Proposing Leanness Measures in Equipment Level for LowVolume Make-to-Order Production Environments – The Abstract b a Complementary Tools for OEE

Asian Journal

of Research in

Business Economics

and

Management Asian Journal of Research in Business Economics and Management

Vol. 5, No. 9, September 2015, pp. 95-120

ISSN 2249-7307

95

www.aijsh.org

Asian Research Consortium

Proposing Leanness Measures in Equipment Level for Low-

Volume Make-to-Order Production Environments – The

Complementary Tools for OEE

KhodayarSadeghia,Mohammad Aghdasi

b

aPhD Student of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.

bAssociate Professor of Industrial Engineering, Tarbiat Modares University, Tehran, Iran. DOI NUMBER-10.5958/2249-7307.2015.00176.0

Abstract

This article presents two new assessment models to investigate the performance of production in a low

volume make-to-order production environment. These tools can help companies running these production

strategy to measure and monitor the degree to which implementation of lean discipline to be

successful.Overall Equipment Effectiveness (OEE) is an important part of the proposed concepts. OEE is

originally used as a measure for evaluation of utilization effectiveness of manufacturing operation, but it

can also be used as an indicator of performance within a manufacturing environment.However, according

to our experiences and research results, using OEE as a performance indicator is not appropriate for low-

volume make-to-order production environments. This is mainly because, OEE has been, originally and

historically, developed and used for mass-production environments and due to high amount of Typ-1

losses in low volume make-to-order environments, it cannot be considered as a proper tool for assessing

leanness of a production environment in equipment level.Therefore, we propose Overall Equipment

Deficiency (OED) and Overall Equipment Inertia (OEI) as two simple metrics to investigate Type-2 and

Type-1 losses. Especially, the purpose of OED is to measure all hidden type-2 waste related to an

equipment in the shop floor of the production environment. It is also used for diagnostic purposes such as

finding root causes of waste.

Keywords:Lean production, waste measurement, low volume make-to-order production, Overall

Equipment Effectiveness (OEE), Overall Equipment Deficiency (OED)

Sadeghi& Aghdasi (2015). Asian Journal of Research in Business Economics and Management, Vol. 5, No. 9, pp. 95-120

96

1. Introduction

This article aims at introducing assessment tools to investigate the performance of production in low

volume make-to-order production environments. These tools can help production companies to measure

and monitor the degree to which implementation of lean discipline to be successful.

Bellgran et. al. (2010), state that manufacturing industry of today is facing extensive and ever-

growing challenges due to the globalization, increased competition and the environmental situation. To

be able to maintain and develop the ability to compete on a global market, manufacturing

companies need to be successful in developing innovative and high-quality products with short lead-

times, as well as designing robust and flexible production systems implying the best preconditions

for operational excellence. The paradigm of Lean Production is implemented worldwide in order to cope

with many of these challenges. Lean could be considered the best way known today of how to

manufacture products in a resource efficient way (Andersson et al, 2011).

Consequently, it is discovered that measurement is needed for identifying the problems in order to

improve the productivity. To achieve this, it is necessary to establish appropriate metrics for

measurement purposes (Nachiappan and Anantharaman, 2006). The TPM paradigm, launched by

Nakajima (1988) in the 1980s, provided a quantitative metric for measuring the productivity of

individual production equipment ( Eswaramurthi, 2013).

As defined by Nakajima (1989), Overall Equipment Effectiveness (OEE) is an important part of this

concept. OEE is originally used as a measure for evaluation of utilization effectiveness of manufacturing

operation, but it can also be used as an indicator of performance within a manufacturing environment.

Comm et. al. (2000) state that ―Industries strive for leanness, because being lean means being competitive

by eliminating the non-value added practices‖, i.e., wastes. However, the strategy for a generic lean

practice implementation, and achieving leanness throughout, lacks strong evidence and is not clear to

many.

