ArticlePDF Available

Abstract and Figures

This paper presents a framework for planning and control of the spare parts supply chain in organisations that use and maintain high-value capital assets. Decisions in the framework are decomposed hierarchically and interfaces are described. We provide relevant literature to aid decision-making and identify open research topics. The framework can be used to increase the efficiency, consistency and sustainability of decisions on how to plan and control a spare parts supply chain. This point is illustrated by applying it in a case study. Applicability of the framework in different environments is also investigated.
Content may be subject to copyright.
Maintenance spare parts planning and control: A framework for
control and agenda for future research
M.A. Driessen
, J.J. Arts
, G.J. van Houtum
, W.D. Rustenburg
, B. Huisman
Eindhoven University of Technology, School of Industrial Engineering
P.O. Box 513, 5600 MB Eindhoven, the Netherlands
Gordian Logistic Experts,
Groenewoudsedijk 63, 3528 BG Utrecht, the Netherlands
NedTrain, Maintenance Development
Delft University of Technology, Department of Software Technology
January 7, 2014
This paper presents a framework for planning and control of the spare parts supply chain in
organizations that use and maintain high-value capital assets. Decisions in the framework are
decomposed hierarchically and interfaces are described. We provide relevant literature to aid
decision making and identify open research topics. The framework can be used to increase
the efficiency, consistency and sustainability of decisions on how to plan and control a spare
parts supply chain. This point is illustrated by applying it in a case-study. Applicability of the
framework in different environments is also investigated.
Keywords: Spare parts management, System availability, Decision framework, Decision support models
1. Introduction
Many industries depend on the availability of high-value capital assets to provide their services or
to manufacture their products. Companies in these industries use capital assets in their primary
processes and hence downtime can among others result in (i) lost revenues (e.g. standstill of
machines in a production environment), (ii) customer dissatisfaction and possible associated claims
(e.g. for airlines and public transportation) or (iii) public safety hazard (e.g. military settings and
power plants). Usually the consequences of downtime are very costly.
A substantial group of companies in these industries both use and maintain their own high-value
capital assets. Examples include airlines, public transportation and military organizations. Within
these companies, a Maintenance Organization (MO) is responsible for maintaining the capital
assets. Besides maintenance activities, supply and planning of resources, such as technicians, tools
and spare parts, are required. A Maintenance Logistics Organization (MLO) is responsible for
matching the supply and demand of the spare parts required to conduct maintenance.
Corresponding author, E-mail:
Because the capital assets are essential to the operational processes of the companies involved,
downtime of the assets needs to be minimized. Downtime of a system is usually divided into (i)
diagnosis and maintenance time; (ii) maintenance delay caused by unavailability of the required
resources for diagnosis and maintenance. A high availability of spare parts is important because
it influences the maintenance delay. In this paper, we focus on the responsibility of a MLO to
minimize maintenance delay due to unavailability of required spare parts.
Our main contribution is the development of a hierarchical framework for MLO’s as described
above. This framework serves as a useful starting point in making specific designs of maintenance
spare part planning and control systems. For organizations with an existing design, the framework
has a mirror function. That is, it can be used to compare the current design of the spare parts
planning and control at a given company to our framework.
This framework can be used to increase the efficiency, consistency and sustainability of decisions
on how to plan and control the spare parts supply chain. This is demonstrated in a case study and
focus group meeting at a public transportation company that maintains rolling stock, and in in-
depth case studies at five companies that operate in different markets. We do not provide detailed
decision models for each decision function within the framework, but we do provide references to
models in the literature that can support decisions in the framework.
To decompose decisions in a hierarchical framework is a well established tradition in operations
management. Initial models consider especially the production environment (Hax and Meal, 1975;
Bitran and Hax, 1977; Hax, 1978; Bitran et al., 1981, 1982) and were motivated by the fact that it
is computationally infeasible to solve one single all encompassing model. Later it was recognized
that the hierarchy in such models is also useful because (i) in reality the power to make decisions is
distributed over several managers or agents and (ii) the information available for different decisions
has varying levels of detail (Dempster et al., 1981; Meal, 1984; Schneeweiss and Schr¨oder, 1992;
Schneeweiss, 1998, 2003; Schneeweiss and Zimmer, 2004). For the production environment, hier-
archical frameworks are now part of standard textbooks (Silver et al., 1998; Hopp and Spearman,
2001). Other successful applications include traffic control (Head et al., 1992) and supply chain
management (Schneeweiss and Zimmer, 2004; Ivanov, 2010).
To the best of our knowledge, the only other paper in which a framework for spare parts control
has been proposed is Cavalieri et al. (2008). This framework focuses exclusively on the inventory
control of spare parts and provides rules of thumb concerning decisions in the framework. We
take a broader perspective by incorporating a repair shop and its control and provide references to
state-of-the-art techniques for supporting decisions in the framework. The framework itself however
does not provide details on any of the decision function included therein. Rather, it provides a
decomposition of all the decisions and how they relate to each other.
This paper is organized as follows. §2 describes the environment we investigate and the po-
sitioning of MLO’s. §3 presents the framework and describes the decisions in the framework. In
§4, we demonstrate the applicability and benefits of our framework in multiple case studies. §5
provides relevant references for each part of the decision framework to aid in decision making and
reveals open research topics. In §6, we give concluding remarks.
2. Characterization of the environment
In the primary processes of the companies we consider, a substantial set of capital assets (asset base)
is used for multiple purposes. Over time, new assets are purchased whereas others are disposed.
Maintaining this set of assets is an important task because downtime of assets immediately affects
the primary processes. A capital asset is (partially) operational in case it is available for (a part
of) all usage purposes and a capital asset is down whenever it is in maintenance or waiting for
maintenance to be conducted. Maintenance is either planned or unplanned and it is conducted
within the constraints of the maintenance policy/program.
When an asset is maintained, parts are taken out and replaced by ready-for-use (RFU) parts
based on their condition. This principle is called repair-by-replacement (Muckstadt, 2005). Spare
parts used at the first level maintenance are called Line Replaceable Units (LRU’s) (Muckstadt,
1973, 2005). The decision to designate a part as a LRU lies with the maintenance organization.
LRU’s that are taken out are either scrapped or sent to a repair shop for repair. Repaired parts
are sent back to a ready-for-use LRU stocking location where they can be used again to replace a
MLO’s try to find the optimal balance between spare parts availability, working capital and
operational costs, within their span of control. Several tasks need to be conducted and decisions
need to be taken in order to achieve the desired spare parts availability, possibly under constraints
of working capital and/or operational costs.
In this section, an outline is given of the environment in which MLO’s operate. First we
characterize the process of maintaining the capital assets. Second we discuss the spare parts supply
chain and we end with the characterization of spare parts demand.
2.1 Characterization of system maintenance
The MO’s we consider maintain a set of high-value capital assets. Examples of such asset bases
include fleets of airplanes, trains, weapon systems, and manufacturing equipment. Maintenance on
a capital asset is conducted according to a maintenance policy/program, or a modification plan.
We distinguish three types of maintenance:
Preventive maintenance: maintenance that is conducted in order to prevent failure. Usually
this maintenance is planned some time in advance and has to be conducted within a registered
time frame during which the asset is in non-operating condition.
Corrective maintenance: maintenance that is conducted after a failure has occurred. Correc-
tive maintenance can be partially planned when it involves a non-critical part whose mainte-
nance can be delayed.
Modificative maintenance: maintenance conducted to improve the performance of the capital
asset. This maintenance can usually be delayed until all resources are available.
Working place
capacity planning
capacity planning
Work order release
job 1 job n...
Working place
capacity planning
capacity planning
Work order release
job 1 job n...
Work orders
maintenance depot 1
Spare parts
Work orders
maintenance depot 2
Figure 1: Hierarchical planning framework for maintenance of high-value capital assets.
Figure 1 presents a hierarchical planning framework for maintenance of capital assets. The figure
is to be read top-down. Work orders generate demand for LRU’s and other resources (technicians,
tools, equipment) needed to conduct the maintenance. A (part of a) work order is released as soon
as all resources and all required LRU’s to start (a part of) the work order are available. Unreleased
work orders are queued, until they are released. The MLO is responsible for the availability of
LRU’s needed to conduct system maintenance, the MO is responsible for all other resources.
2.2 Maintenance spare parts supply chain overview
We consider organizations in which the supply chain already exists, i.e. location and size of ware-
houses are predetermined. The spare parts supply chain is in general a multi-echelon system. We
distinguish two types of spare parts:
1. Repairable parts: parts that are repaired rather than procured, i.e. parts that are technically
and economically repairable. After repair the part becomes ready-for-use again.
2. Non-repairable parts or consumables: parts which are scrapped after replacement.
Consumable LRU’s need to be replenished from outside suppliers, whereas repairable LRU’s are
sent to a repair shop. Repairable LRU’s are usually repaired by replacing one or more lower level
parts. These lower level parts are called Shop Replaceable Units (SRU’s). SRU’s, like LRU’s, can
either be consumable or repairable and need to be replenished from external suppliers/repair shops
or an internal repair shop, respectively. Hence there are multiple levels of repair.
In general, there are multiple first level maintenance depots with associated spare part stocks.
The spare parts supply chain is a multi-echelon divergent supply chain with multiple repair shops.
Furthermore, the supply chain for repairable spare parts has a closed-loop. When demand for
a LRU cannot be met from local stock, emergency procedures such as lateral transshipments or
emergency shipments from upstream stocking locations may be applied.
Repair shop
Operational use
New projects and modifications
repair shops
Maintenance logistics organization Maintenance organization
flow of “ready-for-use" LRU's
flow of parts that need to be inspected and/or repaired
flow of parts that cannot be repaired and need to be scrapped
Figure 2: Example of a maintenance spare parts supply chain
Figure 2 presents a typical example of a spare parts supply chain within companies that both
use and maintain high value capital assets. A central stocking point of spare parts supplies several
local stocking points that are incident to the first level maintenance sites. There is also a stocking
point of parts that still need to go to repair and a stocking point of parts required for new projects
and modifications that occur during the life cycle of a capital asset. In practice, these stocking
points are often in one and the same warehouse. For control reasons these stocks are distinguished.
