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www.elsevier.com/locate/procedia
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June
Manufacturing Engineering Society
Conference
2017, International
Vigo (Pontevedra),
Spain2017, MESIC 2017, 28-30 June
2017, Vigo (Pontevedra), Spain
Use of the ppm and its function in the production process
Use of Engineering
the ppm Society
and itsInternational
functionConference
in the production
Manufacturing
2017, MESIC process
2017, 28-30 June
2017,
Vigo
(Pontevedra),
Spain
L. Bebr, K. Bícová, H. Zídková
L. Bebr, K. Bícová, H. Zídková
Costing models for capacity optimization in Industry 4.0: Trade-off
between used capacity and operational efficiency
University of West Bohemia in Pilsen, Univerzitní 22, Pilsen 301 00, Czech Republic
University of West Bohemia in Pilsen, Univerzitní 22, Pilsen 301 00, Czech Republic
Abstract
Abstract
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
Today's market environment in both engineering and non-engineering industry considers an indicator ppm as one of the most
a
Today's
environment
in both
engineering
non-engineering
industry
considers
indicator
ppm
as one of
most
importantmarket
regarding
the evaluation
of the
efficiencyand
their
processes.
This
paper
describesan
what
the ppm
represents
andthe
what
to
University
ofofMinho,
4800-058 Guimarães,
Portugal
important
regarding the
evaluation
of the efficiency
of is
their
processes.
paper
describes
what theused
ppmbyrepresents
and what
to
Unochapecó,
89809-000
Chapecó,
SC,
Brazil
use his dimensionless
value
that describes
it bfor.
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the customer,
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that describes
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how theThis
ppmpaper
shouldwill
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with thoseit requirements
of the customer
also concern
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alternatives
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as
how a supplier
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requirements
customer
paper will
also concern
the other
alternatives
of
evaluation
of an industrial
process,
process
eligibility,ofitsthe
target
valuesThis
and setting
of specific
boundaries
in order
to become
evaluation
of
an
industrial
process,
the
process
eligibility,
its
target
values
and
setting
of
specific
boundaries
in
order
to
become
noncritical and the derived ppm fulfil customer requirements. Finally, it shows an example of the use of all connections with the
Abstract
noncritical
and in
thethe
derived
ppm fulfil customer requirements. Finally, it shows an example of the use of all connections with the
ppm
described
contribution.
ppm
described
in the contribution.
©
2017
The Authors.
Published by Elsevier B.V.
Under
concept
of "Industry
production processes will be pushed to be increasingly interconnected,
© 2017 the
The Authors.
Published
by
Elsevier
B.V.
Peer-review
under
responsibility
of Elsevier
the4.0",
scientific
© 2017 The Authors.
Published by
B.V. committee of the Manufacturing Engineering Society International Conference
Peer-review
under
responsibility
of
the
scientific
committee
of the
Manufacturing
Engineering
Conference
information
based
on
a
real
time
basis
and,
necessarily,
much
more efficient.
In this Society
context,International
capacity optimization
2017.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
2017.beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value.
goes
Keywords:lean
ppm, process
capability,and
quality
Indeed,
management
continuous improvement approaches suggest capacity optimization instead of
Keywords: ppm, process
capability,
quality
maximization.
The study
of capacity
optimization and costing models is an important research topic that deserves
contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical
1. Introduction
model
for capacity management based on different costing models (ABC and TDABC). A generic model has been
1. Introduction
developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s
Today's
environment
in both engineering
and non-engineering
considers
ppm
as one
value.
The market
trade-off
capacity maximization
vs operational
efficiency is industry
highlighted
and it an
is indicator
shown that
capacity
Today's
market
environment
in both
engineering
and
non-engineering
industry considers an indicator ppm as one
of
the
most
important
regarding
the
evaluation
of
the
efficiency
of
their
processes.
optimization might hide operational inefficiency.
of the
most important
regarding
the
of
efficiency
of theirThe
processes.
Unfortunately,
many
cases
is evaluation
interpreted
thetheppm
value poorly.
mostly case is mainly due to the quality
© 2017
The Authors.inPublished
by Elsevier
B.V.
Unfortunately,
in
many
cases
is
interpreted
the
ppm
value
poorly.
The
mostly case
is mainly
toConference
theinterpret
quality
required
by
the
customer
from
the
supplier.
We
can
here
find
many
misunderstandings
about
how
to due
correctly
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering
Society
International
required
by ppm
the customer
supplier. We
can here/find
manyBiggest
misunderstandings
how
to correctly
interpret
and use the
indicatorfrom
in thethe
relationship
a customer
supplier.
a problem isabout
coming
from
a misconceptions
2017.
and
useppm
the mean
ppm indicator
in the relationship
a customer
supplier.capability
Biggest a C
problem
is coming from a misconceptions
about
when customers
request zero
ppm and/ process
pK = 1.33. This request from a customer
about ppm
mean
customers
request
zero ppm
and
processOperational
capability
CpK = 1.33. This request from a customer
Keywords:
Cost
Models;
ABC;
TDABC;hasn't
Capacity
Management;
Idle
Capacity;
Efficiency
clearly
shows
thatwhen
the
customer
understood
and
understands
his processes.
clearly
thethe
customer
hasn't
understood
and understands
his an
processes.