However, according to our experiences and research results based on implementation of lean production

in a printing house conducted on action research basis, using OEE as a performance indicator is not

appropriate for a low-volume high-mix make-to-order production environment such as commercial

printing industry. This is mainly because OEE is originally developed and used for mass-production

environment (de Ron et. al., 2006)

The objective of this paper is to propose Overall Equipment Deficiency (OED) as a dedicated and simple

metric to investigate the deficiency of make-to-order production environments. The purpose of OED is

trifold: it measures all hidden type 2 waste in the shopfloor for any producing equipment, it is used for

diagnostic purposes such as finding root causes of waste, and it is used to identify the hidden capacity of

production that can be earned.


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2. Literature Review

The concept of lean thinking (Womack et. al., 1996) originated from the Toyota production system (TPS)

developed in 1950s Japan.

Monden (2011), instrumental in introducing the just-in-time (JIT) production system to the United States,

in his book Toyota Production System: An Integrated Approach to Just-In-Time, Fourth Edition explains

how to promote the culture and way of thinking needed to settle the TPS across any organization.

Since long time ago, manufacturing has been trying to optimize operations, supply chains and capital

assets (Pagatheodrou, 2005); and for getting to this aim, elimination of waste has a main role. Recently,

achieving this goal has become increasingly complex due to the fast moving global market, budget cuts

and capacity downsizing (Pagatheodrou, 2005). Hence, lean manufacturing has become the key approach

to managing this complexity (Liker, 1998). The Toyota Production System (TPS), the pioneering

approach to manufacturing leanness, has become the basis for much of the optimization movement that

has dominated manufacturing developments since the last decade (Liker, 1997; Hall, 2004).

While automotive and aerospace industries were the first adopters of lean thinking, its application has

spread into other industries (Womack and Jones, 1996).

2.1. The Leanness Concept

The waste-elimination concept of lean manufacturing has brought significant impacts on various

industries. Numerous tools and techniques have been developed to tackle specific problems in order to

eliminate non-value-added activities and become lean. Compared with the efforts made to address ‗how

to become leaner,‘ the statement ‗how lean the system is‘ received less attention. Several lean metrics

have been developed for evaluating the performance and tracking the improvements of lean systems.

However, an individual metric focusing on a specific performance aspect cannot represent the overall

leanness level. On the other hand, lean practitioners often use self-assessment tools to depict the current

status of their system. However, surveys have the nature of subjectivity, and the predefined lean

indicators of a questionnaire may not fit every system perfectly. An objective, quantitative, and integrated

measure of overall leanness has not been established for lean practitioners to measure how lean a system

is (Hung-da Wan et al, 2008).

Most of the existing lean tools (e.g. Kanban system, quick changeover, etc.) focus on ‗how to become

leaner‘ instead of ‗how lean it is‘. Only a few of them address the latter. The value stream mapping

techniques, lean assessment tools, and lean metrics are three main categories that concern the level of

leanness.

Comm et. al. (2000) state that ―Industries strive for leanness, because being lean means being competitive

by eliminating the non-value added practices‖, i.e., wastes. However, having a clear idea about leanness

in assessing the progress of any lean transformation project is very important. But strategy for a generic

lean practice implementation, and achieving leanness throughout, lacks strong evidence and is not clear to

many (Comm et al., 2000; Chang 2001).


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Waste can be defined as: ―Every activity that adds costs but non-value-added for the customer (Chiarini,

2013)‖.

Regarding waste, many organizations use the Japanese term Muda, although Muda in Japanese has a

much more restricted definition (Chiarini, 2013). Muda is an activity that consumes resources without

creating value for the customer. There are two types of muda . Type-1 and Type-2 muda (Sayer et.al.,

2012):

• Type-1 muda include actions that are non-value-added, but are for some other reason deemed

necessary for the company. These forms of waste usually cannot be eliminated immediately.

• Type-2 muda are those activities that are non-value-added and are also not necessary for the

company. These are the first targets for elimination.