2.3 Demand characteristics of maintenance s pare parts
As mentioned in §2.1, maintenance on a capital asset generates demand for LRU’s. The MO
requests the required LRU’s at the MLO by creating spare parts orders. The LRU’s are delivered
from the stocking location incident to the requesting maintenance depot. Each type of maintenance
on the capital assets generates demand for LRU’s in a different way.
Modificative and planned preventive maintenance generate spare parts orders some time before
the desired start of the maintenance. These spare parts requirements are known and fixed. The
required LRU’s are requested by the MO with a desired lead time, i.e. delivery lead time.
Corrective and inspection based preventive maintenance generate unplanned spare parts orders.
The MO updates its maintenance planning and orders the required spare parts at the MLO. The
desired delivery date for an LRU is equal to the start date of the maintenance.
Typically, maintenance depots and MLO’s make agreements on specified upper/lower bounds
for key performance indicators such as (i) the average work order delay due to unavailability of
spare parts, (ii) the percentage of work orders without delay (caused by unavailability of spare
parts) or (iii) the maximum “number of unfinished work orders” due to unavailability of spare
parts at any given time. Separate agreements are made on the availability of spare parts that do
not cause immediate system downtime.
3. Framework for maintenance spare parts planning and control
In this section, we present the framework for maintenance spare parts planning and control. The
construction of this framework is based on the experience of the authors in working with/in the
maintenance industry. The first author has worked as a consultant in this industry over 6 years.
The second author did his PhD research in collaboration with a maintenance company. The third
author is full professor of maintenance, reliability and quality. Throughout his career, he has worked
with many industrial partners. The fourth author has been working in this industry for about 20
years, of which 8 years as an owner of a consultancy company specialized in spare parts planning
and control. The fifth author has been working in the maintenance industry for over 15 years. The
framework is the result of triangulation between the authors.
In the remainder of this section, we present the framework. Figure 3 presents an overview of
processes and decisions in MLO’s, including their mutual connections. We distinguish eight different
processes, which are numbered one up to eight in Figure 3. Within each process, we distinguish
different decision levels. Decisions that are not made very frequently, i.e. once a year, are marked
‘S/T’ (strategic/tactical decisions); decisions made regularly, i.e. once a month or quarter, are
marked ‘T’ (tactical decisions) and decisions made frequently, i.e. once a day/week, are marked
‘O’ (operational decisions).
An arc illustrates that information, e.g. data or outcomes of decisions, flows from one process
to another. This information is needed to make decisions in subsequent processes. We emphasize
that there are many feedback loops between the various processes. For readability, these feedback
loops are left out of the figure. In §3.1-3.8, we explore each process in Figure 3 and describe in
more detail what information is passed along the arcs in Figure 3. Thus, Figures 4-11 should be
interpreted as more detailed zoom-ins of different parts of Figure 3.
The framework we provide will need refinement and alterations for every particular organization,
but it serves as a useful starting point in making specific designs of maintenance spare part planning
and control systems.
4. Supply management 5. Repair shop control
Manage supplier availability
and characteristics
Select supply source(s) and
Control supply lead time
and supply parameters
1. Assortment management
2. Demand forecasting
Classify parts with respect
to demand forecasting
Characterize demand
Define spare parts
Gather parts (technical)
6. Inventory control
Classify parts and
determine stocking strategy
Determine replenishment
policy parameters
Select replenishment policyT
8. Deployment
Define preconditions order
Manage procurement and
repair orders
7. Spare parts order handling
Define preconditions order
handling process
Manage spare parts ordersO
3. Parts returns forecasting
Classify parts with respect
to returns forecasting
Forecast part return rates
and return time
Determine repair shop
resource capacities
Schedule repair jobs
Figure 3: Overview of processes and decisions in maintenance logistics control.
3.1 Assortment management
Assortment management is concerned with the decision to include a spare part in the assortment
and to maintain technical information of the included spare parts. We emphasize that including a
part in the assortment does not necessarily mean that this part will also be stocked. The process
of managing the assortment can be found in Figure 4.
3.1.1 Define spare parts assortment
The decision to include (exclude) a part in (from) the assortment is usually taken shortly after pro-
curement (phase out) of a (sub)system and strongly depends on the maintenance policy/program.
Parts are excluded from the assortment in case its applicability has expired. There are two options
when to include a part in the assortment: before or after the first need for the part.
In case a part is included in the assortment, then there is a possibility that the part is never
needed during its lifecycle. Time spent on collecting information and adding the part to the
database has been done without any use, which results in unnecessary operational costs.
1. Assortment management
Gather parts
(technical) information
Technical information
from supplier:
Recommended Spare
Parts List (RSPL), MTBF
Maintenance policy
LRU/SRU decisions
Define spare parts
Assortment of parts
stored in a database used
for logistic purpose
Active assortment
including technical
Engineering information:
Capability list internal
repair shop,
commonality, substitution
of parts, redundancy, etc.
Figure 4: Process of managing a spare parts assortment.
However, in case a part is not included in the assortment, there are two possible adverse con-
sequences. First, when the part fails and a supplier is still available, the lead time of the part is
higher due to data collection and negotiation actions. Second, when the part is needed there may
not exist any suppliers for it anymore. In this case, the part may have to be custom made. To do
this, in many cases specialized technical information regarding the form, fit and function is needed.
If a part is not included in the assortment, this information is not available.
3.1.2 Gather parts (technical) information
Once a part is included in the assortment, (technical) information of the part is gathered and
updated when necessary. The MLO needs to decide whether or not to gather and maintain parts
technical information that is important for spare parts planning and control: (i) criticality, (ii)
redundancy, (iii) commonality, (iv) specificity, (v) substitution, (vi) shelf life, (vii) position in the
and (viii) repairability. Additionally technical information regarding form, fit and
function may be gathered. We also distinguish so called ‘insurance’ spare parts.
Parts criticality is concerned with the consequence of a part failure, that is the type of
breakdown and reaction time (Sherbrooke, 2004). Full (partial) system breakdown means
that the system is non-operational for (a part of) all assigned usage purposes. Parts that
cause full system breakdown are denoted (partially) critical. Parts that cause no system
breakdown, i.e. the system can be used for all assigned use purposes, are denoted ‘non-
Parts redundancy is the duplication of system components (parts) with the intention to
increase the reliability of the system. Information on parts redundancy decreases the number
of stocked spare parts as it is known in advance that part failure does not cause immediate
system breakdown.
The configuration is similar to the Bill of Materials. However the configuration changes throughout the lifetime
of an asset due to modificative maintenance whereas the bill of materials is a snapshot of the configuration at the
time of initial manufacture.
Parts commonality concerns parts that occur in the configuration of multiple systems that
are maintained by the MO. For each system, the MLO’s needs to meet a certain service level.
Information on parts commonality is needed for customer (system) service differentiation in
spare parts planning as well as for the decision where to stock parts, i.e. locally or centrally.
The specificity of a part concerns the extent to which a part is tailored for and used by a
customer. Parts availability at suppliers is usually low, if not zero, for specific parts and hence
this might effect the size of the buffer stock needed.
Parts are substitutional in case different parts have the same form, fit and function. This
means that requests for one part can be met by a substitute part. Information on parts sub-
stitution is used to prevent stocking parts for which requests can also be met by a substitute
The shelf life of a part is the recommended time period during which products can be stored
and the quality of the parts remains acceptable for usage. This information is used to prevent
stocking too many parts that are scrapped or revised after the shelf life of the part has expired.
The configuration is a list of raw materials, sub-components, components, parts and the
quantities of each that are currently in a system. Hence this list contains all the SRU’s and
LRU’s in the system that may require maintenance during its use. The position of a part
in the configuration is needed to determine at which level parts (SRU’s) can be replaced, in
order to repair an LRU, and what quantity of each SRU is needed. These different levels in
the configuration are also called indenture levels. The initial configuration is usually provided
by or available at the OEM and coincides with the bill of materials.
Parts repairability concerns the identification whether a part is technically repairable and
if so, whether or not the internal repair shop has the authorization (from the OEM) and
the capability to repair the part. This information is needed to determine the parts supply
Technical information on form, fit and function comes in many forms depending on the
technological nature of the part involved. Sometimes this information is of a sensitive nature
and the OEM may charge extra for this information and/or requires non-disclosure type
‘Insurance’ spare parts are parts that are very reliable, highly ‘critical’ to system availability
and not readily available in case of failure. Often these parts are far more expensive to
procure after the initial buy of the system, compared to buying at the moment of initial
system purchase. Because of their high reliability, these spare parts often will not be used
during the lifetime of the system. Example of an ‘insurance’ part is the propeller of a ship.
Parts (technical) information is sometimes provided by the OEM. However, it is also possible
that the MLO needs to determine this technical information. All the technical information is used
2. Demand forecasting
Classify parts with
respect to demand
Assortment of parts with
planned demand
maintenance policy,
ADI, parts price, data
on historical planned
and unplanned
demand, active parts
assortment, installed
base, MTBF, failure
rates, reliability test
data, sensor data,
degradation of parts,
redundancy and
Classify demand to
appropriate forecasting
Characterize pattern of
planned demand
Characterize pattern of
unplanned demand
Parts with characterized
planned demand pattern
Unplanned demand forecast
per part per period
Assortment of parts with
unplanned demand
Assortment of parts with
chosen model to forecast
unplanned demand
Figure 5: Overview of the demand forecasting process.
to improve stocking decisions and manage supply risks.
3.2 Demand forecasting
Demand forecasting concerns the estimation of demand for parts in the (near) future. Future
demand for spare parts is either (partially) planned or unplanned and is characterized in §2.3.
