MATERIALS ENGINEERING
Department Maritime and Transport Technology
Delft University of Technology Mekelweg 2 2628 CD Delft the Netherlands
Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Specialization: Production Engineering and Logistics
Report number: 2014.TEL.7856
Title:
Design of a machine maintenance
information and control system,
from a continuous improvement
perspective.
Author:
R.M. Buitenhuis
Title (in Dutch) Ontwerp van een informatie- en beheersingssysteem voor machineonderhoud, vanuit het perspectief van continue verbetering.
Assignment: master thesis Confidential: no
Initiator (TUD): prof.dr.ir. G. Lodewijks Initiator (FAE): ir. M.C.A. van der Goes
Supervisor: dr. W.A.A Beelaerts-van-Blokland
MATERIALS ENGINEERING
Department Maritime and Transport Technology
Delft University of Technology Mekelweg 2 2628 CD Delft the Netherlands
Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Student: R.M. Buitenhuis Assignment type: Master thesis Supervisor (TUD): Dr. W.W.A. Beelaerts van
Blokland (TU Delft) Creditpoints (EC): 35 Supervisor (FAE) Ir. M.C.A. v.d. Goes
(Fokker Aerostructures) Specialization: PEL
Report number: 2014.TL.7856 Confidential: No
Subject:
Design of a machine maintenance information and control system,
from a continuous improvement perspective
.Introduction
Fokker Aerostructures, being a supplier in the aviation industry, strives to achieve operational excellence. As a part of this, machine maintenance is subject of research.
Well maintained machines meet the requirements that are set by production, which is needed to achieve operational excellence. The demand for maintenance may change over time, so adjusting machine maintenance is necessary, in order to ensure that machine requirements are met.
Fokker proposes the TPM as a suitable methodology to improve machine maintenance, but attempts to introduce first line maintenance – which is a part of TPM – haven’t been successful. The goal of this research is therefore to design a suitable methodology or system to improve machine maintenance. Problem definition
MATERIALS ENGINEERING
Department Maritime and Transport Technology
Delft University of Technology Mekelweg 2 2628 CD Delft the Netherlands
Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Research question
The following research question has been formulated:
How to design a machine maintenance information and control system, from a continuous improvement perspective?
A) Adding a formalized continuous improvement process to existing operations.
B) Coupling this continuous improvement process to operations, by means of data and information on machine level.
Execution
The research will be executed as following: 1 Literature research covering;
a. The demand for machine maintenance. b. Improvement processes.
2 Analysis of the current machine maintenance improvement system within Fokker, focusing on;
a. Existing operations systems.
b. Existing information flows within operations.
3 Design of a machine maintenance system which connects an improvement process to existing operations
I. Preface
This thesis is the final product of years of studying Mechanical Engineering, a choice of study that was never questioned. When I started graduating at Fokker Aerostructures, people asked whether I was planning to build aircrafts, especially since I’ve also done an internship in the aviation industry. This research however isn’t focused on building aircrafts, it’s about machines, something that has had my interest for many years.
Fokker Aerostructures has given me the opportunity to do a masters research concerning machine maintenance, for which I am grateful. I would like to thank Martijn van der Goes as Manager Operations Engineering for allowing a student to research this subject in the context of Fokker, and giving the subsequent supervision during the assignment. I’d like to thank Leo van Vark as Manager Technical Service Center for guiding the research, and creating a social and no-nonsense atmosphere in the office, together with Arian, Perkas and Roelof. During the last months of this research, Richard joined the team, whom I thank for giving his critical and unbiased opinion.
Although this research has been executed within Fokker, it is the completion of my master study at Delft University of Technology. The chairman of my graduation committee is therefore professor Lodewijks, whom I’d like to thank for his keen input during meetings. My supervisor, dr. Beelaerts van Blokland has always been very interested, and sent me in the right directions whenever I lost track of what I was doing.
Doing this research has been an interesting process, and I’m looking forward to the next step; finally using mechanical engineering in this society, instead of the hard benches of the Faculty 3mE.
II. Abstract
Fokker Aerostructures is a supplier of e.g. fuselage parts, wings or tails in the aviation industry. In its goal to achieve operational excellence, machine maintenance has been subject of interest. The machine park has an inherent demand for machine maintenance; it is therefore subject of continuous adjustment and improvement.
This research discusses a machine maintenance improvement process on machine and organizational level, covering the required information flows and necessary functions for improvement. No connection between this processes and existing operations has been found in literature, which is therefore subject of research within Fokker Aerostructures.
Analysis shows that no formalized and structured improvement process exists, and improvement actions are initiated by gut feelings. No formal connection exists between improvement projects and operations. The available data is limited or incomplete when compared to the required data for machine maintenance improvement.
A machine maintenance information and control system is designed from a continuous improvement perspective. Both the machine level, as well as the organizational level is covered, using respectively an information framework; and the DMAIC cycle, which is suitable due to its data driven nature. The design has been created by adding a formalized improvement process to existing operations, which has been interconnected by means of a data framework that combines the different data types required to determine the demand for maintenance for a specific machine; being Function, Impact, Cause, Part and Action.
The steps Define, Analyze and the development of Improve are performed by a team, which consists of experts on the machine, and is executed outside regular operations. Measure and the implementation of Improve are executed within operations. Measure creates data that feeds the initiation and development of an improvement. The Control step has been expanded to a control mechanism that connects the improvement process to operations. This is showed in Figure 32.
The demand for maintenance differs per machine, which can be quantified using machine specific data. This serves as an input for the improvement process, by both initiating it, and making the development of an improvement possible. It therefore is the interconnecting aspect between operations and improvement, and is actively being guided by the control mechanism.
The model has been developed within Fokker Aerostructures, within the scope of a limited amount of departments. It is however applicable wherever continuous improvement has to be executed; the
III. Samenvatting (Dutch abstract)
Fokker Aerostructures is een leverancier van bijvoorbeeld rompdelen, vleugels of staartstukken in de vliegtuigindustrie. In het streven naar operational excellence is machine-onderhoud onderwerp van interesse; het machinepark heeft de inherente vraag naar onderhoud. Machineonderhoud is daarom onderhevig aan continue aanpassing en verbetering.
Dit onderzoek behandelt het verbeterproces van machineonderhoud op machine- en organisatieniveau, door de benodigde informatiestromen en noodzakelijke functies te behandelen. In de literatuur is geen verbinding gevonden tussen dit verbeterproces en de reeds bestaande handelingen; dit vormt daarom het onderwerp van dit onderzoek binnen Fokker Aerostructures. Een analyse laat zien dat er geen formeel en gestructureerd verbeteringsproces aanwezig is, en dat verbeteracties op onderbuikgevoel gestuurd worden. Er bestaat geen formele connectie tussen verbeteringsprojecten en reguliere werkuitvoering. De beschikbare data is beperkt en incompleet wanneer deze vergeleken wordt met de benodigde data voor verbetering van machine-onderhoud. Er is een ontwerp gemaakt van een informatie- en beheersingssysteem voor machineonderhoud, vanuit het perspectief van continue verbetering. Zowel het machine- als organisatieniveau zijn behandeld, waarvoor respectievelijk het dataframework; en de DMAIC1-aanpak – welke geschikt is
door de focus op data – zijn gebruikt.