2.2. Overall Equipment Effectiveness (OEE)

TPM is a manufacturing program designed primarily to maximize equipment effectiveness throughout its

entire life through the participation and motivation of the entire work force (Nakajima, 1989). Nakajima

also developed overall equipment effectiveness (OEE) as a measure for assessing the progress of TPM,

which is calculated by the multiplication of availability, performance and quality (Jeong, 2001). Actually

OEE is a key performance measure in mass-production environments (de Ron, 2006)

The metric, which is called Overall Equipment Effectiveness (OEE), is accepted as a measurement of

internal efficiency (Johnson and Lesshammer, 1999) and it is the true measure of the value added

production by equipment.( Eswaramurthi, K.G. and P.V. Mohanram, 2013)

The original definition of OEE by Nakajima excludes planned downtime such as scheduled

maintenance and breaks from the total available time, while e.g. Jeong& Phillips (2001), include

these as equipment losses especially important in capital intensive industry. They even extend the list

of losses to also include R&D and engineering usage time, i.e. increasing the total available time

in the OEE calculation but hence reducing the risk of overestimating OEE (Anderson et al, 2010).

Bamber et al. (2003) observe that OEE is often used as a driver for improving the performance of a

business by concentrating on quality, productivity and machine utilization issues and, hence, it is

aimed at reducing non-value adding activities often inherent in manufacturing processes.

The OEE tool is designed to identify losses that reduce the equipment effectiveness. These losses are

activities that absorb resources but create no value. It is a bottom-up approach where an integrated

workforce strives to achieve overall equipment effectiveness by eliminating six large losses (Nakajima,

1989). According to Muchiri et. al. (2008), in Fig. 1, these losses are given below:


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Fig. 1. Different losses based on OEE measurement tool (Nakajima, 1988)

Downtime losses

(1) Breakdownlosses categorized as time losses and quantity losses caused by equipment failure

or breakdown. Major stoppages due to machine failure or due to production defect lead to

downtime losses. A stoppage is said to be major if it takes more than 10 min. Major

stoppages may also result from supplier-related downtime or warehouse downtime (

Muchiri et. al, 2008).

(2) Set-upand adjustment losses occur when production is changing over from requirement of

one item to another. Changeover from one product to another or from product of one size to

another are some examples of these type of losses (Muchiri et. al, 2008).

Speed losses

(3) Idling and minor stoppage losses occur when production is interrupted by temporary

malfunction or when a machine is idling. Minor stoppages are the stoppages that are less

than 10 min, for example due to equipment jams (Muchiri et. al, 2008).

(4) Reduced speed losses refer to the difference between equipment design speed, and actual

operating speed.

Quality losses

(5) Quality defects and rework are losses in quality caused by malfunctioning production


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equipment. Quality losses due to products that fail to meet specifications.

(6) Reduced yield during start-up are yield losses that occur from machine start-up to

stabilization.

2.3. OEE measurement

In addition to the popular way for calculating OEE in terms of three factors as shown in Fig.2, we can

also calculate OEE as following:

𝑂𝐸𝐸 =𝑉𝑎𝑙𝑢𝑎𝑏𝑙𝑒𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑡𝑖𝑚𝑒

𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝑡𝑖𝑚𝑒 (1)

Fig. 2. calculation of OEE measurement tool (Nakajima, 1988)

Jeong et. al. (2001), stated that equation (1) can be used to roughly estimate OEE without collecting all

six loss categories. Available production time is the total time available for production in a given period

and valuable operating time can be estimated by multiplying the theoretical cycle time by the number of

products that are successfully completed.


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3. A New Approach To Production Losses

In this section a general framework with different categories of production losses has been developed.

The framework shown in Fig. 4, classifies production losses into different categories depending on the

cause of loss. This classification helps the decision-maker to measure different causes of

production losses so that attention can be given to the relevant causes. In the following main

categories of production losses are defined.

Lossesdue to no production at all.These are production losses that prevent to produce any units of

products and include the following:

1. Make-ready:These are set-up and adjustment losses occur when production is changing

over from requirement of one item to another. Changeover from one product to another or

from product of one size to another are some examples of these type of losses. These losses

should be minimized but could not be eliminated. In other words they are non0value added

but necessary activities.