MLO’s need to decide whether to use information about planned demand. The demand forecasting
process is visualized in Figure 5.
3.2.1 Classify parts with respect to demand forecasting
Two types of spare parts are considered: parts for which advance demand information (ADI) is used
or not. Using ADI usually decreases the overall forecast error. On the other hand, it is clear that
using ADI increases the difficulty, the effort and hence the operational costs to forecast demand
(Hariharan and Zipkin, 1995; Benjaafar et al., 2011). In case there is no information available
or it is decided not to use it, then all demand is accumulated and one single demand stream is
considered. Otherwise, two demand streams (planned and unplanned) are separated.
Within unplanned demand, another classification is made to aid the decision of using a particular
forecasting technique. Two factors that determine what methods are appropriate are the interarrival
time of demand moments and the variability of demand size. When the time between demand
moments is very long, then demand is said to be intermittent. When intermittence is combined
with variable demand sizes, demand is said to be lumpy.
3.2.2 Characterize demand process
After deciding whether or not to separate demand streams, the demand process needs to be char-
acterized on behalf of the following three purposes: (i) to determine the number of parts to stock,
(ii) to determine the repair shop capacity and (iii) to provide the necessary input for updating and
characterizing supply contracts.
Planned demand during the delivery lead time is deterministic, since all parts are requested at
least early enough in advance. Expected planned demand after this delivery lead time is not known
in advance exactly and should hence be forecasted.
Demand for spare parts could also be partially planned in advance. Parts that are needed in
about x% of some types of preventive maintenance inspections are termed x%-parts. Combining
these probabilities (x) with the information on planned inspections, a forecast for this planned
demand stream can be made.
Several methods are applicable to forecast unplanned demand. The first method to forecast
unplanned demand is reliability based forecasting. The goal of this method is to forecast parts
requirements based on part failure rates, a given installed base and operating conditions. This
method determines the failure rate of one part and extrapolates the failure rate to the installed
base and varying operating conditions.
The second method to forecast unplanned demand is time series based forecasting. Based
on known historic requirements, extrapolations are made using statistical techniques. Examples
of well-known time series based forecasting techniques are Moving average, Smoothing methods,
Croston’s method and bootstrapping (Silver et al., 1998; Croston, 1972; Willemain et al., 2004).
The advantage of time series based forecasting is that only historical demand data is needed to
forecast demand. Disadvantage is that manual changes to the demand forecasts need to be made
in case the installed base changes. The result of characterizing demand is a demand distribution
per part per period.
Technical information about substitution is used in forecasting to determine demand for new
parts that substitute old parts. Combining demand streams, for different parts that can be met by
the same spare part (i.e. substitutes), increases the overall demand forecast reliability. Technical
information on commonality of new parts is used to determine how usage in different capital assets
affects demand.
3.3 Parts returns forecasting
Parts requested by the MO’s are sometimes returned in RFU condition. In case it is not known
which part causes system breakdown, sometimes all parts that may be the cause are requested.
After it is found out which part caused the breakdown, unused RFU parts are returned to the
original stocking point within an agreed hand in time. If the requested part was a repairable, a
part is always returned that either (i) needs repair, (ii) is ready-for-use or (iii) is beyond repair and
will be scrapped. The MLO needs to account for return rates and hand in times in their planning
and control.
Consider the case of consumables. Here parts are either returned ready-for-use (with probability
) or not at all (with probability p
), see also Figure 6. The question is now whether a part
request should be considered a part demand where only part demand influences replenishment
decisions. If a procurement order is placed and the part is handed in afterwards, the inventory
levels grow unnecessarily.
The case of repairables is different, because replenishment orders cannot be released until a
failed item is sent to the MLO. Let p
denote the probability that a returned part is ready-for-
Figure 6: Overview of different part return streams.
4. Supply management
Control supply lead
time & supply
Current contract information,
supplier information, market
information, substitution of parts,
shelflife, parts specifity,
information on installed base
changes, modifications
Manage supplier
availability and
Contract lead times, constant
purchase order lead time,
agreements on handing in
times of defect repairables
minimum order quantity,
multiples, packaging units,
quantity discounts
Supply lead time per part/
supply source combination
Select supply source
and contracts
Assortment of parts for
which to find an
alternative supply source
Parts with selected
Current repair shop resource
capacity, required repair
resource capacity per repair
Figure 7: Process of managing the supply structure.
use, p
denote the probability that a returned part needs repair and p
denote the probability
that a returned part will be condemned (see Figure 6). These return fractions are used by inventory
control as follows. Requests for parts can be considered as demand and with probability p
lead time is equal to the hand in time, with probability p
the lead time is the sum of hand in
time, return lead time and the repair lead time and with probability p
the lead time is the sum
of the hand in time and the procurement lead time.
The most straightforward technique to forecast return rates is to use historic return rates,
possibly corrected for special events such as unusual accidents. For most parts, this technique is
sufficiently accurate. For some parts different failure modes often correspond to different types of
returns. Techniques from reliability engineering can be used to estimate these return rates.
3.4 Supply management
Supply management concerns the process of ensuring that one or multiple supply sources are
available to supply ready-for-use LRU’s, as well as SRU’s, at any given moment in time with
predetermined supplier characteristics, such as lead time and underlying procurement contracts
(price structure and order quantities). A process overview of supply management can be found in
Figure 7.
3.4.1 Manage supplier availability and characteristics
The process of managing supplier availability and characteristics within MLO’s is concerned with
having one or more supply sources available for each spare part in the assortment, including supply
characteristics. MLO’s have several possible supply types: (i) internal repair shop, (ii) external
repair shops, (iii) external suppliers and (iv) re-use of parts, i.e. parts that are known to become
available in a short time period caused by phase out of end-of-life systems, possibly used by other
companies. Moreover, within each supply type, it is possible to have multiple supply sources.
Each part has either one or more supply sources, one supply source that is known to disappear
within a certain time period or no available supply source at all. In the latter case, the MLO
needs to find an alternative supply source for all parts that need future resupply. Alternative
supply sources are e.g. a new supplier/repair shop, a substitute part or the part needs to become
a repairable instead of a consumable, if technically possible.
When the only supply source of a part is known to disappear, the MLO needs to decide whether
to search for an alternative supply source or to place a final order at the current supply source. The
final order decision concerns the determination of a final order quantity that should cover demand
during the time no supply source will be available (Teunter and Klein Haneveld, 1998; Van Kooten
and Tan, 2009). The supply availability for these parts is guaranteed through the available inven-
tory. Managing supply availability is also concerned with timely updating and maintaining current
contracts with external suppliers.
MLO’s also need to gather and maintain information on supply characteristics. Information
concerning the following matters is needed to determine the supply lead time (distribution) and to
select a (preferred) supply source and contract: (i) contractual or historical repair/new buy price(s)
of the part, (ii) quantity discounts (iii) contractual lead and/or repair lead time, (iv) minimum order
quantities and (v) multiples.
3.4.2 Control supply lead time and supply parameters
The supply lead time consists of: (i) repair or supplier lead time, (ii) procurement time, (iii)
picking, transport and storage time of parts and, in case of repairables, (iv) hand in times of failed
repairables. For all these components of the supply lead time, agreements are made on planned
lead times.
Using planned lead times for internal repair is justified because: (i) MLO’s make agreements
with the internal repair shop on planned repair lead times and (ii) the repair shop capacity is
dimensioned in such a way that internal due dates are met with high reliability. Using planned
lead times for external supply is justified because MLO’s agree on contractual lead times with their
external suppliers.
The supply lead time is determined for each part/supply source combination separately. We
distinguish two types of supply lead times: (i) repair lead time and (ii) procurement lead time. For
all parts that are known to be ‘technically repairable’, the MLO needs to determine the procurement
lead time, the external repair lead time and the internal repair lead time, in case internal repair is
5. Repair shop control
Schedule repair jobs
List of parts to repair
internally, estimated
offered repair load per
part, resources
required per part per
repair type, agreed
repair lead times,
agreed workload
Determine repair shop
resource capacities
Resource availability
Planning of the required
number of shifts,
number of engineers and
specialists of various
types, number of tools
of various types,
proposed planned lead
Repair job schedule,
due-date reliability
Figure 8: Overview of repair shop control process.
possible. For consumables only the procurement lead time needs to be determined.
3.4.3 Select supply source and contracts
The MLO needs to make sure that spare parts can be replenished at any given time. For this
purpose, the MLO needs to set up contracts with one or multiple supply sources in a cost efficient
way. The decision is based on the following costs incurred while selecting a supply source: (i) setup
and variable costs of the repair shop capability and resources, (ii) setup costs of the contract, (iii)
procurement or repair costs and (iv) inventory holding costs.
The MLO uses information on supply characteristics and supply lead times to select one or more
supply sources out of all possible part/supply source combinations. Important in selecting a supply
source is the decision whether to designate a spare part as repairable or consumable. Alfredsson
(1997) states: “The task of determining whether an item should be treated as a discardable (con-
sumable) or repairable item is called level-of-repair-analysis (LORA). If the item is to be treated as
a repairable item, the objective is also to determine where it should be repaired”. See also Basten
et al. (2009) and MIL (1993) for analogous definitions of LORA.
The LORA is reconsidered each year or in case of substantial changes in the asset base. The
outcome of the LORA is used to reconsider the internal repair shop resource capabilities. Note
that MLO’s need to set up a contract for repair as well as for new buy of repairables.
3.5 Repair shop control
The repair shop in the spare parts supply chain functions much like a production unit in a regular
supply chain. At the interface with supply structure management, agreements are made on lead
times for the repair of each LRU. Also agreements are made on the load imposed on the repair
shop so that these lead times can be realized. For example it is agreed to release no more than y
parts for repair during any week.
To comply with these lead time agreements, decisions are made at a tactical and operational
level. At the tactical level the capacity of the repair shop is determined and at the operational level,
repair jobs are scheduled to meet their due dates. A schematic overview of repair shop control is
given in Figure 8.