Het ontwerp is tot stand gekomen door een verbeterproces te formaliseren en toe te voegen aan de bestaande bedrijfsvoering. Het verbindende element hiertussen wordt gevormd door een dataframework waarin de verschillende benodigde datatypes om de vraag naar machineonderhoud te bepalen gecombineerd worden, te weten; Functie, Invloed, Oorzaak, Onderdeel en Actie.
De stappen Definiëren, Analyseren en de ontwikkelingstap van Verbeteren worden uitgevoerd door een team van experts op de betreffende machine. Deze stappen worden buiten de reguliere processen uitgevoerd. Meten en de implementatie van Verbeteren worden wel binnen het reguliere proces uitgevoerd. Meten creëert de data die benodigd is voor het initieren en ontwikkelen van een verbetering. De stap Beheersen is uitgebreid tot een beheersingsmechanisme dat de koppeling vormt tussen het verbeterproces en de bestaande bedrijfsprocessen, zoals te zien is in Figure 32.
De vraag naar onderhoud verschilt per machine, hetgeen gekwantificeerd kan worden op basis van machinespecifieke data. Deze data dient als input voor het verbeterproces door dit zowel te initiëren
IV. Table of Contents
I. Preface ... 4
II. Abstract ... 5
III. Samenvatting (Dutch abstract) ... 6
IV. Table of Contents ... 7
V. Abbreviations ... 9
1. Introduction ...10
1.1. Fokker background ...10
1.2. Motivation for this research ...11
1.3. Scope of research ...12
1.4. Lay-out of the report...12
2. Literature and Method ...14
2.1. Machine maintenance concepts ...14
2.2. Demand for machine maintenance ...18
2.3. Relating machine maintenance concepts and demand ...21
2.4. Sub conclusions ...27
2.5. Main research question ...28
2.6. Method ...29
3. Analysis ...33
3.1. Data analysis ...33
3.2. Organizational analysis ...42
4. Design of a machine maintenance improvement model ...46
4.1. Formalized machine maintenance improvement process ...46
4.2. Integrating data framework and improvement process ...51
4.3. Design of an Integrated Machine Maintenance Improvement Process ...53
4.4. Implementation ...56
5. Conclusions ...64
Tables ...67
8. References ...68
9. Appendices ...71
9.1. Research paper ...72
9.2. The relation between failure mechanisms and applied loads. ...78
9.3. OEE and Ultimo standstill overlap ...79
9.4. Detailed redesign, including all information flows from en to production; maintenance and purchasing. ...83
9.5. Pareto analysis of sheet metal failure reports ...84
9.6. Breakdown of measuring categories for Uniport ...85
9.7. Histograms of throughput times of orders 2013 ...89
9.8. Value stream maps of Uniport and Portatec, from an information perspective ...90
V. Abbreviations
CBM Condition Based Maintenance
CMMS Computerized Maintenance Management System DMAIC Define, Measure, Analyze, Improve, Control DSA Delft Systems Approach
FMECA Failure Mode Effect and Criticality Analysis PM Preventive Maintenance
RTB Run To Breakdown SHM Sheet Metal Department TPM Total Productive Maintenance TSC Technical Service Center VSM Value Stream Map
1. Introduction
This first chapter introduces this research by giving a brief background of Fokker Aerostructures, and explaining the motivation to start this research. The scope will be discussed, focusing on what will be included in the research, and where the research will be executed. The final section of this chapter covers the layout of this report, giving a brief overview of the different chapters and contents.
1.1.
Fokker background
Fokker was a Dutch aircraft manufacturer, founded by Anthony Fokker in 1912, after successfully building his first aircraft, the ‘Spin’, see Figure 1. Before the period of the first world war Fokker produced military aircrafts such as the Fokker Dr.I (Figure 2), but during the 1920s and 1930s Fokker dominated the civil aviation market, leading to the development of the Fokker 100 (Figure 3) in the 1980s. In 1996 however, it went bankrupt. The manufacturing and maintenance departments were taken over by Stork N.V., resulting in the Stork Aerospace Group, which changed its name to Fokker Technologies in 2011. In August 2012, as part of the Stork Group refinancing, stand-alone financing for Fokker Technologies was arranged (Fokker, 2013).
Figure 1; Antony Fokker in his ‘Spin’ (1911) Fokker Technologies contains five individual business units:
Figure 2; Fokker Dr1 (1917)
The business unit Aerostructures forms the context of this research. Aeorstructes has facilities in the Netherlands (Hoogeveen, Papendrecht), Romania, Mexico and the United States. The businesslines defined by Fokker Aerostructures are; Business Jets; Large Commercial Aircraf, Landing Gears and Defense.
The operations of Fokker Aerostructures are divided over the different production facilities in; Papendrecht, Hoogeveen, and since 2012 Chihuahua (Mexico) has been added. This research has been executed within Operations Papendrecht, which has different production departments; Assembly Large Commercial; Assembly Business Jets; Sheet Metal; and Metal Bonding Glare & Chemicals. Next to these production departments two supportive departments exist; Technical Service Centre; and Technical Production Support Non-Recurring.
Figure 3; Fokker 100 from KLM (Royal Dutch Airlines). This plane had its maiden flight in 1986.
1.2.
Motivation for this research
Fokker Aerostructures is a supplier in the aviation industry, creating aerostructures like wings or tail panels for companies like Boeing or Airbus. In order to construct these parts, a set of equipment is used. This equipment is subject to maintenance, and in its goal to reach operational excellence,
purchasing process in order to investigate package handling problems. This graduation research starts at a more fundamental level in order to assess which functions and data are required in order to improve machine maintenance.
1.3.
Scope of research
This research consists of a theoretical research to the demand of maintenance, a practical research to the current state of machine maintenance improvement within Fokker, and the design of a maintenance improvement system from a continuous improvement perspective. Although the continuous improvement philosophy originated from the manufacturing industry, this research investigates its applicability in a maintenance context.
The research of the demand for machine maintenance will focus on what is known in general about the creation of the maintenance demand. This research will therefore not attempt to improve the maintenance concept of a specific machine within Fokker Aerostructures, as general knowledge is required.
The analysis of the current maintenance improvement within Fokker Aerostructures is limited to three departments; Technical Service Centre, Purchasing Department and Sheet Metal Department, of which the latter is one of the production departments. The analysis will focus on machine maintenance improvement; therefore excluding hand tools, or the improvement of the production process.
1.4.