2. Unplanned machine-stop:These are losses caused by equipment failure or breakdown,

material shortage, no orders for being accomplished and some human factors such as labor

absence.

Losses due to low production. These are production losses that decrease the volume of production and

include the following:

3. Low speed:Reduced speed losses refer to the difference between equipment design speed,

and actual operating speed. These losses are resulted from some factors such as equipment

age and unskilled labor.

4. Minor stoppage and idling: Idling and minor stoppage losses occur when production is

interrupted by temporary malfunction or when a machine is idling. Component jam is a

good example for these type of losses.

Losses due to useless production. Quality defects and rework are losses in quality caused by

malfunctioning production equipment. According to lean concept, overproduction is also a special type

of waste. Poor quality of materials and product not matched to customer‘s needs are also causes for this

type of losses.

Asian Journal

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Business Economics

and

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Vol. 5, No. 9, September 2015, pp. 95-120

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Fig. 3. Categorizing production losses for proposing OED

3.1. Factors To Be Measured

If we define:

𝑁𝑇= the amount of value created or Number of good parts produced in available production

time or shift time (units) (2)

𝑁𝑇𝑚𝑎𝑥 = theoretical number of parts produced in available production time

=theoretical production rate (units/h) X available production time (h) (3)

𝑆𝑁= normal speed (units/h) (4)

𝑆𝐿= low speed (units/h) (5)

𝑛= number of orders accomplished in the shift time (6)

𝑡𝑆= total machine stop time (h) (7)

𝑡𝐿𝑆= total low speed production time (h) (8)

𝑡𝑠𝑕𝑖𝑓𝑡 = total available production time (h) (9)

𝑡𝑀= total make-ready time in the shift time for making n order (h) (10)


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𝑁𝐷= total defects produced in the shift time (units) (11)

Fig. 4. Incorporating parts of available production time

Then, based on Fig. 4, we have:

Poor quality losses = 𝑁𝐷

𝑆𝑁 (h) or 𝑁𝐷 (units) (12)

Low speed losses = 𝑡𝐿𝑆 (h) or (𝑆𝑁−𝑆𝐿)

𝑆𝑁 × 𝑡𝐿𝑆(units) (13)

Machine-stop losses = 𝑡𝑆 (h) or (𝑡𝑆 × 𝑆𝑁) (units) (14)

Make-ready losses = 𝑡𝑀 (h) or (𝑡𝑀 × 𝑆𝑁) (units) (15)

Ideal value = 𝑡𝑆𝑕𝑖𝑓𝑡 × 𝑆𝑁 (units) or 𝑁𝑇𝑚𝑎𝑥 (units) (16)

4. Proposing Overall Equipment Deficiency (OED) and Overall Equipment Inertia

(OEI)

As we know, there are two types of non-value added activities (i.e. waste or muda) which are categorized

here as: Type-1 and Type-2 losses. Type-1 losses are actions that are non-value-added, but are for some

other reason necessary for the production. Type-2 losses are those activities that are non-value-added and


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are also not necessary for the production. In table 2, the relation of Type 1 and Type 2 losses and previous

mentioned causes of production losses are shown.

Table 2. Causes of Type 1 and Type 2 losses

Production losses

category Causes Root causes

Type 1 losses Make-

ready

Warm-up

Set-up and adjustments

Machine changeovers

Type 2 losses

Unplanned

machine

stop

Equipment failures, Tooling damage, Unplanned

maintenances, Repairs, shortage of energy, Material

shortage, Supply problems, Material handling problems

Market downturn, Marketing and sales weakness,

Scheduling problem, Labor absence, Labor unrest,

Labor laziness

Low

production

Equipment Age, Product/Material requirements, Unskilled

labor, Minor stoppage and idling, Component Jams

Useless

production

Human errors

Equipment malfunctioning

Over productions

Poor quality of materials

Product not matched to customer‘s needs

Then according to Fig. 4, we have:

Available production time (shift time) =Valuable operating time +

( Poor quality losses + Low speed losses + Machine stop losses)+ Make-ready losses