3.5.1 Determine repair shop resource capacities
When a repair job enters the repair shop, the sojourn time in the repair shop consists mostly of
waiting time for resources such as specialists, tools and SRU’s to become available. The amount of
resources that are available in the repair shop determines the waiting times. For control reasons,
these resource capacities need to be dimensioned in such a way that most repair jobs are completed
within the agreed planned lead times. Within MLO’s, internal repair lead time agreements are
made (or targets are set) in consultation with the repair shop.
Decisions need to be taken on the amount of engineers and specialists to hire, the number
of shifts and the number of tools of various types to acquire. In some instances, these tools are
themselves major capital investments. The SRU stocking decision lies outside the responsibility of
the repair shop and is part of the total inventory control decision; see §3.6 for the reasoning behind
The resource capacity dimensioning decisions are based on the estimated repair workload, the
repair workload variability (which follows from demand forecasting and parts returns forecasting)
and the estimated repair time (and variability) required for a LRU when all resources are available.
In making this decision, congestion effects need to be incorporated explicitly.
Since the costs of internal repair are mostly the result of the resources required for repair,
the dimensioning decision together with the offered repair load can be combined to estimate the
repair lead time and the cost of performing an internal repair. This information is used by supply
structure management to periodically reconsider the LORA decision.
3.5.2 Schedule repair jobs
During operations LRU’s are released to the repair shop and need to be repaired within the agreed
planned lead time. This naturally leads to due-dates for repair jobs. The repair job scheduling
function is to schedule the repair jobs subject to the resource constraints which are a consequence
of the capacity dimensioning decision. Within these constraints, specific resources are assigned to
specific repair jobs for specific periods in time so as to minimize the repair job tardiness. Addi-
tionally the repair shop may batch repair jobs to use resources more efficiently by reducing set-up
time and costs associated with using certain resources.
3.6 Inventory control
The inventory control process is concerned with the decision which spare parts to stock, at which
stocking location and in what quantities. Thus, inventory planning is done centrally for all locations
(multi-echelon approach). This includes both LRU and SRU inventory locations. The inventory
control process is visualized in Figure 9.
For control reasons, LRU’s required for new projects and modifications are planned separately.
We will not discuss the inventory control of these LRU’s in detail here.
6. Inventory control
Select replenishment
Active assortment, insurance
parts, parts criticality
Classify parts and
determine stocking
Stocking locations, supply
Select replenishment
policy parameters
Replenishment policy
selected for each part and
stocking location
Individual part stock
levels, reorder levels
and order quantity at
each stocking location
Planned replenishment lead
times, planned demand
pattern, unplanned demand
forecast, parts price, KPI
requirements, budget,
warehouse capacity
Stocking strategy for
each part
Figure 9: Process overview of controlling inventories.
3.6.1 Classify parts and determine stocking strategy
The MLO has several stocking strategies and classifies the spare parts assortment into different
subsets: (i) (partially) critical spare parts and (ii) non-critical spare parts. Insurance parts are
a specific subset of critical parts. The decision to stock insurance parts is not based on demand
forecasts or on the contribution to a certain service level, but is based on other criteria such as
supply availability, failure impact or initial versus future procurement price.
The availability of (partially) critical parts is needed to reduce system downtime. The stocking
decision of (partially) critical spare parts depends on the contribution of a part to the overall service
level of all (partially) critical parts. The availability of non-critical parts is needed for supporting
an efficient flow of system maintenance, non-availability however does not cause immediate system
downtime. Separate service level agreements are made for non-critical parts.
3.6.2 Select replenishment policy
The MLO is responsible for inventory replenishment of spare parts at all stocking points. For
batching reasons the central warehouse replenishes the local stocking points only once during a
fixed period (typically a couple of days or one week). This results in a (R, S)-policy for all parts at
the local stocking locations (Silver et al., 1998). The length of the review period is set such that
internal transport of parts is set up efficiently. In order to reduce system downtime costs, it may be
beneficial to use emergency shipments from the central warehouse or lateral transshipments from
other local stocking locations to deliver critical parts required at a local stocking point.
The MLO determines the timing and frequency of placing replenishment orders for the central
stock based on supply characteristics. Spare parts for which framework-contracts are set up are
usually delivered only once during a fixed period. Hence the stock level needs to be reviewed only
once during this period, which results in a (R, S)-policy for these parts. The stock level of other
parts is reviewed daily, resulting in an (R , s, S) or (R, s, Q)-policy for these parts (Silver et al.,
7. Spare parts order handling
Manage spare parts
preconditions order
handling process
Part requests under
Spare parts release
accepted requests
Regular or emergency
spare parts release,
request for handing in
defect repairables
Part requests
(desired quantity
and delivery date),
current inventory
Figure 10: Process of handling spare parts orders.
3.6.3 Determine replenishment policy parameters
The MLO uses different methods to determine replenishment policy parameters for non-critical
parts and (partially) critical parts. For (partially) critical parts one all encompassing model should
be used to aim at a system (multi-item) service level. Optimizing policy parameters to satisfy a
system service level is called the system approach (e.g. Sherbrooke, 2004; Muckstadt, 2005). In
maintenance logistics, this is particularly useful, because MLO’s are not interested in the service
level of one part but in the multi-item service level instead.
The model should contain the following characteristics: (i) multi-echelon, (ii) multi-item, (iii)
multi-indenture structure, (iv) emergency shipments from central depot, (v) lateral transshipments
and (vi) multiple service level criteria. Input for this model are demand forecasts and information
from supply structure management (supply lead times, parts prices), parts returns forecasts and
information on the current inventory positions and replenishment policies of the spare parts. We
note that some organizations also face budget constraints that need to be incorporated in the model.
3.7 Spare parts order handling
As discussed in §2, system maintenance work order planning and release is done locally by the
MO’s. Each MO plans its work orders based on their available resource capacities. Resources that
MO’s share are spare parts. Spare parts order handling is assigned centrally to the MLO and
consists of the following steps: (i) accept, adjust or reject the order, (ii) release spare parts on the
order and (iii) handle return order of failed repairable(s). For each of these steps, preconditions
need to be defined as well as rules to manage these steps. A process overview of handling spare
parts orders can be found in Figure 10.
3.7.1 Determine preconditions order handling process
The first decision in handling spare parts orders is to accept, adjust or reject the order. The
advantage of checking spare parts orders is that unrealistic or unusual orders can be adjusted or, in
case of incorrect orders, rejected. On the other hand, checking spare part orders is time consuming
and increases the operational costs.
8. Deployment
Manage procurement
and repair orders
Parts price, criticality,
budget constraints
Define preconditions
order process
Parts that are
manually procured
with specified
Regular and emergency
internal repair orders
Daily information,
current inventory
position, replenishment
policy parameters,
inventory pool of failed
External procurement
or repair order
Parts for which
procurement orders are
generated automatically
Orders of parts with
automatic resupply
Figure 11: Deployment process.
When checking spare part orders, the MLO obtains a trigger to contact the MO and adjust
the order lead times and/or quantities. In this manner, MO’s can reschedule certain tasks of their
system maintenance work orders and adjust their spare parts orders based on the new system
maintenance schedule. This might decrease system downtime (costs) caused by unavailability of
spare parts.
Prioritization amongst spare parts orders while releasing spare parts is not easy in case the
available stock is insufficient to meet all demand for that spare part. This is caused by the fact
that the required spare parts are (i) part of a set of spare parts needed to start a maintenance task
and (ii) are needed to start a different type of maintenance including different levels of criticality.
Thus to fill orders, spare parts order handling faces an allocation problem similar to that found in
assemble-to-order systems. The optimal solution to this problem is not generally known.
Once spare parts are released on a work order, the return process for failed repairables starts.
For this purpose, the MLO creates a return order to hand in the failed repairable by the MO within
the agreed hand in time.
3.7.2 Manage spare parts orders
Incoming spare parts orders are either automatically accepted or not, based on the preconditions
set in the previous section. There might be several good reasons for unusual or unrealistic orders,
hence there are no standard rules for accepting, adjusting or rejecting spare parts orders. This task
lies with the MLO, who needs to consult with the MO on this.
3.8 Deployment
Deployment concerns the process of replenishing spare parts inventories. The deployment process
consists of the following steps: (i) define preconditions order process and (ii) manage procurement
and repair orders. A process overview of the deployment process can be found in Figure 11.
3.8.1 Define preconditions order process
The replenishment policy parameters set by inventory control implicitly determine when to replenish
spare parts inventories and what quantities to repair or procure. Deployment may deviate from
this based on new (daily) information not known at the time the replenishment policy parameters
were set, or when exceptional repair or procure orders arise from exceptional inventory levels.
Deployment then starts a feedback loop to reconsider e.g. the demand forecast or supply lead
times that led to this exceptional inventory level. Hence, deployment sets rules for exception
management. The MLO’s should set a precondition on whether to replenish inventories with or
without interference of deployment.
3.8.2 Manage procurement and repair orders
The process of managing procurement and repair orders consists of the following steps: (i) procure
or repair parts with the right quantity and priority, (ii) check the quality of the received spare
parts and (iii) monitor supply lead times. The MLO needs to determine which quantity of each
part to order and with what priority, for parts for which the procurement or repair order is checked
upon release. The quantity deployment actually orders may deviate from the order quantity set
by inventory control, based on newly obtained information. When an order is received, the MLO
needs to check the quality of the received parts. When orders do not arrive within the agreed lead
time, deployment takes necessary recourse actions.
4. Case studies
In this section, we substantiate our claim in §1 that MLO’s can use our framework to increase the
efficiency, consistency and sustainability of decisions on how to plan and control the spare parts
supply chain. We do not make or test any other claims (such as validity or robustness) than this
one. This claim is substantiated by several case studies. We provide more details on one of these
case studies and report on a focus group meeting in which managers reflect on the comparison
of the framework with operations in their company. The focus group and in-depth case studies
support our claim that managers can use the framework to improve their operations. Applicability
is demonstrated in multiple environments.