Lay-out of the report
This report consists of three main parts, being a theoretical background, and analysis of the current state of Fokker Aerostructures and the design of an machine maintenance improvement model. The first part contains a literature research which is chapter 2. This research focuses on three main aspects; the existing ways to execute maintenance, i.e. the maintenance concepts; the demand for maintenance, i.e. why the maintenance concepts exist; and the relationship between the latter two. This theoretical research leads to the formulation of the main research question in section 2.5, of which the two major aspects are the actions and data required for machine maintenance improvement. Section 2.6 will discuss how the research question will be answered by presenting the
covering machine and organizational aspects. Finally, the conclusions will be presented in chapter 5, and recommendations will be given in chapter 6.
2. Literature and Method
In order to improve something, the subject should be understood at first. The field of maintenance will be explored in this section, focusing on three aspects; the existing ways to execute maintenance, i.e. the maintenance concepts; the demand for maintenance, i.e. why the maintenance concepts exist; and the relationship between the latter two.
The function of maintenance is to sustain the integrity of a physical asset by repairing, modifying or replacing it as necessary (Kelly, 1997). Improving maintenance therefore requires understanding and insight in the request for maintenance, which is generated by the fact that equipment may fail during use or over time. The maintenance process tries to prevent or repair this failure by means of maintenance concepts such as ‘run to breakdown’. Each maintenance concept is suitable for its own specific situation, i.e. maintenance demand. The relationship between the demand and a concept will therefore be discussed as well.
A literature research will be executed in order to gain insight in the maintenance field. This research forms the basis for the actual research, by investigating what is known and where research is required. These aspects of maintenance are covered;
• Machine maintenance concepts; The field of maintenance is investigated by discussing machine maintenance concepts like run-to-breakdown or preventive maintenance. It will be showed that the applicability of a concept depends on the demand for maintenance.
• Machine maintenance demand; The demand for maintenance is created by (the chance of) equipment failure, disabling the possibility to meet requirements. Different equipment failure modes will be discussed.
• Relating concept and demand; Creating the correct combination of demand and concepts. Choosing the right maintenance concept for a certain demand is the core of good maintenance. This research shows how this can be done, covering the required information to make such decisions.
The theoretical research gives directions for the analysis of the processes within Fokker. The required information and functions to achieve improvement arise form literature research and will therefore be
product quality (Hansson & Backlund, 2003). The different maintenance concepts will be discussed in the following sections.
2.1.1. Run to breakdown
The most basic concept is called run to breakdown (RTB), or preventive maintenance (PM). Within this concept failure is allowed, but with prior consideration (Robson, MacIntyre, & Trimble, 2013). The failed part or installation is then repaired or replaced. A very basic example is the replacement of a light bulb in a car, which is only replaced when broken. The failure of a light bulb is not critical because the function of illuminating the street is still in place due to multiple lights being installed. Replacing the bulb only when broken is the cheapest option, requiring no planning.
When applied to industrial systems, a disadvantage of this approach arises, which is the unpredictability of failures. This can create an fluctuating production capacity, due to the time required to repair a failure (Swanson, 2001).
2.1.2. Preventive maintenance
A classical, planned and scheduled approach to maintenance is called preventive maintenance. It has been defined as ‘the reduction of failures through inspection, servicing, lubrication and repair of equipment, at set frequencies’ (Slack, Chambers, & Johnston, 2007) or ‘the adjustment, calibration and repair actions, which are needed to correct or prevent failures’ (Kelly, 1997). In other words, this concept tries to prevent equipment to fail at all, instead of repairing a failure that has occurred. The prevention takes place by replacing a part or machine before it brakes down, based on a known or estimated failure frequency.
An replacement frequency based on manufacturers’ data or in-house experience is somewhat subjective and brings this technique into question because it relies heavily on the predictability of the time to failure. (Robson, MacIntyre, & Trimble, 2013). It is therefore important to use a good unit for changeover frequency (i.e. a specific usage parameter, e.g. load cycles instead of operation hours) to minimize spread on MTBF. (Tinga, 2013, pp. 241-243)
This concept has no effect when a constant failure chance is present, because replacement of a part will not decrease the probability that a part fails. Preventive replacement will only have a positive effect on failure rate if the failure chance increases with time (Hacker, 2001). PM even has a negative effect on failure rate if the failure rate decreases with time.
Within the automobile, preventive maintenance can be found in the more critical parts such as the engine. The timing belt for example should be replaced after a set interval, specified by the manufacturer, usually around 150.000 km. If omitted, the risk of failure of this belt increases, and when the belt actually snaps, the engine is normally destroyed in such a way that repair is unfeasible.
2.1.3. Condition based maintenance
The PM concept makes sure a part is replaces on a fixed interval. It is however also possible to execute maintenance actions only when equipment requires it. These actions are thus triggered by the condition of a part, therefore this concept is called condition based maintenance (CBM) (Slack, Chambers, & Johnston, 2007). In other words, this concept requires the inspection of a part, and when it is almost to the end of its life, it needs replacement. Note that in order to be able to execute this maintenance concept, a readily monitorable parameter of deterioration is needed (Kelly, 1997 p. 112). This can be found in e.g. the replacement of brake pads on a car. These are not changed after fixed number of years or kilometers, but when the padding thickness reaches a certain minimum, which indicates that the functionality will be lost soon.
An advantage of this concept is, that if such a parameter is available, this concept is suitable for random failure (Kelly, 1997 p. 114). Note however that a failure in this case can be random, but the time between noticing deterioration and actual failure should be large enough to prevent the failure from happening. The example of the brake pads does not cover this type of failure, because brake pads don’t fail random; there is a strict correlation between usage and failure. A random failure however that might occur on a car is rusting of the bodywork. Normally the bodywork is protected by paint, and will therefore not rust. A scratch – which is a random event – might however reveal the metal and start the corrosion. This is a slow process, giving the owner of the car enough time to repair the failure once the ‘parameter of deterioration’ (i.e. the rust or even the scratch) has been noticed.
2.1.4. Total productive maintenance
The maintenance concepts described so far (reactive, preventive and condition based maintenance) all attempt to fill the maintenance needs of a single component or machine by replacing or repairing it when required. The more pro-active concept called ‘Total Productive Maintenance’ (TPM) focusses not only on replacement or repair, but is a top-down management philosophy which stimulates all levels of a company to cooperate, focusing on improving installations; business operations and personnel (Lauwers). TPM consists of 8 pillars (Venkatesh, 2009);
maintenance mechanics are freed up in this way, and can spend time on more value added activities and technical repairs.
• Kaizen. This Japanese term for ‘improvement’ or ‘chance for the best’ refers to the philosophy of continuous improvement. This will be achieved according to 4 steps; measuring performance; analyzing equipment condition; improve and stabilize equipment condition; improve productivity (Lauwers).
• Planned maintenance. This pillar aims to change from using a reactive maintenance concept to using a proactive such as preventive or condition based maintenance.
• Quality maintenance. The goal here is to achieve manufacturing without any defects, by focusing on eliminating non-conformances. Understanding of what parts of the equipment influence the product quality is crucial. This pillar also induces a shift from reactive to proactive maintenance.