(17)

But if according to table 2, we define:

( Poor quality losses + Low speed losses + Machine stop losses) as Type 2 losses, andMake-ready losses

as Type 1 losses, then, we have:

Available production time = Valuable operating time + Type 1 losses + Type 2 losses (18)

therefore,

1= Valuable operating time

Available production time +

Type 1 losses

Available production time +

Type 2 losses

Available production time (19)


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however, from Eq. 1, we know that, Valuable operating time

Available production time= OEE

Then, if we define Overall Equipment Inertia as:

OEI = Type 2 losses

Available production time (20)

and Overall Equipment Deficiency as:

OED = Type 1 losses

Avai lable production time (21)

then, we will have,

OEE + OED + OEI =1 or (22)

OEE (%) + OED(%) + OEI(%) =100% (23)

However, according to definitions 2 to 11 and equations 12 to 16:

OEE = 𝑉𝑎𝑙𝑢𝑎𝑏𝑙𝑒𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑡𝑖𝑚𝑒

𝑡𝑆𝑕𝑖𝑓𝑡 or OEE=

𝑁𝑇

𝑁𝑇𝑚𝑎𝑥

(22)

OEI = 𝑡𝑀

𝑡𝑆𝑕𝑖𝑓𝑡 or OEI=

𝑡𝑀 ×𝑆𝑁

𝑁𝑇𝑚𝑎𝑥

(23)

OED = 𝑡𝑆+𝑡𝐿𝑆 +

𝑁𝐷𝑆𝑁

𝑡𝑆𝑕𝑖𝑓𝑡 (24)

As we get from Eq. 24, calculating OED is not easy. Because usually measuring the required variables in

a work day and for an assumed equipment is a time consuming and may be impossible job.

However, from the equation 22, we can find that:

OED =1- (OEI + OEE) (25)

So we propose a very easy way for calculating OED as the following:


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According to lean concepts we know that:

• There are two types of non-value added activities, waste or muda* which are categorized as:

Type-1 and Type-2 muda. Type-1 muda are actions that are non-value-added, but are for some

other reason necessary for the production. Type-2 muda are those activities that are non-value-

added and are also not necessary for the production.

• To move to the destination of lean transformation in production stage of any production

environment, we must eliminate Type-2 waste (muda) as much as possible, ideally to zero, and

reduce Type-1 waste (muda) to the minimum level.

• For an equipment, Type 1 waste is mainly because of make-ready time and the major part of it is

necessary and totally cannot be eliminated. So we can just reduce it to a specific amount (i.e.

minimum amount) by some tools such as SMED or standard work. This minimum amount

(Type-1|min = minTsetup) is determined by experience or according to technological and

operative restrictions. Therefore we may conclude:

Type-1|actual = Type-1|min + Type-1 (26)

• Sincewe assume that Type-1 is eliminable, it is not inherently necessary and we can take it as

Type-2 waste. In other words the amount of time spent on set-up process which is more than

𝑚𝑖𝑛𝑇𝑠𝑒𝑡𝑢𝑝 (= Type − 1|min) ) is included in Type-2 waste by assumption.

• From here on wherever we say Type 1 waste or Type 1 muda we mean this mentioned minimum

amount of Type-1.

• So the actual destination in our lean transformation journey, or actual target, is to eliminate total

Type-2 muda while ignoring to eliminate or reduce Type-1 muda beyond the minimum level

because it is actually impossible by definition (i.e. non-value-added but necessary).

• Afterward, in real situation when we talk about leanness on operating level, e. g. production of a

printing machine in a work day, we mean the degree to which Type-2 waste is close to zero and

Type-1 waste is close to the minimum level. This is shown in Fig. 5.

• From Fig. 5, is obvious that OEE is a dependent variable and affecting by Type-1 waste, i.e. by

decreasing or increasing of Type-1 waste, it changes.

*In this paper we use waste and losses ( in narrow sense) instead of muda


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Since Type-1 waste (in minimum level) is usually out of control, therefore its consideration in the process

of measuring performance (by some tools such as OEE) prevents us from identification of the major

problem i.e. Type-2 waste.

