Overall Equipment Effectiveness

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Theoretical background

This chapter explains overall equipment effectiveness, its development and modified forms. Further theory is presented to elaborate the reasons for fluctuating OEE. Total Productive Maintenance is explained to keep control over uncertainty with introduction to fuzzy overall equipment effectiveness. The theory topics do not necessarily follow an order. Reader can revert to these topics to understand their use in the later parts of the research

Overall Equipment Effectiveness

Companies strive for improving their processes and productivity which is becoming a crucial competitive factor. Different performance measures are used and monitored by companies to improve their processes. A performance measure that is commonly used to evaluate the efficiency of production processes is OEE. The term “OEE” stands for overall equipment effectiveness which is used to measure in what degree the machines or equipment’s in a production are utilized (Klabusayova, 2014). Nakizama (1988) provided OEE as aquantitative metric known to support the overarching concept of Total Productive Maintenance(TPM) (Nakajima, 1988). According to Muchiri and Pintelon (2008),”OEE is a measure of total equipment performance, that is degree to which the equipment is doing what it is supposed to do”. It helps to identify and measure the losses for manufacturing aspects namely availability, performance and quality (Muchiri & Pintelon, 2008). OEE has gained popularity by helping companies improve their productivity which is a mojor competetive factor. (Ylipää, Skoogh, Bokrantz, & Gopalakrishnan, 2017). A focus on OEE helps to identify losses and improve them which contributes to increased profits and it is a powerful tool to do so (Puvanasvaran, 2013)

OEE Development

Since its introduction by Nakizama(1988), OEE has evolved over the years. Muchiri and Pintelon(2008)has done a comprehensive literature review on this topic. The modified versions of OEE has been proposed to overcome the inefficiencies of the traditional OEE, these modifications are mainly motivated by looking into effectiveness at the equipment level or at the factory level (Muchiri & Pintelon, 2008).
TEEP (Total equipment effectiveness performance) and PEE (Production equipment effectiveness) are two of the modified forms at the equipment level. TEEP introduced by Ivancic (1998) differs from OEE in a way that it clearly differentiates between planned downtime and the unplanned downtime (Ivancic.I, 1998). PEE introduced by Raouf (1994) puts emphasis on weights associated with availability, performance and quality and it also makes distinction between discrete and continuous production processes (Raouf.A, 1994).
OFE (Overall factory effectiveness) was developed by Scott and Pisa (1998) to express the factory level effectiveness of the production process where there are a multiple number of production steps and machines are involved (Scott & Pisa, 1998). Further Huang et.al (2003) came up with OAE (Overall asset effectiveness) which uses simulation analysis as a reliable method to understand the dynamic nature of the production processes (Huang, Dismukes, Mousalam, & Razzak, 2003).
OPE (Overall plant effectiveness) and OAE (overall asset effectiveness) are extensions of the OEE tool which helps to identify and measure all the losses in a production process. Both terms are same from an industrial application purpose, but losses differ when using them across different industries (Muchiri & Pintelon, 2008)

Six big losses

Nakizama (1988) categorised six big losses under three categories namely Downtime losses, Speed losses and Quality losses. Downtime losses are due to (1) breakdowns and (2) set-up(adjustment) losses. Breakdown losses are categorised as time losses and quantity losses due to equipment breakdown or failure. When there is change in the product then the time consumed during that changeover is associated with set-up losses. Speed losses includes (3) idling and minor speed losses as well as (4) reduced speed losses. When there is a temporary malfunction or machine is in the idling position, the loss of time is associated with speed loss. Every equipment has a designed speed and there is actual operating speed, the difference in these two speeds is referred to as the reduced speed loss. Quality losses includes (5) quality defects and reworks along with (6) reduced yield during start-up. Figure 1 presents the losses. Malfunctioning production equipment can cause quality defects and reworks. The reduced yield losses are due to loss of yield from machine start-up to machine stabilization (Nakajima, 1988).
A production suffers from losses and breakdowns which lower the efficency and productivity.The bottleneck of the production could be a single equipment, tool or a human. Hence OEE is a method to measure those losses and is not only focused to the whole line but individual parts of the line aswell. This enable analyzing the losses and the causes for the losses (Klabusayova, 2014). The more common way of representing downtime losses, speed losses and quality losses is in terms of availability, performance and quality. The breakdown, adjustment and setup losses belong to the availability faction. While the speed losses are related to the performance ratio, and lastly the rework and yield losses belong to the quality ratio. For instance, when performing planned maintenance, unplanned maintenance, setup or improvements it stops the equipment which decreases the availability. As long as the equipment is available for usage it does not affect the availability. Performance is on the other hand defined as how much was done compared to what was planned. Therefore, speed losses as small stoppage or low productivity does affect the performance ratio. When running a production there can be quality losses which can be defined as scrap, default products, defects or rework, which increases the quality losses (Puvanasvaran, 2013).
The Figure 2 explains the time break-up of Total calendar time (TCT). The Closing time (CLT) is the time for holidays, festivities, exceptional events etc. Non-Working time (NWT) is the time lost due to lack of demand, stock-outs, strikes, line overhauls etc, Stand by time (SBT) is the time lost due to planned maintenance, starving, blocking, on-line quality control, handling, loading etc., down time(DT) is lost time due to failures, reactive maintenance, recalibration etc., performance losses(PL) is time consumed in micro stoppages, start-up, reduces speed, regulation, cleansing etc. and finally the Quality rate (QR).The major losses are divided into equipment dependent and equipment independent losses. The equipment dependent time losses are such due to failure, set-up, defects etc. while the equipment independent time losses are starvation, stock-out, lack of demand etc. The closing time (CLT), non-working time (NW) and standby time (SBT) are under the equipment independent category. Down time (DT), performance loss time (PLT) and QR (quality rate) are under the category equipment dependent time losses (Zammori, Braglia, & Frosolini, 2011)