• Training. Executing maintenance is not only about the equipment, but also concerns the people executing it. This pillar aims to educate operators and create multi-skilled employees with a high morale, because they know why things happen, instead of only how.
• Office TPM. Maintenance is most visible on the work floor, but in order to increase the efficiency of the administrative functions and eliminate losses there, TPM should find its way to the office, by means of analyzing and improving processes and procedures.
• Safety, health and environment. The target of this pillar is to create a workplace without accidents, health damages or fires. It will play an active role in the other pillars.
2.1.5. Asset Management
Asset management broadens the scope even more than TPM, by widening the time horizon of the asset to its complete life cycle, i.e. from procurement to end-of-life-actions. It is a model for companies responsible for technical installations, using the PAS55 norm (PAS55, PAS55-1:2008 Asset Management; specification for the optimized management of physical assets, 2008), including the management of the asset portfolio, the asset systems, and the individual assets, by e.g. respectively capital investment decisions; system performance, cost and risk optimization; and asset life cycle cost analysis (PAS55, 2012)
Maintenance is a part of asset management, and planning and delivery of maintenance should be optimized to ensure that the service and performance requirements are achieved at minimum whole life cost. (PAS55, 2012 p39). Maintenance should be based on asset data and knowledge, therefore historical records should be available.
The asset management standard PAS55 provides general guidelines to asses management means and processes (Laat, 2011). Maintenance is amongst other subjects covered, but not in detail. No
2.2. Demand for machine maintenance
Equipment failure generates the request for maintenance. However, even if failure does not actually occur, but only might occur, this can generate a maintenance request, depending on the criticality of the function that will be lost.
The definition of failure is commonly stated as the loss of functionality (Blache & Shrivastava, 1994) (NEN-EN13306, 2010), or differently said; the disability to perform according to its operational specifications (BusinessDictionary, 2013). Such a failure may occur at some point in time, and has a certain cause. These two aspects characterize a failure; the mode, i.e. the probability it will occur over time, and the cause. These two aspects are discussed in the following sections.
2.2.1. Failure mode
The failure mode is the way in which a part fails in relation to age or usage of equipment. Different relationships are displayed in Figure 4 (Levitt, 1997, pp.37-38), showing the failure probability on the Y-axis and age or usage on the X-axis. In general six different relationships between failure rate and time exist (Brake, 2011) (Shohet & Paciuk, 2007) (Moubray, 2001) (Moubray, 1997) (Wireman, 2008).
Time/usage Failure rate Time/usage Failure rate Time/usage Failure rate Time/usage Failure rate
Bathtub
Initial break-in
Random
Infant mortality
Wear-out
Fatigue
A
D
E
B
choosing an appropriate unit (e.g. the steepness at the end of B gets higher) but the concepts stay in place. If for example fatigue of wings is considered, one can choose ‘time’ on the x-axis, but this will give larger spread than e.g. ‘load changes’ (Tinga, 2013).
These different patterns are discussed in turn.
A. Bathtub model; This model shows a raised failure rate during both beginning and end of life. During ‘normal’ life, a constant failure rate occurs. Examples are fan belts or bearings. Due to incorrect installation or production errors failure may occur during or shortly after commissioning. If the parts survived this period, a constant failure rate applies due to e.g. oil pollution. When the parts wear out, i.e. end-of-life, the failure rate rises again. This model is therefore actually a combination of ‘wear-out’ and ‘infant mortality’
B. Wear-out; During life the part has a constant failure rate, but after a certain period of time or use, the failure rate increases rapidly, indicating end of life. A typical example is an electrical contact such as a motor starter.
C. Fatigue; Components with a failure rate that increases steadily over lifetime follow the ‘fatigue’-pattern. There is no strict identifiable wear-out range, but the chance of failure increases constantly. This is the case when the main failure cause is e.g. corrosion or fatigue, which may occur in respectively chemical piping or springs.
D. Initial break-in; Other than an initial break-in period during which the probability of failure is relatively low, this failure pattern shows an equal likelihood of failure at any point in the asset or component’s life (Berger, 2011).
E. Random; A random failure pattern indicates a constant failure rate over the complete lifetime of a component. The failure rate therefore does not in- or decrease during time or over usage. This ‘random phase’ can also be incorporated in other failure patterns, but during the random phase, a component fails due to ‘random incidents’ such as a tire puncture or a bird strike.
F. Infant mortality; When complex equipment is assessed as a whole, this failure pattern may occur. Problems during installation raise the failure rate during commissioning, but when these problems are solved – the so called teething problems – a constant failure rate applies, due to random failure.
2.2.2. Failure cause
The failure cause is the reason why it failed, and should be subject of investigation at all time. Some likely failure causes may be found on initial use of an installation, but during lifetime this might change due to wear; a shift in usage; or different operating conditions. Gaining information about the
Failure cause categories
Many different failure causes exist, but all can be categorized into one of 5 categories; man, material, machine, method, and measurement (Gwiazda, 2006) (Young, 2005). These categories will be discussed in turn.
Man; Failures caused by men are mistakes or violations by people. A mistake is an error where the operator did not have the intention to create a failure, but it happened by accident. A violation however is intended disobeying the rules or methods that are present.
Material; The material that is being processed, forms a separate category, as the material properties may cause errors. The material can be e.g. unnecessarily hard or stiff, causing cutting tools to break. Note that the material of the machine itself falls in the category ‘machine’. This failure cause category contains more detailed failures causes on deeper levels, as discussed in the section Failure mechanisms.
Machine; The machine itself can be the cause of failure. This category therefore covers all failures inherent to the machine, such as wear or fatigue of components, or a design that doesn’t fit the needs. This failure cause category contains more detailed failures causes on deeper levels, as discussed in the section Failure mechanisms.
Method; A ‘method’ failure might seem to be a ‘man-failure’, but here the operator exactly follows the given procedure or working order, and still creates a failure. The method which is used is erroneous in this case.
Measurement; Failures caused by an erroneous measure are a separate category. An incorrect measure itself is no failure, it causes an malfunction somewhere else, and is therefore a failure cause. This failure cause category contains more detailed failures causes on deeper levels, as discussed in the section Failure mechanisms.
Failure mechanisms
that a stronger redesign of the part might prevent the failure in the future, but if the cause lies in the ‘man’ category, the solution should be sought elsewhere. If for example a mistake of the operator caused the collision, better training might be the answer.
The example above already mentioned a load type and a corresponding failure mechanism. A limited number of external load types exist, which has been discussed elaborately (Tinga, 2013). A brief summary of these load types and possible corresponding failure mechanisms is shown in Appendix 9.2.
2.2.3. Criticality of machine availability
The demand for maintenance depends not only on the inherent characteristics of equipment such as failure mode, but also on the requirements that are set for its use. If for example the requirements for a component state that it should be available only on Thursday, it is no problem that this component loses its function on Monday. It should however be repaired before Thursday, so there is still a demand for maintenance. If the requirements for this component however state that it should function at all time, the demand for maintenance has the requirements that a maintenance concept should prevent failure at all.