As it is shown in Fig. 6, the new measure is proposed as Overall equipment Deficiency (OED) as

following:

OED =𝑇𝑦𝑝𝑒 2 𝑤𝑎𝑠𝑡𝑒

𝑉𝑖𝑑𝑒𝑎𝑙 (27)

This measure directly shows the amount of Type-2 waste in relation to 𝑉𝑖𝑑𝑒𝑎𝑙 and it is independent to

Type-1 waste. So in make-to-order production environments where a large number of orders being

produced each day and consequently large number of Type-1 waste being recognized, it could be a good

measure for justifying about the leanness of the production.

According to Fig. 6, OED is defined as:

OED =𝑇𝑦𝑝𝑒 2 𝑤𝑎𝑠𝑡𝑒

𝑉𝑖𝑑𝑒𝑎𝑙=

𝑉𝑖𝑑𝑒𝑎𝑙 − 𝑉𝑎𝑐𝑡𝑢𝑎𝑙 − 𝑇𝑦𝑝𝑒 1 𝑤𝑎𝑠𝑡𝑒

𝑉𝑖𝑑𝑒𝑎𝑙 (28)

where,

𝑉𝑖𝑑𝑒𝑎𝑙 = 𝑇𝑠𝑕𝑖𝑓𝑡 × 𝑆𝑁 (29)

𝑇𝑦𝑝𝑒 1 𝑤𝑎𝑠𝑡𝑒 = 𝑛𝑇𝑠𝑒𝑡𝑢𝑝 (30)

𝑉𝑎𝑐𝑡𝑢𝑎𝑙 = 𝑁𝑇 (31)


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Fig. 5. Possible situations for degree of leanness of a printing machine in an arbitrary time

period


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4.1 Special case for calculation of Overall Equipment Deficiency (OED)

where in addition to definitions 2 to 11:

𝑇𝑠𝑒𝑡𝑢𝑝 is defined as the minimum required make-ready time for producing an order that is assumed

constant and is identified by the experts according to technological constraints

Therefore for an equipment in each shift time we have:

OED (%) = (1 −𝑁𝑇 𝑆𝑁

𝑇𝑠𝑕𝑖𝑓𝑡−

𝑛𝑇𝑠𝑒𝑡𝑢𝑝

𝑇𝑠𝑕𝑖𝑓𝑡) × 100 (32)

Fig. 6. The comparison between OED and OEE

4.2 OED as a graphical tool

Based on Fig. 7, we can write:

𝑉𝑎𝑐𝑡𝑢𝑎𝑙 = 𝑉𝑖𝑑𝑒𝑎𝑙 − 𝑇𝑦𝑝𝑒 1 𝑤𝑎𝑠𝑡𝑒 − 𝑇𝑦𝑝𝑒 2 𝑤𝑎𝑠𝑡𝑒 (33)

or


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𝑁𝑇 = 𝑆𝑁 𝑇𝑠𝑕𝑖𝑓𝑡 − 𝑛𝑇𝑠𝑒𝑡𝑢𝑝 − 𝑇𝑦𝑝𝑒 2 𝑤𝑎𝑠𝑡𝑒 (34)

if we assume that Type-2 waste equals zero we will have the equation of a line (𝑁𝑇 = 𝑓(𝑛)) as following:

𝑁𝑇 = 𝑆𝑁 𝑇𝑠𝑕𝑖𝑓𝑡 − 𝑛𝑇𝑠𝑒𝑡𝑢𝑝 (35)

This line has been shown in Fig. 7.

This line is our target for any leanness improvement and it is also the graphical equivalent of the middle

situation in Fig. 5.

Fig. 7. Graphical model of OED


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According to geometric principles, it can be proved that for every operation point A (𝑛,𝑁𝑇) which is

obtained from the production information of a work day, we will have in Fig. 7:

𝑂𝐸𝐷𝐴 =𝑕𝐴

𝐻

(36)

and a line, can be sketched parallel to the target line which passes through this point.

