should be easily interfaced with standard
databases of leading software manufacturers Like Microsoft and Oracle
Corporations.
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