Determining the criticality of a failure requires combing which function is affected, and how much, i.e. the impact of the failure.
2.2.4. Sub conclusions; Maintenance demand
The demand for maintenance is a result of the inherent characteristic of equipment to deteriorate when used or over time, i.e. it might fail to fulfill its function. The relationship between usage and failure can be of six different types. If a failure occurs, it falls within one of the five categories; man; material; machine; method; or measure. If the failure is caused by an external load, it can be described in more detail by its failure mechanism. Combining these failure characteristics with the requirements set for the equipment, creates the demand for maintenance, which has to be fulfilled by a maintenance concept.
2.3. Relating machine maintenance concepts and demand
The previous sections discussed both the demand for maintenance, and the possible responses to this demand by means of maintenance concepts. Not every response will fit any demand, and therefore the matching the demand and with a correct concept is not arbitrary. How to respond on a failure depends on the failure mode and cause (Brake, 2004), as well as criticality, which has to be assessed at equipment level (Robson, MacIntyre, & Trimble, 2013). The matching process is therefore based on knowledge of the equipment itself, which is in turn based on information (Zollner, 2000). Information in turn is distilled from data (Najafi, Ahmadzedeh-Raji, Fathollai, Dadkah, & Faryadi, 2013).
2.3.1. Applicability of maintenance concepts
The three main maintenance concepts (run-to-breakdown; preventive maintenance; condition based maintenance) have different moments of replacement or repair of the equipment under consideration. This however does not mean that it is unambiguous when to apply which concept. As stated before, which concept is the correct one, depends on failure mode and the requirements of the equipment. Another aspect is that it is not always possible to execute every concept; CBM for example requires a parameter that indicates deterioration. If such a parameter is not available, this might become subject of research, but in the meanwhile another maintenance concept should be applied.
Figure 5; Example of a maintenance concept that is not suitable for the given failure mode.
An example of a poorly chosen maintenance concept is shown in Figure 5. The part suffers from ‘infant mortality’, meaning that the failure rate decreases during the first period of time, after which is it stabilizes. In this example a preventive maintenance concept is applied, using a fixed time interval. The interval however is set in a way that the part didn’t pass the initial ‘high-failure-period’; replacement took place during this period, causing the failure rate to rise. It might seem very unlikely that this combination ever occurs, but that can only be examined if the failure mode is known. Maybe some company applies this concept because it is thought that ‘this part fail often, so we replace it quite frequent’.
Time/usage
Failure rate
Re placement Re placement ReplacementFigure 6; Example of a good match between the applied maintenance concept and the failure mode.
An example of a well applied maintenance concept is shown in Figure 6, which shows the failure rate of a piece of equipment that wears out. This maintenance interval is chosen in a way that replacement takes place when the failure rate starts to rise. Doing so, the failure rate stays constant at the lowest possible level. Note that this is a very simple example of a ‘good match’, because no notion of requirements is made. In this example the failure rate remains low, but the equipment is (probably) replaced before it failed. This is desirable when the equipment should be functioning 24/7, but when a non-critical piece of equipment is considered, a run-to-breakdown concept might also suffice, and be a cheaper choice. This will introduce more downtime, but when this is acceptable within the requirements, it might be the better choice.
These examples show that a maintenance concept should be adapted to this failure mode (Levitt, 1997, p.39), which is easily seen at scheduled discard, which is only feasible if a clearly defined age exists where equipment fails (Brake, 2011). It also shows that when matching a concept to the demand for maintenance, the complete demand should be taken into account, not only the failure mode. This however fully depends on the specific piece of equipment that is under consideration, and therefore the matching should be executed for each piece of equipment.
Because both the machine and the requirements might change, due to respectively e.g. another type of use and altering governmental regulations, the demand for maintenance is not fixed. The matching therefore should be a process that continues as long as the machine is in use. Matching should consequently not be seen as a single ‘good answer’, but as an improvement of the existing match. Literature provides with multiple methods to execute this improvement process; e.g. PDCA ( Plan-Do-Check-Act); DMAIC (Define-Measure-Analyze-Implement-Control); DFSS (Design-For-Six-Sigma); RADAR (Results-Approaches-Deploy-Assess-Refine), where DMAIC and PDCA are most common. The DMAIC-cycle however is most of all a data-driven approach (Sokovic, Pavlecic, & Kern Pipan, 2010). This makes it a logical choice for the matching process. The individual steps of DMAIC will be discussed in section 2.6.3. Re placeme nt Re placeme nt
Time/usage
Failure rate
2.3.2. Required information for machine maintenance improvement
Robson (2013) addresses criticality and failure mode as determining factors for maintenance concept decisions, which also has been discussed previously. This must be evaluated on equipment level because concepts need to be appropriate for a given situation. It should however be noted that an actual design process is preceded by defining the failures (including loss of functionality and the impact it has) of the technical system under consideration (Gits, 1992). This has to be linked to criticality of these functions to choose RTB (no criticality) or a pro-active concept. Which pro-active concept applies is dependent on the failure mode and the availability of a monitorable parameter indicating the condition of a part or machine. Determining the failure mode requires failure time data of the part that failed.
The failure cause should be known in order to be able to take preventive actions (Robson, MacIntyre, & Trimble, 2013). Without knowing the cause of a failure, this cause cannot be eliminated, only its symptoms can be tackled. Learning whether the present maintenance concept is effective, requires information about the existing concept, therefore the maintenance history should be known.
In short; the following information is required; the part that failed, the function that is lost, the reason it failed, the impact this has and the action that has been taken after the failure has been noticed. These data types are discussed in more detail in the following sections.
Function
The function that has been influenced by the failure is best measured when a functional breakdown of the machine is made. This is a hierarchical structure starting with the main function, and zooming in level by level in order to achieve more levels of detail.
Impact
Quantifying the impact of a failure means looking at the effects that take place. Equipment failure impacts production, and therefore the production objectives. The production objectives are quality,
dependability, speed, flexibility and cost (Slack, Chambers, & Johnston, 2007) (Ferdows & Meyer, 1990) (Tangen, 2003). These quantities have a sand cone relationship, indicating that quality is the base for dependability, which is a combined base for speed and so forth.
Figure 7; Sandcone model, showing the relationship between the five production objectives
These objectives are influenced by a failure with the same hierarchy; e.g. a failure that impacts quality will influence all the other objectives, because quality is a requirement for the other objectives. A failure however that impacts speed, does not necessarily impact quality.
The costs generated by a failure can be divided into two groups; indirect cost due to impact on the production process and direct cost to solve the failure. When the impact of a failure is subject of interest, its direct impact should be measured, which is in this case the impact on e.g. speed, resulting in cost. The indicator ‘cost’ should therefore be omitted as a parameter to measure impact. Another aspect of using costs as a measure, is that this tends to focus on short term effects (Tsang, 1999), which is not desirable when trends are to be found.