As much as the line passing through the operation point be closer to the target line, the related OED is

smaller and as much as this line be away from the target line the related OED is larger and consequently

the operation is more inefficient.

Therefore, by putting the points related to the operation of a work center or a printing machine in a

specified period of time we can visually have a good idea about the leanness situation of the production.

This is shown in Fig. 8. , for a printing machines under three days of observation.

Fig. 8. positioning three days of operation on OED graphical model


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5Industrial examples of OED application

5.1 Example 1

Based on the above logic, while OEE usually is used for indication of how effective the production

process is, it will be clear from the following example (see Table 3) that OEE is not appropriate for

assessing the leanness of a low-volume make-to-order production such as commercial printing. This is

because in such a production environment, too much time spend for changeover and set-up operations

which result in too much Type-1 waste.

In table 3, the production information of two days working of a printing machine has been compared to

determine the useless of OEE in commercial printing industry.

Table 3. OEE comparison between two working days of a printing machine

2 1 Day

21 1 Number of jobs (n)

8 8 Available operating time or Tshift (h)

10000(sph) 10000 (sph) Normal print speed or S (sheet per hour)

15‘ 15‘ Minimum amount of Tsetup for each job (min)

20‘ 50‘ Actual Tsetup for each job (min)

20 x21=420‘ 50‘ Total time spent for setup

0 40‘ Other than set-up time waste/Type-2 waste (min)

21 x (20-15)=105‘ 40+(50-15)=75‘ Total Type2 waste (min)

5‘ 75‘ Average of type2 waste for each job(min)

1h 8-1.5=6.5h Valuable operating time (h)

10000 sheets 65000 sheets Total Value earned (Number of printed sheets)

(10000/80000) x

100≈12.5%

(65000/80000) x

100=81.25%

OEE (%)

5.2 Example 2

when the minimum set-up time required for having color ok for any job is 15 minutes (𝑇𝑠𝑒𝑡𝑢𝑝 ), normal

speed of printing equals 11000 sheets per hour (𝑆), the total number of accepted sheets by the customers

equals 18000 sheets (𝑁𝑇), and 16 jobs done in one day (𝑛), then for a 15 hours shift time(𝑇𝑠𝑕𝑖𝑓𝑡 )we have:


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OED(%) = (1 −(18000 11000 )

15−

16 × (15 60)

15) × 100 (37)

Then 𝑂𝐸𝐷 % = 62.42% (38)

Example 2: according to the information in the Table 2, for the assumed printing machine in those days

we have:

for day 1:

OED % = 1 − 65000 10000

8−

1 × (15 60)

8= 15.625% (39)

and for day 2:

OED % = 1 − 10000 10000

8−

21 × 15 60

8= 21.87% (40)

By comparing OEDs of day 1 and day 2 with OEEs of these two days, we can see that while the OEE for

day1 (i.e. 81.25%) is very larger than OEE for day2 (i.e. 12.5%), but this is not the same for OEDs of

these two days. In other words their OEDs are not as different as their OEEs.

So if the total Type-1 waste of these two days be neglected, we can say that the leanness of day2 is almost

as much as day1 and it is not so worse.

Remember that the total Type-1 waste, in minimum level, is because of commercial printing business

model and it is out of our control.

6. Results and discussion

The context of this research is a commercial printing company located in Tehran. This printing house is

running a project for implementation of lean production in the company, since 2012.

As it is mentioned in section 3.2, in phase 3 of the implementation road map, i.e. the phase of discovering

waste, the project team including the researcher investigated on using OEE as a performance metric or

progress measure of the project.

But after several runnings of the action research cycle in this phase, as it is shown in Fig. 3, we came to

the following conclusions:

OEE is decreased dramatically because of numerous changeover between productions of orders. So it

is useless as a performance measurement tool for a low volume make-to-order environment such as

commercial printing business.

Total Type-1 waste (in minimum level which is specified by the number of jobs done and


114

technological constraints) is usually out of the management control, therefore its consideration in

measuring performance by any means does not lead us to the main problem in our continuing effort

to make the printing operation lean or leaner.