This Section is organized as follows. In §4.1, we compare the current spare parts planning and
control structure at NedTrain to our framework and report on a focus group meeting in which
managers reflect on this comparison. In §4.2, we discuss the results of in-depth case studies of
MLO’s in several other industries.
4.1 Case study at NedTrain
NedTrain is the main provider of rolling stock maintenance and overhaul in the Netherlands and
consists of a MO and a MLO. The MLO has internal repair shops that repair parts. Hence this
company fits the environment we described in §2. The result of the comparison is a fit-gap analysis
that may be used to determine possible activities to improve the performance of the MLO.
We organized a focus group meeting with five participants of this company: the manager supply
chain control, manager repair shop control, manager maintenance development, asset configuration
manager and a supply chain manager.
4.1.1 Set up of the focus group meeting
The framework describes how to plan and control a spare parts supply chain. We made a list of
checkpoints in the form of statements for convenient comparison at any given company
. Prior
to the focus group meeting, we sent our framework and the list of checkpoints to the participants
of the focus group meeting. The goal of this focus group meeting is to find out to what extent
NedTrain deviates from these checkpoints and why. Furthermore, we want to find out whether
and how NedTrain will use the obtained insights to improve their spare parts planning process and
The focus group was led by a moderator who provided a short clarification of each checkpoint.
After this clarification, the participants discussed whether current operations are in line with the
checkpoint (framework). During this discussion, the moderator made sure that all participants
provided feedback and that the discussion time for each checkpoint was roughly equal. The par-
ticipants were then asked to assess the extent to which NedTrain operations are in line with the
checkpoint on a 5 point Likert scale
. This score was reached by consensus among the partici-
pants. In case NedTrain deviates on a checkpoint, reasons and explanations for these deviations
were formulated by the participants and summarized by the moderator. The main findings and the
scores were summarized by the moderator and sent back to participants the day after the meeting.
The summary was completed based on individual feedback of the participants. In case of feedback
contradictions, source triangulation (Yin, 2003) was used through additional phone call interviews.
4.1.2 Results of the focus group meeting
In §4.1.1, we discussed the methodology of the focus group and the evaluation of the checkpoints.
The evaluation of these checkpoints, including the scores, is listed below. The checkpoints them-
selves are written in italics and the number of the process to which a checkpoint belongs can be
found between parentheses. For the sake of brevity, we will not describe the discussion on all
checkpoints, but restrict ourselves to only eight checkpoints that best illustrate the benefits of our
framework. The discussion on the other checkpoints is available from the authors upon request.
1. Registration of spare parts (e.g. in a database) is done to simplify spare parts requests and
resupply (1). Identification of the spare parts in the asset configuration is done. These spare parts
are registered in a database. All parts that are (possibly) considered for resupply are registered in
a separate IT-system. Score: 5.
2. Spare parts are classified into two groups: parts for which advance demand information
(ADI) is used or not (2). The MO makes this classification. For the scheduled maintenance work
orders, the parts that are planned to be replaced are requested in advance. This is not done for
See Appendix A for the list of checkpoints
On this Likert scale: 1=totally not in line, 5=totally in line
cheap spare parts. Furthermore, for expensive spare parts the condition is monitored. This is not
done for the cheaper spare parts. Score: 4.
3. On behalf of a proper resupply process, spare parts supply conditions are updated on a regular
basis (4). Many contracts with suppliers of spare parts only have agreements on prices and not
on lead times. The policy to update supply conditions is reactive. Conditions are updated at the
moment of resupply. Score: 2.
4. On strategic level it is determined which spare parts to denote as ‘repairable’ and whether
or not internal repair is beneficial (4). This decision is often made once at the phase-in of the
spare parts and reconsidered on operational level. The LORA is made only occasionally. The
choice which parts to repair internally is mainly based on the knowledge about the spare parts that
NedTrain wants to obtain. Score: 3.
5. For all components of the supply lead time, agreements are made on planned lead times (4).
The external (supplier) lead time is often not based on the contractual lead time, but is an average
of realized historical lead times. NedTrain operates in a oligopolistic market in which suppliers are
not always reliable. In partnerships, NedTrain may be able to agree on reliable contractual lead
The decisions how to set base stock levels for internally repaired LRU’s and the internal repair
shop scheduling rules are integrated. The repair priority levels are based on a planned lead time of
28 days. Score: 2.
6. Repair shop control and inventory control agree on the minimum and maximum workload
that is offered at the repair shop during a period (5). No agreements are made on the expected
workload. The repair shops have many capacity flexibility options to meet fluctuations in the
workload. However, insight in the corresponding costs of capacity flexibility is lacking. If agreements
are made on the expected workload, then it may be easier to monitor these costs. Score: 1.
7. The planning and control of all stocking locations is centralized (6). The responsibility for the
planning and control is divided over various parts of the MLO. Spare parts inventories are levelled
using (lateral) transshipments in case this is needed. NedTrain thinks that the costs of decentralized
planning and control of spare parts do not outweigh the migration costs of centralization. Score:
8. The planning and control of SRU’s and LRU’s is integrated (6). The planning and control
of SRU’s and LRU’s is split up at NedTrain. The attractiveness of changing this depends on the
financial benefits. Most progress will probably be obtained when NedTrain pays external repair
shops to stock more SRU’s. Score: 1.
4.1.3 Applicability and benefits of the framework
After the focus group meeting, the participants were asked for their opinion about the applicability
and benefits of the framework. Here we quote three participants. The other two participants fully
agreed on these opinions.
Participant 1: “Applying the framework to the MLO of NedTrain shows the potential for
NedTrain to improve and on what aspects. The motivation to really change our spare parts planning
process and control strongly depends on the financial benefits though.”
Participant 2: “The framework gives us a view on the ideal world. It enables us to identify
and rank the differences between the current situation and the framework, which is our potential
to improve the performance of our MLO. The ranking enables us to discuss the right things in
our supply chain meetings. Decisions to change things according to the framework are based on
Participant 3: “The value of applying the framework is threefold: (1) it creates awareness about
the differences between the framework and the current situation; (2) it improves the communication
between departments in the MLO, the way of sharing the company strategy and uniformity in
terminology; (3) it is an important backbone to enable the possibility of benchmarking several
Although some caution should be taken when interpreting consensus among focus group par-
ticipants, we nevertheless believe that their reaction supports the claim that our framework can
be used to increase the efficiency, consistency and sustainability of decisions on how to plan and
control the spare parts supply chain.
4.2 Case studies in other industries
Besides a case study and focus group meeting at NedTrain, we also conducted in-depth case stud-
ies at five other companies that both use and maintain high value capital assets. The case study
companies operate in different industries and the assets that the companies maintain include mili-
tary equipment (Royal Netherlands Airforce, Royal Netherlands Navy, Royal Netherlands Army),
airplanes (Royal Netherlands Airlines, KLM) and container handling systems (Europe Container
Terminal, ECT). In the interest of brevity, we do not provide a detailed description. Details are
available from the authors upon request.
In these case studies, we used our framework and the list of checkpoints as a mirror in order to
compare them to the current design of spare parts planning and control processes in the companies.
The findings from each case study were shared among all other case study companies. This section
describes some interesting findings from these case studies including the actions defined by the
companies as a result from the case study.
In one of the companies SRU and LRU inventory control was not integrated. An integrated
approach is part of our framework and we explained the company about the benefits of such an
approach. This insight was a motivation for the company to investigate the possible benefits and
ramifications of an integrated approach.
In another company, no agreements on the tactical level are made between inventory control
and repair shop control, e.g. on lead times and the workload offered to the repair shop. Currently
only agreements are made on the operational level, which results in an uncontrolled internal repair
process with many interrupted repair jobs and changing repair priorities. The insight to set agree-
ments on the tactical level and the discussion on how other case study companies do this was a
reason for this company to investigate how to set agreements between inventory control and the
repair shops.
Each decision function in the framework was found in at least two of the case study companies.
The insight for companies that one or more decision functions were missing where other companies
do have this decision function, was reason for all companies to think about the necessity of including
the missing decision function in their company. Furthermore, sessions were organized in which the
companies could jointly discuss whether or not and how to fill in the decision functions. These
sessions, which can be interpreted as a multi-company focus group, further showed the benefits of
the framework and provided inter-company triangulation.
These results show that our framework is applicable in different environments. The case stud-
ies furthermore support our claim that MLO’s can use our framework to increase the efficiency,
consistency and sustainability of decisions on how to plan and control the spare parts supply chain.
5. Framework related literature and open research topics
Each decision function in the framework can be supported by several methods and Table 1 contains
several references that are a good starting point to investigate specific areas of literature in more
depth and find models to support decisions that need to be made. The first column of Table 1
contains the processes in the framework. The second and third column contain the specific topic(s)
related to the decision function and the relevant references.
Several review papers have been written on spare part management (Kennedy et al., 2002;
Guide Jr. and Srivastava, 1997; Basten and Van Houtum, 2013). These papers provide an enu-
merative review of the state-of-the-art at the time of writing. In this paper, we do not attempt
to provide an exhaustive review of contributions on these subjects. Rather, we provide relevant
references for each part of the decision framework to facilitate decision making.
Some potential research topics remain unaddressed in the literature. In the remainder of this
section, we describe four topics that are on the top of our research agenda.
Most forecasting methods, also for spare parts, are solely based on analyzing past observations.
Demand for maintenance spare parts arises from maintenance. Recent developments in condition
based maintenance (CBM) and remote monitoring (e.g. Wang and Syntetos, 2011; Heng et al., 2009)
enable us to predict the need for maintenance more carefully and in real time. The ramifications
for spare parts forecasting are not fully understood and deserve further investigation.