Cause
Knowing the cause of a failure is essential in taking the right actions to prevent this failure. When the cause is not known, preventive actions might focus on symptoms instead of root causes. There might be a lucky case where a root cause is solved by accident, but improvement should be based on facts, rather than luck.
The possible failure causes are discussed in 2.2.2, and each failure should be examined to find the failure cause; if the failure cause is not known, it is not possible to prevent it in the future.
Part
Finding the failure mode of a part (e.g. a bathtub curve) requires knowing when parts fail. This on turn requires registering which part failed when an installation breaks down.
Another use of registering the part which had failed, is noticing deviations from expectations. If for example a bearing fails every 3 months, but the supplier gives a life expectancy of a year, these failures should be investigated, e.g. by means of a root cause analysis.
Action
Solving a failure requires action, ranging from resetting the installation to a complete modification, overhaul or renewal. When improving a maintenance concept, the existing concept, and therefore the actions, should be known in order to analyze its effectiveness.
Only a few actions exist when a part failed. First of all can the part be replaced by an identical part (which of course did not fail). Another option is to repair the part that failed. These cases suggest that there is no obvious cause for the failure which can be eliminated by a modification of the part. If this however is the case, the third option comes into place, i.e. modifying the failed part and re-installing it.
2.3.3. Aggregate information framework
The aspects function; impact; cause; and part characterize a particular failure, whereas ‘action’ gives information about what is done after the failure occurred. Combining the failure characterization aspects gives the framework as depicted in Figure 8.
The first three failure causes contain failures, caused by man, material or method. These are the failure categories that do not contain failures inherent to the part or machine, but are caused by external factors; the operator or the material that is used. The last two failures, measure and machine, are inherent to the machine.
The top two categories of impact, being flexibility and speed, are have least impact, whilst the bottom two, dependability and quality are the most severe.
Using these two divisions, the four quadrants all have their own characteristics and therefore initiate different types of investigation to optimize the maintenance concept. Examples are given below:
A) Review the existing instructions; are they clear and understood, or are operators working according to their own experience and insights. Are the boundary conditions such as the number of operators per machine still the same as they were when the methods or working orders were created?
B) Review the current maintenance concept. Are the boundary conditions such as the production rate still the same as they were when the maintenance concept was created? Is the failure mode still the same, or did it change due to a changing use of the machine? C) Execute a Root Cause Analysis; Investigate why the failure occurred, i.e. what is the
cause of the failure? An example of a method that can be used is the so-called 5-Why; the investigator asks the question “Why?” five consecutive times in order to get to the real cause of a problem. Analyze what can be done to prevent this in the future, whether methods have to be altered, or instructions should be given more frequent.
D) This quadrant contains severe failures that are inherent to the machine, i.e. not caused by errors of operators or inadequate methods. An FMECA (Failure Mode Effect and Criticality Analysis) or historical data analysis can give insight in the failure modes a machine has; what is the effect of a failure is; and how critical it would be if such a failure occurs. Knowing this, adequate actions or follow-up-researches can be initiated.
2.4. Sub conclusions
Sections 2.1, 2.1 and 2.3 discuss the different maintenance concepts, the demand these concepts try to fulfill and the relationship between the latter two.
It was showed that matching had to be done on an equipment-level, and could not be generalized for e.g. a complete factory or department. This matching consisted of ‘an improvement of the existing match’, and therefore the DMAIC improvement cycle was discussed, being the most data-driven improvement cycle. The required data to make maintenance decisions within such a DMAIC-process has been discussed as well, being function that is lost, impact the failure has, cause of the failure, part
Literature does not make a connection between the existing production processes in which the equipment operates, and the improvement process. The steps of the improvement process are defined, but the integration within an operating environment is left blank. Coupling the improvement process to existing operations is however necessary to guarantee its execution. This is not only necessary, but also the only option, because the measure phase of DMAIC is executed within operations.
The following section will convert this problem to the main research question of this study.
2.5. Main research question
The sub conclusions form literature research as stated in section 2.4 are the basis to formulate the main research question. It is showed that the existing improvement processes do not propose a connection between this process and an existing operations environment.
This research will investigate this connection between improvement and operations within Fokker Aerostructures, in order to create a redesign of the maintenance system, coupling operations to an improvement process. The following research question is therefore formulated:
How to design a machine maintenance information and control system, from a continuous improvement perspective?
The two relevant aspects are the improvement process and the information flows required for this process. The improvement process cannot operate as a standalone process due to the fact that ‘measure’ is inherently linked to operations. The information flows serve the goal of interconnecting each step in the DMAIC process, as well as connecting the DMAIC process to operations. This leads to the formulation of the following two sub questions:
A) Adding a formalized continuous improvement process to existing operations.
B) Coupling this continuous improvement process to operations, by means of data and information on machine level.
These sub questions broadly divide the research in two parts; A) adding actions and B) adding information. Note that ‘adding actions’ covers both the actual improvement actions, as well as a
2.6. Method
The methods used for this researched are discussed in this chapter. In paragraph 2.6.1 the modeling methods are treated, where paragraph 2.6.2 will discuss the ways of information finding. This section ends with a paragraph (2.6.3) discussing the DMAIC improvement cycle, which will be used in the design of chapter
2.6.1. Modelling
The investigation of Fokker will be done by modelling the system, which means finding the different processes and data flows. These models are used to locate the improvement actions in the current state. This section discusses which methods are used to model the production and maintenance system, as well as methods to find the different flows of data. These data flows are added to the models to gain a complete overview.
Figure 9; Simplest scheme of a function.
The relevant systems will be modeled according to the Delft Systems Approach (DSA) (Veeke, Ottjes, & Lodewijks, 2008), which is a formal method of analyzing and drawing systems. The DSA focusses on the functions (Figure 9) of the elements of a system and the control mechanisms that exist and make sure the system behaves as desired.
Figure 10; Function control
A typical system has a function, e.g. ‘use equipment to create product’. This function has an input, which is transformed to an output. This requirements and, when executed, a performance, as depicted in Figure 10.
Figure 11; Process control
A control loop, makes sure requirements are met. The control loop consists of a ‘initiate’ and ‘evaluate’ function, where the initiate function translates requirements to standards. These standards are both used during operations and in the ‘evaluate’ function to assess the performance of operations. Deviation from the standards on turn initiates the changes of standards in order to meet requirements. This loop is used to control the process by reacting upon disturbances, but it does not facilitate improvement. The innovation model which is proposed as a solution, is however focused on environmental change of the desired function of a system. (Veeke, Ottjes, & Lodewijks, 2008, pp. 169-179)
Once the functions and control mechanisms have been established, the data flow which is used can be made explicit. This means converting a term like ‘decision data’ to ‘number of failures’. The next section discusses the methods used for finding these data.
2.6.2. Information finding
The control mechanisms which are showed with the DSA use information flows. These information flows should be quantified in order to examine whether the information is correct and elaborate. The production and maintenance processes are examined by different means.