The most important type of waste in operation level is Type-2 waste and can be measured and

illustrated by OED very easily and effectively.

In Fig. 9, and Fig. 10, the operation points of one of the printing machines (i.e. Sakurai 475SD

with𝑇𝑠𝑕𝑖𝑓𝑡 = 15 𝑕 ,𝑇𝑠𝑒𝑡𝑢𝑝 = 15′, and 𝑆 = 11000𝑠𝑝𝑕 ) are shown in two different phases of the lean

implementation project:

firstly as shown in Fig. 9, at the end of the phase 3 of implementation road map (i.e. phase of

discovering waste) on September 2012

secondly as shown in Fig. 10, at the middle of the phase 7 (i.e. phase of implement flow) on

September 2014


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Fig. 9. OED graph showing operation points on September 2012


116

Fig. 10. OED graph showing operation points on September 2014


117

From these two graphs we can conclude very quickly and easily that:

The overall leanness situation of the production for this Sakurai printing machine is better on

September 2014 in related to the same machine on September 2012. This is because totally the

operation points in Fig. 10, are closer to target line than the points on Fig. 9.

As shown in both graphs, identifying the leanest and the most unwanted days of these two months is

very simple. These are shown as point A (for the leanest day) and point C (for the day with most

Type-2 waste) in Fig. 10 for September 2014.

By OED graphs, we can identify the OED of any point on the graph approximately but very easily.

For example, in Fig. 10, point C is positioned between two parallel OED lines 40% and 50%. So its

OED should be something about 43%.

By counting the number of points between any two OED lines, i.e. dashed and oblique lines in the

graphs, we can figure out which OED interval, i.e. the zone between two parallel OED lines,

encompasses the most operation points. For example, as seen in Fig. 9, it is the interval between

OED 50% and OED 60% which encompasses 8 points. It means that for 8 of 21 days that their

operation points have been displayed in the graph, the Type-2 waste are more than 50%.

In a commercial printing company, usually many jobs are produced every day and many different

printing machines are used. For these reasons, selecting a day of working for cause-and-effect

analysis of waste for a printing machine is not a previously specified task. Commonly, this

managerial work is done according to the customer claim or based on the in charge manager‘s desire.

By using OED graphs, however, the right days can be selected visually. For example, in Fig. 10, the

point C as the worst day or preferably any days on the left side of OED line 70% or OED line 80%

can be selected.

Finally, what is most important is that the improvement which is shown in the OED graph of September

2014 in related to the OED graph of September 2012 is not accidentally. On September 2012 we were at

the end of the phase 3 (i.e. the phase of discovering waste), but, on September 2014 we were nearly at the

end of the phase 7 of the lean transformation road map (i.e. the phase of implement flow) as shown in Fig.

3. In other words, after two years of running the lean implementation project in the printing house, we

eliminated many causes of the Type-2 waste through establishment of some management systems such as

production control and scheduling system or work standardization.

6. Discussion & Conclusion

The most important difference between these two is that OEE highly depends on Type 1 waste but OED

is independent of Type 1 waste and is a good criteria for measuring the waste which must be observed in

implementation of any kind of projects.

Another difference between these two is that OEE differs only by a change in the number of orders in one

day; in other words, if it brings about the decreased of OEE. But this is not true about OED; it is possible

that in two different days with different number of orders have the same OED.

Although OEE is used in some references for measuring the performance of printing machine, the cases

are limited to the mass production in packaging printing and publication printing and do not include

commercial printing.


118

The aim of OED development is to have a measuring tool by which it could be possible to simply and

visually indicate the leanness of commercial printing machines which received a large number of orders

in daily manners.

In practice, what we need in using the OED graph, for any printing machine in any day, is restricted to the

number of orders (n) and total printed sheets accepted by the customers. Obviously, these are simply

available in any printing house from usual production or sales records.

The development of OED has been also the successful result of integrating action research cycle with the

phase of discovering waste in the lean transformation journey.

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