On the inventory control side, there are also challenges associated with this. Most spare parts
inventory models assume Poisson demand. When forecasts evolve in real time based on sensor
information, this assumption is not tenable. In essence, the information from prognostics offer
some kind of advance demand information. Leveraging this information in inventory control is not
straightforward. Consider repairables; it is usually not possible to react to this realtime information
by changing the number of spare repairables. The repair lead time of different repairable items
perhaps can be influenced more or less in real time, thus leveraging advance demand information
from prognostics. How to organize this efficiently is an open research topic.
Table 1: Literature on different decision functions in the framework
Process Topic(s) Literature
1. Assortment Management
FME(C)A (failure mode Stamatis (1995)
effects (criticality) analysis) Ebeling (1997)
Criticality, specificity, value Huiskonen (2001)
Commonality Kranenburg and Van Houtum (2007)
Reliability and quality
Oner et al. (2010)
2. Demand forecasting
Overview Altay and Litteral (2011)
Time series analysis Box and Jenkins (1970); Chatfield (2004)
Croston methods Croston (1972); Teunter and Duncan (2009)
Life data analysis of equipment Nelson (1982); Ebeling (1997)
Prognostics Heng et al. (2009)
Linking forecasting to Wang and Syntetos (2011)
maintenance planning Hua et al. (2007)
3. Parts return forecasting Scrap rates / Reliability engineering Nelson (1982); Ebeling (1997)
4. Supply management
LORA (level of repair analysis) Basten et al. (2009, 2011)
Alfredsson (1997); Barros and Riley (2001)
Contract management Van Weele (2010)
Last buy Van Kooten and Tan (2009)
Bradley and Guerrero (2009)
Teunter and Klein Haneveld (1998)
Teunter and Fortuin (1999)
5. Repair shop control
Capacity dimensioning and Iglehart (1965); Borst et al. (2004)
machine repairman models Chakravarthy and Agarwal (2003)
Scheduling / Capacity assignment Caggiano et al. (2006); Pinedo (2009)
Overtime usage Scudder (1985); Scudder and Chua (1987)
Priority assignment to jobs Scudder (1986); Guide Jr et al. (2000)
Hausman and Scudder (1982)
6. Inventory control
Review articles and books Sherbrooke (2004); Muckstadt (2005)
Kennedy et al. (2002)
Guide Jr. and Srivastava (1997)
Basten and Van Houtum (2013)
METRIC-type models (multi-echelon Sherbrooke (1968, 1986)
technique for recoverable item control) Muckstadt (1973); Graves (1985)
Lateral transshipments Lee (1987); Paterson et al. (2011)
Kranenburg and Van Houtum (2009)
Emergency procedures Alfredsson and Verrijdt (1999)
Verrijdt et al. (1998)
Finite repair capacity Sleptchenko et al. (2002); Caggiano et al. (2006)
D´ıaz and Fu (1997); Adan et al. (2009)
Empirical research and case studies Cohen et al. (1997); Bailey and Helms (2007)
Wagner and Lindemann (2008)
7. Spare parts order handling Allocation policies Ak¸cay and Xu (2004); Lu et al. (2010)
8. Deployment literature
Behavioral aspects of planning Fransoo and Wiers (2006, 2008)
Wiers (2009)
The topic in the previous paragraph raises a more fundamental topic from a control perspective.
In the framework, repair shop control and inventory control are separate processes and supply
management acts as an interface. How to arrange this interface exactly is not clear. The usual
decoupling assumed in the literature is through lead times. However, for efficient control of repair
job priorities, repair shop control and inventory control are sometimes assumed to be fully integrated
(e.g. Hausman and Scudder, 1982). How the interface between inventory control and repair shop
control should be setup is an open research question.
In many respects, a repair shop is not unlike a job shop. However, the fact that a repair shop
operates in a closed system (turn-around-stock) rather than an open system (customer orders from
outside) seems to indicate that there must be fundamental differences. These differences are not
well understood and are an open research topic.
6. Concluding remarks
In this paper, we presented a framework for maintenance spare parts planning and control for orga-
nizations that use and maintain high-value capital assets. This framework can be used to increase
the efficiency, consistency and sustainability of decisions on how to plan and control a spare parts
supply chain. The applicability and benefits of our framework are demonstrated through a case
study at NedTrain, a company that maintains rolling stock. The applicability of the framework is
also demonstrated in other companies operating in different industries. We also provided literature
to assist in decision making for different parts of the framework and identified open research topics.
The authors thank NedTrain, and especially the participants of the focus group meeting, for their
support and cooperation in our case study research. The authors also thank the other case study
companies for their cooperation.
We thank the anonymous referees and J¨urgen Donders for their invaluable input on the frame-
work itself and its presentation.
A. List of checkpoints
This Appendix presents the list of checkpoints used in our case study research. The number of the process to which
the checkpoint belongs can be found between parentheses.
Registration of spare parts (e.g. in a database) is done to simplify spare parts requests and resupply (1).
All spare parts are registered in a central database (1).
Parts for which replenishment is no longer required (as no demand will occur) are removed from the database
The MLO consciously collects technical information that can be used for the control of the spare parts supply
chain (1).
A demand forecast is made in order to facilitate the spare parts supply chain planning (2).
Spare parts are classified into two groups: parts for which advance demand information (ADI) is used and for
which it is not used (2).
Reliability based forecasting techniques are used to forecast demand (2).
A forecast is made for the expected number of items that return unused for each spare part, in order to improve
the spare parts supply chain planning (3).
A scrap rate is registered/determined and used to improve the spare parts supply chain planning (3).
On behalf of a proper resupply process, spare parts supply conditions are updated on a regular basis (4).
On strategic level it is determined which parts to denote as ‘repairable’ and whether or not internal repair is
beneficial (4).
For all components of the supply lead time, agreements are made on planned lead times (4).
The repair of a set of spare parts is done in-house (5).
The repair shop capacity dimensioning decision depends on the demand forecast for spare parts and the agreed
workload for the repair of repairable items (5).
Repair shop control and inventory control agree on the minimum and maximum workload that is offered at
the repair shop during a period (5).
Inventory control makes agreements on tactical level with the repair shop about planned repair lead times (5).
Inventory control/Deployment makes agreements on the operational level with the repair shop on setting
priorities for repairs (5).
The planning and control of all stocking locations is centralized (6).
The planning and control of SRU’s and LRU’s is integrated (6).
A system approach is used to optimize/determine the inventory policy parameters (6).
Preconditions are set for handling spare parts orders (7).
Failed repairable item returns are registered and monitored in a spare parts order, which means that a spare
parts order is closed as soon as the failed item is returned by the MO (7).
Preconditions are set on the replenishment of spare parts (e.g. levels of authorization, automatic replenishment
of spare parts) (8).
I.J.B.F. Adan, A. Sleptchenko, and G.J. Van Houtum. Reducing costs of spare parts supply systems via static
priorities. Asia-Pacific Journal of Operatonal Research, 26(4):559–585, 2009.
Y. Ak¸cay and S.H. Xu. Joint inventory replenishment and component allocation optimization in an assemble-to-order
system. Management Science, 50(1):99–116, 2004.
P. Alfredsson. Optimization of multi-echelon repairable item inventory systems with simulataneous location of repair
facilities. European Journal of Operational Research, 99:584–595, 1997.
P. Alfredsson and J. Verrijdt. Modeling emergency supply flexibility in a two-echelon inventory system. Management
Science, 45(10):1416–1431, 1999.
N. Altay and L.A. Litteral, editors. Service Parts Management: Demand Forecasting and Inventory Control. Springer,
G.J. Bailey and M.M. Helms. Mro inventory reduction - challenges and management: a case study of the tennessee
valley authority. Production Planning & Control, 18(3):261–270, 2007.
L.L. Barros and M. Riley. A combinatorial approach to level of repair analysis. European Journal of Operational
Research, 129(2):242–251, 2001.
R.J.I. Basten and G.J. Van Houtum. System-oriented inventory models for spare parts. Beta working paper 422,
2013. Eindhoven University of Technology.
R.J.I. Basten, J.M.J. Schutten, and M.C. van der Heijden. An efficient model formulation for level of repair analysis.
Annals of Operations Research, 172(1):119–142, 2009.
R.J.I. Basten, M.C. van der Heijden, and J.M.J. Schutten. A minimum cost flow model for level of repair analysis.
International Journal of Production Economics, 133(1):233–242, 2011.
S. Benjaafar, W.L. Cooper, and S. Mardan. Production-inventory systems with imperfect advance demand informa-
tion and updating. Naval Research Logistics, 58(2):88–106, 2011.
G.R. Bitran and A.C. Hax. On the design of hierarchical production planning systems. Decision Sciences, 8:28–55,
G.R. Bitran, Haas. E.A., and Hax A.C. Hierarchical production planning: a single stage system. Operations Research,
29(4):717–743, 1981.
G.R. Bitran, Haas. E.A., and Hax A.C. Hierarchical production planning: a two stage system. Operations Research,
30(2):232–251, 1982.
S. Borst, A. Mandelbaum, and M.I. Reiman. Dimensioning large call centers. Operations Research, 52(1):17–34,
G.E.P. Box and G.M. Jenkins. Time series analysis, forecasting and control. San Francisc0: Holden-Day, 1970.
J.R. Bradley and H.H. Guerrero. Lifetime buy decisions with multiple obsolete parts. Production and Operations
Management, 18(1):114–126, 2009.
K.E. Caggiano, J.A. Muckstadt, and J.A. Rappold. Integrated real-time capacity and inventory allocation for re-
pairable service parts in a two-echelon supply system. Manufacturing & Service Operations Management, 8(3):
292–319, 2006.
S. Cavalieri, M. Garetti, M. Macchi, and R. Pinto. A decision-making framework for managing maintenance spare
parts. Production Planning & Control, 19(4):379–396, 2008.
S.R. Chakravarthy and A. Agarwal. Analysis of a machine repair problem with an unreliable server and phase type
repairs and services. Naval Research Logistics, 50(5):462–480, 2003.