VSM
these actors on the horizontal axis. Such a model is created by finding the starting point of a process, e.g. a machine breakdown, and writing down all the actions taken by the different actors in order to come to the endpoint, e.g. a repaired machine.
2.6.3. Continuous improvement DMAIC cycle
The SIX Sigma philosophy is a data driven philosophy, using a Define, Measure, Analyze, Improve, Control (DMAIC) cycle to execute improvement actions. These steps are summarized in Figure 12 (Dreachslin & Lee, 2007) (Dedhia, 2005) (Montgomery & Woodall, 2008), and will be further explained in turn. Although the DMAIC cycle can be used in many environments with many scopes, the maintenance process has been the focus, where applicable. The DMAIC cycle is part of the Continuous Improvement philosophy (Bhuiyan & Baghel, 2005), which also provides measuring methods such as VSM and Swimlane, which will be discussed in section 2.6.2.
Figure 12; Overview of the different steps in the DMAIC process. (Dreachslin & Lee, 2007) (Dedhia, 2005) (Montgomery & Woodall, 2008)
Define; The definition phase of this process serves the goal of creating boundary conditions and identifying, prioritizing and selecting the goals and scope for the project.
Measure; Measuring serves multiple goals. The most visible goal is to gather data in a structured way, to serve maintenance decisions, which should be based upon facts and without high quality relevant data this would be impossible (Al-Najjar & Kans, 2006). Measuring however also serves as a powerful motivational tool driving decisions and action (Tsang, 1999). Without any formal measures of maintenance performance, it is difficult to plan, execute, monitor control and improve the maintenance process (Parida, 2007).
Measures have to fit the operating environment in order to be useful. Tsang (1999) summarizes 9 different studies concerning the principles of measuring, stating that good measures should be:
- Organization specific; - Multiple measures; - User-friendly;
- Different levels of hierarchy; - Involve employees;
- Encourage desired behavior and support operation; - Reviewed periodically.
Note that no single measure is sufficient to indicate the total performance of a system or even adequately reflect the whole of a performance objective (Slack, Chambers, & Johnston, 2007 p. 608)
Analyze; Once data has been gathered, it has to be converted to information, which is in turn used to gain knowledge. The required knowledge has been discussed in paragraph 2.2, and the information to come to this data has been discussed in section 2.3. The data transformation to knowledge results in a root cause, which is the input for the next phase.
3. Analysis
This chapter starts with an analysis of the available and missing data (3.1), after which follows an analysis of the current state of the relevant departments within Fokker; the production department; the technical service center; and the purchasing department. The analysis in section 3.2 consists of modeling the systems in order to achieve insight in the available functions and processes, and a comparison with the required functions to improve machine maintenance. The production processes are not analyzed in detail, because optimizing the production processes lies beyond the scope of this research. Figure 13 gives a schematic overview of this analysis.
Figure 13; Schematic visualization of analysis of current state of Fokker, by modeling and finding information at first, after which this is compared with the requirements.
3.1. Data analysis
Both an analysis of the available data and functions is executed. At first section 3.1.1 treats all available data by investigating the different departments. Section 3.1.2 then models these departments on an organizational level.
3.1.1. Available data
Data investigation starts with the production department, after which the Technical Service Center is treated. The Purchasing department is not treated separately because all relevant information also flows within the Technical Service Center.
Production Department
The information flow around production equipment has been investigated from a value stream map perspective, i.e. being on the work floor and registering what actually happens, instead of what should happen. Figure 14 shows this VSM, where the different types of data flows are found. More VSMs have been attached as appendix 9.8. No processing times or buffer capacities are added to the
Overall Equipment Efficiency; Two machines are monitored on OEE. The operator reports its activities on a 15 minute base. This results in detailed information of the time spent on different activities.
Planning; Operators use machines according to a planning, prescribing which product has to be processed.
Scrap (SC) report; When a product does not meet requirements, this is reported. This can be noticed at the machine that produced the SC/NC, or at subsequent machines in the production line. The scrap report defines where the scrap is noticed. These reports give a reason for scrap, mostly (97%) being machine related, as an analysis of the reports of the Uniport in 2013 showed. There is however only a very small correspondence between these reports and failure reports in the CMMS; only for 4.5% of the SC/NC reports there is an Ultimo report on the same date. The team leader also gathers non-conformity (NC) reports, the however does not act upon these reports, because no standards exist to determine whether the amount of NCs is good or bad.
Informal improvement idea; When operators come up with ideas for improvement, this is reported to the team leader. No formal notion is taken whatsoever.
Technical Service Center
Some equipment data however exists within the maintenance process. A swimlane analysis as depicted in Figure 15 shows which actors execute actions in the maintenance process, and what (types of) data is sent between the actors in the process. This gives an overview of the available data.
Figure 15; Swimlane analysis of the Technical Service Center2.
As shown in this model, a failure is reported in the computerized maintenance management system (CMMS) when it occurred and when it is solved. This is therefore the place to look for usable equipment data. The quality of the data that has been registered in the CMMS has been investigated further to assets its usability for maintenance decisions, i.e. it is investigated whether it is possible to convert the data to knowledge. The working orders contain several pieces of information;
The digital failure report when failure occurred contains different data, which will be discussed in turn;
Registration number; Each object within Fokker has a unique number. This does not go into the detail of the part that failed.
Object standstill Yes/No; This parameter tells whether the production department still uses the object or not. This parameter has been compared with the available
Storingsafhandeling – Swimlane analyse
Teamle ide r Ope rator Monteur Hoofdmonte ur Werkbon doorgeven We rkvoorbereide r Maint Eng Head TD Head Op . Eng Inkoop Ontvangst Unit Dire ctor Departm dir . Initiate Register Forward to hoofdmonteur Order verdelen Werk inschatten Kosten bekend? Kosten bepalen Nee €? <1000 Verdelen Repareren Ja & <1000 Afmelden Werkbon Goedkeuren Bestelling plaatsen Ontvangst melden Bestellig betalen Wacht op ontvangst GOB Bestelling ontvangen Bestelling intern doorsturen Direct uit te voeren? Nee Ja €? Goedkeuren >1000 <5000 Goedkeuren >5000 Vergunning nodig? Nee Vergunning schrijven Werkvergunning Ja Vergunning goedkeuren Ultimo storingsmeld ing Email Inkoopaanvr aag Ultimo order
standstill information of the two installations (Uniport and Portatec) where this is known due to OEE registration. The overlap between the OEE-standstill data and the Ultimo-standstill data is analyzed in appendix 9.3 and summarized in Figure 16, showing that only 1% of the number of standstills of these two machines is reported in Ultimo. In 71% of downtime occurrences, no report exists. When a report exists, it often does not show downtime.
Figure 16; Analysis of failure reports when downtime occurred for Uniport and Portatec.
Time of notification; The time at which the failure is reported is automatically registered by the CMMS.