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Uncertainty in Production Environment

Any unpredictable event in the production environment that disturbs the operations and the performance of the enterprise is called uncertainty (Koh & Saad, 2002). Ho (1989) has categorised uncertainty in two broad groups namely 1) environmental uncertainty and 2) system uncertainty. Environmental uncertainties pertain to uncertainties beyond the production process which includes demand uncertainty and supply uncertainty. System uncertainty is associated to the production process which includes quality uncertainty, production lead time uncertainty, failure of production system, operation yield uncertainty and changes to product structure (Ho, 1989). The focus of this research is the production process related uncertainties therefore system uncertainty is considered only.
Uncertainties can be related to disturbances at the shop floor such as resource breakdowns and random production lead times. The availability of the machines is going to vary a lot due to breakdown times. The loss of time due to breakdown of machines, set-up of machines and scrap becomes the key factor for increase in production lead times (Azizi, bin Ali, & Ping, 2015).
Researchers have explored the uncertainty associated with data collection methods at the shop floor (Rößler & Abele, 2013; Sonmez, Testik, & Testik, 2018). The current state analysis begins with the data collection from different machines. The data collection methods are predominately manual and semi-automatic, and the quality of data in these methods is usually not up to the mark and leads to inaccurate conclusions (Rößler & Abele, 2013).When operators are involved in data collection on the shop floor there are sources of uncertainty as the data recording process is itself disturbance in the operation cycle, for example the operator may feel overburdened with minor stoppages or answering short questions, the self-written logs are also error prone (Bokranz & Landau , 2006).
Another cause for uncertainty may arise from the categorisation of losses i.e. the loss is associated to which parameter of OEE (Zammori, 2015). Even if the semi-automatic data collection systems being used there will always be need of a human being to categorise the losses (Rößler & Abele, 2013).
The definition of data collection period is also a source of uncertainty. Different time horizons may reflect different trends in OEE, the daily value of OEE would be highly uneven due to day to day variability of the manufacturing process. In another scenario if the OEE values are taken on a monthly or quarterly timeframe , it converges to an average value and the variability of the process is concealed (Zammori, Braglia, & Frosolini, 2011).
Changing product mix when a variant of the product is produced on the same production line the changeover time must be considered, in relation to this changeover time there will be more effects on machine, tools or services. This may lead to fluctuations in OEE and fuzziness (Roessler & Abele, 2015).
The maximum amount of production that can be achieved by using external or internal resources in known as capacity. Capacity can be considered in terms of machinery as well as workforce (labour). Absenteeism, sickness and fatigue can be potential reasons for labour shortage. To meet the demands of increased capacity, companies opt for outsourcing, renting equipment, hiring contingent workers etc. However, this need for external capacity may not be met always due to lack of available quantity or quality hence it leads to capacity uncertainty both in labour as well as machinery (Fazil Pac, Alp, & Tan, 2009).
Quantity uncertainty can be viewed from supply side as well as from demand side. Supply side uncertainty occurs when there is shortage of material, scrap occurs or when production overrun occurs. Demand side uncertainty occurs when there is changes in the master production schedule to match the changes in the customer orders or demand forecasts (Clay Whybark & Gregg Williams, 1976

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HTO approach

Any manufacturing process is a complex web which has interactions between process tools, materials, machines, people, departments, companies, and processes (Scott & Pisa, 1998). Many a time an isolated view of these interdependent activities is taken into consideration which results in inefficiency due to lack of coordination in deploying these resources, therefore it becomes inevitable to focus attention beyond an individual equipment to overall performance of the factory. The goal is to have a highly efficient integrated system instead of brilliant individual equipment (Oechsner, Pfeffer, Pfitzner, & Binder, 2003).
To gain a better understanding of such a system a different approach is required. Therefore, an approach called HTO (Human, Technology, Organisation) is used in this paper to have a better understanding of the underlying reasons of uncertainty as shown in Figure 3. HTO is an approach to understand the complex systems from a systems perspective ( Karltun, Karltun, Berglund , & Eklund, 2017)

1 introduction 
1.1 Background
1.2 Problem Description
1.3 Aim and Research questions
1.4 Relevance to Area of education
1.5 Delimitations
1.6 Chapter outline
2 Theoretical background 
2.1 Overall Equipment Effectiveness
2.2 Uncertainty in Production Environment
2.3 HTO approach
2.4 Total productive Maintenance
2.5 Fuzzy Logic and its industrial use cases
2.6 Fuzzy Overall Equipment Effectiveness (FOEE)
3 Method and approach 
3.1 Research design
3.2 Literature review
3.3 Process mapping – Current status analysis
3.4 Observation
3.5 Interview
3.6 Survey (Ranking Question)
3.7 Expected reliability and validity of the research
4 Findings and analysis from the case company 
4.1 Process mapping
4.2 Observation
4.3 Interviews
4.4 Survey (Ranking Question)
4.5 Analysis
5 Discussion and conclusions 
5.1 Discussion on methods
5.2 Discussion on findings
5.3 Conclusion
References 
Appendices
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