C. Chatfield. The analysis of time series: an introduction. Chapman & Hall/CRC, 2004.
M.A. Cohen, Y. Zheng, and V. Agrawal. Service parts logistics: a benchmark analysis. IIE Transactions, 29:627–639,
J.D. Croston. Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3):289–303,
M.A.H. Dempster, M.L. Fisher, Jansen L., Lageweg B.J., Lenstra J.K., and Rinnooy Kan A.H.G. Analytical evalu-
ation of hierarchical planning systems. Operations Research, 29(4):707–716, 1981.
A. D´ıaz and M.C. Fu. Models for multi-echelon repairable item inventory systems with limited repair capacity.
European Journal of Operational Research, 97:480–492, 1997.
C.E. Ebeling. Introduction to reliability and maintainablity engineering. McGraw-Hill, 1997.
J.C. Fransoo and V.C.S. Wiers. Action variety of planners: Cognitive load and requisite variety. Journal of Operations
Management, 24:813–821, 2006.
J.C. Fransoo and V.C.S. Wiers. An empirical investigation of the neglect of mrp information by production planners.
Production Planning & Control, 19(8):781–787, 2008.
S.C. Graves. A multi-echelon inventory model for a repairables item with one-for-one replenishment. Management
Science, 31(10):1247–1256, 1985.
V.D.R. Guide Jr. and R. Srivastava. Repairable inventory theory: Models and applications. European Journal of
Operational Research, 102:1–20, 1997.
V.D.R. Guide Jr, R. Srivastava, and M.E. Kraus. Priority scheduling policies for repair shops. International Journal
of Production Research, 38(4):929–950, 2000.
R. Hariharan and P. Zipkin. Customer-order information, leadtimes, and inventories. Management Science, 41(10):
1599–1607, 1995.
W.H. Hausman and G.D. Scudder. Priority scheduling rules for repairable inventory systems. Management Science,
28(11):1215–1232, 1982.
A.C. Hax. Aggregate production planning. In J.J. Moder and S.E. Elmaghraby, editors, Handbook of operations
research models and applications, pages 53–69. Van Nostrand-Reinhold Co, New York, 1978.
A.C. Hax and H.C. Meal. Hierarchical integration of production planning and scheduling. In M.A. Geisler, editor,
Studies in Management Sciences Vol. 1 Logistics, pages 53–69. North-Holland-American Elsevier, 1975.
K.L. Head, P.B. Mirchandani, and D. Sheppard. Hierarchical framework for real-time traffic control. Transportation
research record, 1360:82–88, 1992.
A. Heng, S. Zhang, A.C.C. Tan, and J. Mathew. Rotating machinery prognostics: State of the art, challenges and
opportunities. Mechanical Systems and Signal Processing, 23:724739, 2009.
W.J. Hopp and M.L. Spearman. Factory physics. McGraw-Hill, 2001.
Z.S. Hua, B. Zhang, J. Yang, and D.S. Tan. A new approach of forecasting intermittent demand for spare parts
inventories in the process industries. Journal of the Operational Research Society, 58:52–61, 2007.
J. Huiskonen. Maintenance spare parts logistics: Special characteristics and strategic choices. International Journal
of Production Economics, 71:125–133, 2001.
D.L. Iglehart. Limiting diffusion approximations for the many server queue and the repairman problem. Journal of
Applied Probability, 2:429–441, 1965.
D. Ivanov. An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics and
operations. International Journal of Production Research, 48(13):3999–4017, 2010.
W.J. Kennedy, J.D. Patterson, and L.D. Fredendall. An overview of recent literature on spare parts inventories.
International Journal of Production Economics, 76:201–215, 2002.
A.A. Kranenburg and G.J. Van Houtum. Effect of commonality on spare part provisioning costs for capital goods.
International Journal of Production Economics, 108:221–227, 2007.
A.A. Kranenburg and G.J. Van Houtum. A new partial pooling structure for spare parts networks. European Journal
of Operational research, 199(3):908–921, 2009.
H.L. Lee. A multi-echelon inventory model for repairable items woth emergency lateral transshipments. Management
Science, 33(10):1302–1316, 1987.
Y. Lu, J.S. Song, and Y. Zhao. No-holdback allocation rules for continuous-time assemble-to-order systems. Operations
Research, 58(3):691–705, 2010.
H.C. Meal. Putting production decisions where they belong. Harvard Business Review, 62:102–111, 1984.
STD-1390D MIL. Level Of Repair Analysis (LORA). Technical report, Military Standard, United States Department
of Defense: MIL-STD-1390D, 1993.
J.A. Muckstadt. A model for a multi-item, multi-echelon, multi-indenture inventory system. Management Science,
20(4):472–481, 1973.
J.A. Muckstadt. Analysis and algoritms for service parts supply chains. Springer, 2005.
W. Nelson. Applied life data analysis. John Wiley & Sons, 1982.
Oner, G.P. Kiesm¨uller, and G.J. Van Houtum. Optimization of component reliability in the design phase of
capital goods. European Journal of Operational Research, 205:615–624, 2010.
P. Paterson, G.P. Kiesm¨uller, R. Teunter, and K. Glazebrook. Inventory models with lateral transshipments: A
review. European Journal of Operational Research, 210(2):125 136, 2011.
M.L. Pinedo. Planning and scheduling in manufacturing and services. Springer, 2009.
C.A. Schneeweiss. Hierarchical planning in organizations: Elements of a general theory. International Journal of
Production Economics, 56-57:547–556, 1998.
C.A. Schneeweiss. Distributed decision making - a unified approach. European Journal of Operational Research, 150:
237–252, 2003.
C.A. Schneeweiss and H. Schr¨oder. Planning and scheduling the repair shops of the deutsche lufthansa ag: A
hierarchichal approach. Production and Operations Management, 1(1):22–33, 1992.
C.A. Schneeweiss and K. Zimmer. Hierarchical coordination mechanisms within the supply chain. European Journal
of Operational Research, 153:687–703, 2004.
G.D. Scudder. An evaluation of overtime policies for a repair shop. Journal of Operations Management, 6(1):87–98,
G.D. Scudder. Scheduling and labour assignment policies for a dual-constrained repair shop. Intenational Journal of
Production Research, 24(3):623–634, 1986.
G.D. Scudder and R.C.H. Chua. Determining overtime policies for a repair shop. Omega, 15(3):197–206, 1987.
C.C. Sherbrooke. Metric: A multi-echelon technique for recoverable item control. Operations Research, 16(1):122–141,
C.C. Sherbrooke. Vari-metric: Improved approximations for multi-indenture, multi-echelon availability models. Op-
erations Research, 34(2):311–319, 1986.
C.C. Sherbrooke. Optimal inventory modeling of systems: Multi-echelon techniques. Wiley, 2004.
E.A. Silver, D.F. Pyke, and R. Peterson. Inventory management and production planning and scheduling. John Wiley
& Sons, 1998.
A. Sleptchenko, M.C. van der Heijden, and A. van Harten. Effects of finite repair capacity in multi-echelon, multi-
indenture service part supply systems. International Journal of Production Economics, 79:209–230, 2002.
D.H. Stamatis. Failure mode and effect analysis. ASQC Quality press, 1995.
R. Teunter and L. Duncan. Forecasting intermittent demand: a comparative study. Journal of the Operational
Research Society, 60:321–329, 2009.
R.H. Teunter and L. Fortuin. End-of-life service. International Journal of Production Economics, 59:487–497, 1999.
R.H. Teunter and W.K. Klein Haneveld. The ‘final order’ problem. European Journal of Operational Research, 107:
35–44, 1998.
J.P.J. Van Kooten and T. Tan. The final order problem for repairable spare parts under condemnation. Journal of
the Operational Research Society, 60:1449–1461, 2009.
A.J. Van Weele. Purchasing and supply chain management. London: Cengage, 5th edition, 2010.
J. Verrijdt, I. Adan, and T. de Kok. A trade off between emergency repair and inventory investment. IIE Transactions,
30:119–132, 1998.
S.M. Wagner and E. Lindemann. A case study-based analysis of spare parts management in the engineering industry.
Production Planning & Control, 19(4):397–407, 2008.
W. Wang and A.A. Syntetos. Spare parts demand: Linking forecasting to equipment maintenance. Transportation
Research Part E: Logistics and Transportation Review, 47(6):1194–1209, 2011.
V.C.S. Wiers. The relationship between shop floor autonomy and aps implementation success: evidence from two
cases. Production Planning & Control, 20(7):576–585, 2009.
T.R. Willemain, C.N. Smart, and H.F. Schwarz. A new approach to forecasting intermittent demand for service part
inventories. International Journal of Forecasting, 20:375–387, 2004.
R.K. Yin. Applications of Case Study Research. Sage Publications, Inc., 2003.
... Life cycle cost (LCC) assessment has been employed as a typical method for evaluating economic sustainability in capital asset management [1]. As capital assets are essential in the operational processes of the organizations involved, the downtime of the assets needs to be minimized [2]. ...
... In industrial factories, in order to replace the depreciated and defective parts, the maintenance department managers use the supply chain management system (SCMS) of spare parts proportional to their demand capacity [2]. In fact, based on the reliability targets and the existing conditions, decision-making support system (DMSS), maintenance plan, and supply chain management can be utilized [3,4] so that the production line is always ready for service. ...
... Despite the advanced maintenance (corrective, preventive and predictive) measures in large and industrial complexes such as petrochemical and power plants, the equipment replacement program is very sensitive and complicated [5,6,7]. In these plants, the spare parts supply chain is a prerequisite for solving this problem [2,8]. The problem in these plants is that most of them were built more than two decades ago and after many years of operation and maintenance, their equipment must be renovated simultaneously [8]. ...