Priority; The production department gives a priority to the failure; high; medium; or moderate. No standards however exist to assign a certain priority to a failure. The majority (67%) of orders has a high priority, whereas medium and moderate orders represent respectively 19% and 14% of the total, as shown in Figure 17. Note that reports are given a priority by the team leader of a production department, but the maintenance planner can re-prioritize a report. No standards exist for determining the priority of a failure.
2012 2013 2012 2013
Downtime, reported on order 0 1 0 0 1 1% Downtime, not on order 14 2 4 0 20 28% Downtime, no order at all 17 14 12 8 51 71%
Uniport Portatec
Time of closure; When a failure is handled, the time at which this is entered in the CMMS is registered automatically.
Failure cause; Since the end of 2012 the CMMS requires a failure cause to be entered before closing an order is allowed. An entry must be selected from the list as shown in Table 1. This list however contains mainly the identification of the part that failed, instead of the cause why it failed. Only 6 of the total of 35 options are true causes. These 6 true causes consist of three times ‘wear of part x’ and three times ‘operator input at part x’. The use of these failure causes is depicted in Table 1, which shows that the incorrect causes are most used. These data however are not analyzed in the present system. Figure 18 shows the usage of this data type in 2012 and 2013. Since 2012 it is possible to choose a failure causes from the list in Table 1, which is not always done. Only the numbers 104, 803, 301, 305, 400, 703 are true failure causes.
Table 1; Possible failure causes that can be reported in Ultimo (Fokker CMMS).
Code Storingoorzaak
200 Besturing / Softw are 100 Elektrisch Aandrijving 104 Elektrisch Bediening 106 Elektrisch Bekabeling 103 Elektrisch Componenten 105 Elektrisch Modificatie 108 Elektrisch Overige 107 Elektrisch Verlichting 800 Hydraulisch Componenten 804 Hydraulisch Filters 801 Hydraulisch Lekkage 802 Hydraulisch Modificatie 805 Hydraulisch Overige 803 Hydraulisch Slijtage 300 Mechanisch Aandrijving 301 Mechanisch Bediening 308 Mechanisch Componenten 304 Mechanisch Geometrie 306 Mechanisch Modificatie 307 Mechanisch Overige 305 Mechanisch Slijtage 500 Niet te definiëren 601 Onderhoud Meet inspectie 600 Onderhoud Periodiek 400 Operator Bedieningsfout 401 Operator Schade door derden 700 Pneumatisch Componenten
Figure 18; Analysis of the failure causes of reports in 2012 and 2013. 2012 2013 Incorrect cause 94 - 10% 814 - 71% Correct cause 39 - 4% 182 - 16% No cause 852 - 86% 148 - 13% Total orders 985 1144
From these data, Fokker weekly analyses and reviews the time related data, by means of the graphs shown in Figure 19. These graphs contain information, which has been assessed for quality and usability.
Throughput timeorder; The average throughput time of all orders is plotted each week. Both the planned and the real throughput time are plotted, although the planned time is adapted after the order has been handled. These times are not compared with the standards indicated by the priority of an order, nor are the times separately reported for each priority. Doing this however would provide insight in the performance, because these results have standards (given by priority) which can be reviewed. This has been done for the orders in 2013, resulting in histograms attached in appendix 9.7, which are summarized in Table 2. An example of such a histogram is showed in Figure 20. Although hand tools don’t fall within the scope of this research, figures have been added for the interested reader. Note that the performance that can be assessed this way, is the performance of the overall maintenance process. It does not tell anything about the maintenance of a specific machine.
Table 2; Analysis of the throughput times, separated per priority category.
Priority Hand tools Machines High (1 day) 67% 36% Medium (7 days) 12% 33% Moderate (28 days) 0% 6%
Figure 20; Histogram of the troughput times of of high priority orders of machines.
Work in progress; # orders; This number is reported, but no action is initiated by it. No differences made between priorities. No standards exist to review this number.
Output; in number of orders/wk; See ‘work in progress’.
Reliability of delivery; a percentage; The reliability of both the mechanics and the planner is plotted as a percentage, which shows the fraction of orders that is handled within the time limit that is given. This time limit can be based on the priority of the order, but often the due date is adapted because the standard time limits don’t seem realistic. Note that no distinction is made between high, medium, or moderate priority orders.
All data is gathered with Ultimo, the CMMS used by Fokker. This is an off-the-shelf software package, which therefore enforces the data structure onto the business. This in general results in data content that will be either huge or too restricted (Al-Najjar & Kans, 2006). At Fokker the result is a too narrow data content, which will be discussed in section 3.1.2 ‘Missing information’.
been compared with the required data from section 2.3.2, as summarized in the framework (Figure 21). The results of this comparison are shown in Table 3. This table shows that, although some data is available, it is mostly incomplete or limited. This is due to the fact that the data is not standardized, and therefore impossible to analyze in an efficient way; data with different formats cannot easily be recognized automatically.
Figure 21; The required information is summarized in this framework. Table 3 gives an overview of the available data.
Table 3; Overview of the current data, compared with the required data as described in the framework.
Data Available Usable In use
Function No No No
Impact Incomplete/Limited No No
Cause Incomplete/Limited No No
Part Incomplete/Limited No No
Action Incomplete/Limited No No
Table 3 shows that no usable data exists to execute analysis and drive improvement, as shown in section 3.1.1. The limited available data is not registered in a formal way, which makes it unfeasible for analysis in a structured way.
In order to gain data in all five categories, these categories have to be assessed at equipment level. This means that the categories have to be specified for each machine of interest. This data has to be usable, meaning that analysis of this data should be possible. Data therefore has to be unambiguous; data input has to be formalized and standardized. These boundary conditions for data gathering form part of the input in the define phase of the improvement process. An example of a structured data input tool has been given in section 4.4.1; ‘Implementation’.
The required data for machine maintenance improvement is only limited available, and often incomplete. It is therefore impossible to gather the data as presented in the framework of section 2.3.2, and no maintenance improvement decisions can be made.
3.2. Organizational analysis
The previous section started the analysis on machine level, showing available and missing data. Adding missing data however is not a sufficient solution to achieve a machine maintenance improvement process; the process itself has to be structured. This section analyses the current available functions, and determines which are missing in order to facilitate the machine maintenance improvement process.
3.2.1. Available functions
The functions within different departments are modeled. A production department (Sheet Metal) has been modeled, as well as supporting Departments. The technical service center (TSC) is the department that is responsible for maintaining the equipment of the complete Fokker Aerostructures facility in Papendrecht. The Purchasing Department handles procurement for the TSC, and is modeled as well.
Production Department
The production department ‘Sheet Metal’ has been modeled from a maintenance process perspective. The detailed control of the production process has been omitted if not relevant for the maintenance process. Such process control reacts upon disturbances, e.g. by allocating a suitable amount of people to a task in order to meet the planning. This however is not relevant for the improvement of the maintenance process, because it acts upon incidents, while maintenance improvement requires long term, and sustainable information.