Full-text available
The management of capital assets in industrial complexes is very complicated given the large number of existing equipment in them. This becomes even more complicated when several equipment units need to be replaced simultaneously. Since the replacement program is costly and time-consuming, the critical equipment should be identified and prioritized. Thus, the main objective of this research is to develop a decision-making support system (DMSS) for prioritizing the critical equipment. A practical and scientific framework is developed for the optimal prioritization of the replacement of the critical equipment in industrial complexes. This optimization framework is multi-criteria and consists of two mathematical steps. In the first step, life cycle costing (LCC) is used to measure the economic life of the equipment and to identify the critical equipment. In the next step, the optimal time for replacing the equipment and their priorities are determined. An optimization application is designed for this purpose using an NSGA-II method and the Java software. A case study was conducted on Iran’s gas transmission operations in Khuzestan province. Out of 595 equipment available in this study, 110 units were identified as critical equipment and were prioritized.
... A Replacement-Part Supply Chain (RePtSC) comprise a network of people, equipment and technologies using information and resource (material, intellect, and finance) within organisations to perform tasks that are required to sustain overall customer utility, satisfaction, and loyalty (Driessen et al. 2014, Mcdermott et al. 2021. RePtSCs are established in pre-delivery stages (primary market) and operated in the post-delivery stages (secondary market) of a product or service life cycle (Ben Yahia et al. 2017). ...
Full-text available
Additive manufacturing underpins Industry 4.0 and is often identified as having potential applications in replacement part supply chains; however, it also introduces complex challenges for existing governance structures, especially those linked to intellectual property security concerns. This paper quantitatively surveyed views of experts in management, engineering, and academic roles about their concerns regarding intellectual property security of additive manufacturing applications in replacement part supply chains. The findings reveal that despite the often-cited benefits there remain significant concerns about this technology's application from management and security perspectives within the Industry 4.0 era.
... Spare part management consists of all decisions and planning related to spare parts from spare part ordering to storage, classification, inventory management, logistics, and other issues [3,4]. The spare part supply chain includes spare part inventory control and spare part logistics. ...
Full-text available
Spare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multiple supply periods. An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes which can determine supply time, consumption and demand. In this spare part supply chain, the supply environment is different in different periods, so the mathematical model and solution method should be able to respond to and detect the environment change quickly. Hence, a dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions so as to minimize the total cost. Furthermore, an improved self-adaptive dynamic migrating particle swarm optimization algorithm is proposed to solve the optimization problem. In this algorithm, a novel environment change detection and response strategy is applied to deal with the dynamic period in the spare part supply chain network. The results obtained show that the improved algorithm improves the computation time by eight percent and has better computational efficiency compared with the traditional algorithm.
... Since the experts and senior teams are aware of this problem, they search for ways to teach useful skills to newly graduated engineers while at the same time finding the problems within elevator spare part procurement planning process by creating a framework of spare parts decision-making steps. (Driessen, Arts, Van Houtum, Rustenburg, & Huisman, 2015). The creation of this framework is done by experts and senior teams who are responsible for the planning procurement process such as the maintenance engineers, the inventory control team, the purchase team, and the high-level managers who are the final decision-makers. ...
Conference Paper
Full-text available
This paper proposes a framework for elevator spare part procurement planning decisions in a group of airport buildings based on a Problem-based Learning (PBL) model. According to the lack of experience of newly graduated engineers in this field, they could not efficiently determine procurement planning concerning the yearly budget and several related factors. By organizing a group brainstorming team and applying the PBL model, newly graduated engineers have practiced procurement planning and discussed it with a team of senior engineers who have more experience in this field. The main discussion process has great benefits not only for junior engineers but also for senior engineers who can get some new points of view for creating the solution. The crucial criteria for elevator spare parts procurement planning, obtained from group brainstorming meetings and the PBL model, have been listed as the cost of spare parts purchasing, the lifetime of spare parts, the delivery lead times, and the warehousing management. These criteria were concluded in the procurement framework resulting in enhanced performance of procurement planning as well as the reduction of workflow.
... Tracht et al. (2013) refer that these parts are required immediately when any component of the system fails to minimize downtime and loss of production. Driessen et al. (2014) demonstrate the importance of selecting the appropriate location and number of items. Some other studies on SPM were done by Suryadi (2007), Do Rego and de Mesquita (2011), Moharana and Sarmah (2016), and Ayu Nariswari et al. (2019). ...
Full-text available
Offshore Wind Farm (OWF) downtime causes huge financial loss to the stakeholders. One of the major concerns for them is to reduce the downtime of the offshore wind turbine as much as possible. To do this, inventory managers must keep the required number of spare parts in the inventory. It is important to forecast the type and amount of spare parts ahead of time. The maintenance team tries to figure out failure symptoms to predict the approximate time for failure. This prediction helps to purchase and stock spare parts systematically. There is a trade-off between the ordering cost, holding cost, and shortage cost. Proper inventory planning saves a manager from placing expensive emergency orders and also an extended period of holding spare parts. The desired service level should be determined earlier, based on which spare parts planning is done. In this paper, some prominent spare parts models have been studied, findings have been systematically presented, compared against some key determinant factors, critical analysis has been performed and the applicability of the models has been discussed. More than a 100 research articles on spare parts have been reviewed and major contributions from the most relevant articles in OWF have been presented in this paper. One advanced spare parts modeling reported up to 51% cost reduction compared to traditional spare parts planning. Another integrated spare parts planning reported 27% savings. This critical review aims to suggest some guidelines for the managers and other associates of wind farms about the effective and efficient spare parts management technique from the beginning of the turbine installation to the end of its life cycle.
Complex capital goods such as aircraft engines are stressed by the environment and actual operation. These influences, e.g. air pollution, lead to wear of the capital goods. Therefore, capital goods are maintained, repaired or overhauled (MRO). Regeneration is carried out by MRO service providers and enables an elongation of the utilization period within the product life cycle. Within the regeneration supply chain, spare parts demands arise for MRO service providers. However, at the beginning of the regeneration supply chain, the precise spare parts demand is uncertain in terms of the required type, quantity and quality of the spare parts. This kind of information is only available after the capital good has been inspected. Often long replenishment times for spare parts lead to the challenge of providing upcoming spare parts demand for assembly in time. This paper presents an approach to dimension spare parts inventory levels forecast- and model-based that addresses the underlying uncertainty related to aircraft spare parts demand. The forecasting model is based on historical data of past regeneration orders and takes relevant features into account. Based on this, a procedure is developed for the structured evaluation of procurement opportunities with regard to their logistical potential and financial risk. The approach addresses shortcomings of existing approaches related to repairable and serviceable aero engine components that can be stocked in pool levels. Consequently, the approach supports the achievement of short delivery times and high schedule reliability in the MRO industry. In addition to a literature review identifying shortcomings in existing approaches in this field, this paper includes a case study on forecasting spare parts demand focusing on the explorative data analysis to define forecasting model requirements.
Spare parts forecasting, in general, is a complex task, due to its intermittent and erratic demand patterns. Furthermore, the underlying reason for the demand is usually not considered since most forecasting methods are based on demand history, merely. Spare parts demand is dependent on the need for replacement of components due to repair or maintenance reasons, which varies due to, e.g., life cycle, utilization of the finished product, and the number of finished products. Although machine learning methods have been more prevalent for spare parts forecasting in recent years, the prediction of initial demand, i.e., what to be stocked before the breakdowns and maintenance needs occur is understudied. So, the purpose of this paper is to investigate if the spare part need can be predicted before the demand occurs and, by that, increase the availability to the customers and decrease the cost of unavailability, e.g., expediting costs and lost sales. By adopting a machine learning model based on decision trees, called Random Forest, we predicted the probability of initial sales based on the installed base, and categorical variables such as product group, vital code, function, and weight, for spare parts at an automotive company. The analysis was made for three different markets in the Asia-pacific region and predictions were made for three different time horizons, 1, 3, and 6 months and the model performance shows potentially good results with an accuracy of around 70%. We also analyzed the business impact concerning availability and supply chain-related costs where, for example, we obtained a substantially lower total supply chain cost.
We targeted a data mining and machine learning approach for integrated maintenance/production and spare parts management problems for components of a wind farm where the level of degradation is noticeable. The degradation is established as a function of the actual functioning mode. Spare parts are stored in a local inventory. The costs associated with the supply of spare parts and their renewal are related to the functioning mode. Our goal is to use a data mining and a machine learning approach in order to optimize the total current cost of maintenance, production and spare parts related costs over a fixed planning horizon. We formulate the problem of the optimal policy structure, which turns out to be a three-threshold policy in all operating modes. Our numerical results show that the cost reductions achieved by the integrated maintenance, production and spare parts optimization are significant.
Full-text available
In the present research work the Failure Mode and Effect Analysis (FMEA) of a conventional radial journal bearing is presented. The FMEA process is applied to identify the various possible failures modes of a journal bearing and the corresponding effects of these failures on the bearing performance. The severity, occurrence and detection of the failures modes are determined based on a rating scale of 1 to 10 to quantify the relative risk of a failure and its effects on the bearing performance. The Risk Priority Number (RPN) of the failure mode is quantified and it is utilized in ranking the failure. The methods to eliminate or reduce the high-risk failure modes are proposed and experimental investigations are conducted to validate the proposed solutions.
With the pressure of time-based competition increasing, and customers demanding faster service, availability of service parts becomes a critical component of manufacturing and servicing operations. Service Parts Management first focuses on intermittent demand forecasting and then on the management of service parts inventories. It guides researchers and practitioners in finding better management solutions to their problems and is both an excellent reference for key concepts and a leading resource for further research. Demand forecasting techniques are presented for parametric and nonparametric approaches, and multi echelon cases and inventory pooling are also considered. Inventory control is examined in the continuous and periodic review cases, while the following are all examined in the context of forecasting: • error measures, • distributional assumptions, and • decision trees. Service Parts Management provides the reader with an overview and a detailed treatment of the current state of the research available on the forecasting and inventory management of items with intermittent demand. It is a comprehensive r