Thisshows
articlethat
shows
cohesion
of ppm
and process
capability with
understanding of how the ppm value should
article shows the cohesion of ppm and process capability with an understanding of how the ppm value should
be This
interpreted.
Introduction
be1.interpreted.
The cost of idle capacity is a fundamental information for companies and their management of extreme importance
in
modern
systems.
In general,
it isB.V.
defined as unused capacity or production potential and can be measured
2351-9789 ©production
2017 The Authors.
Published
by Elsevier
2351-9789
2017responsibility
The
Authors.
Published
by Elsevier
B.V.hours
Peer-review
of
the scientific
committee
of the Manufacturing
Engineering
Conference
in
several©under
ways:
tons
of production,
available
of manufacturing,
etc.Society
The International
management
of the 2017.
idle capacity
Peer-review
under Tel.:
responsibility
the761;
scientific
committee
the Manufacturing Engineering Society International Conference 2017.
* Paulo Afonso.
+351 253of
510
fax: +351
253 604of741
E-mail address: psafonso@dps.uminho.pt
2351-9789 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
2351-9789 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
10.1016/j.promfg.2017.09.122
L. Bebr et al. / Procedia Manufacturing 13 (2017) 608–615
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609
2. Methodology ppm and process capability
If we are based on the dependence of production where the product is produced, and the quality control where the
final product has to be checked and units which do not fulfill the specifications are sorted out, then these
nonconforming units do form number ppm.
Detection strategy of nonconforming units (sorting) is unnecessarily expensive and uneconomical because it
requires the control only after expensive production has already taken place. Instead, it is more efficient to establish a
strategy to prevent and avoid the loss of production resulting from producing nonconforming products. This can be
achieved by obtaining information about the production process and such its analysis so as to be able to influence the
production process itself. This prevents generation of complaints from customers and we hereby achieve reduction of
ppm - parts per million, which mainly make up the frequent nonconforming products.
2.1. Parts per million
PPM is an expression of one millionth of a whole, but technically the use of this acronym is incorrect. According
to technical standards ISO 80000-1, Article 6.5.5 the correct expression is in powers of tenths. [5]
Calculation of this dimensionless number is used in manufacturing and other companies to monitor non-conforming
parts, the quantity of nonconforming products from a single batch or in the monitored period divided by the total
number of units in the same batch or during the monitoring period, and then multiplied by 106.
��� �
��
��
(1)
∗ 1 000 000
Where:
ND
- number of defect units; NS
- number of supplied units
As can be seen from the above formula ppm is used as an indicator for comparing production efficiency, supplier
performance, or to compare businesses, etc. [5]
2.2. Process
This brings us to how to influence ppm itself, to suit customer requirements. Supplier’s quality ppm for example
evaluates vendors based on their deliveries. "Zero ppm" is a key indicator of the quality of the supplier and if the
supplier ceases to fulfill this key indicator, regardless of other evaluation criteria, he is required to implement effective
corrective measures to bring the process to a state as agreed with the customer.
Therefore, our attention must be directed at influencing the process itself. The previous text already implies that
we must not focus on the ending of the process, where the product is finished, but to establish a prevention strategy to
avoid unnecessary costs. That means trying to constantly improve the production process and try to keep the process
capable. We can start with basic statistical approaches and visualization of measurement data. Here, its role is also
played by customer demands, usually defined by customers themself, which analytic outputs he requires for the
deliveries as proof of quality.
2.3. Statistic process control and control charts
To watch the behavior of processes, the probably most used statistical method in manufacturing organizations used
is the method SPC. The basic aim of the regulation is improving quality and bringing the process to a stable state and
to maintain it.
Fig. 1. SPC.
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Resolution and identification of the causes of variability (random or definable), which affect the process, is
performed by use of control charts. These diagrams are in fact graphic representations of the process variability in
time with upper and lower control limits and enable:
 detection and monitoring of the process,
 distinction of special causes of variation from random,
 it's a tool for process control. The regulation contains measures to be taken if the results are nonconforming.
See fig. 2.
Fig. 2. Process control. [8]
2.4. Variability
If we try to improve the manufacturing process, then we should primarily define and uncover the causes that give
rise to substantial and undesirable changes in the monitored process and thus affect the quality characteristics, which
we follow.
The causes are chance or assignable.
Random effects - chance causes [7]:
 The CSN 01 0265, the random fluctuations of the manufacturing process attributes the effect of random
impacts
 In 8258 the CSN IS random fluctuations in the production process is understood as a consequence of global
action accidental causes that are inherent component of the manufacturing process.
Systematic effects - assignable causes [7]:
 definable causes refers to an identifiable cause, producing real change in the manufacturing process; while ISO
8258 requires these types of causes to be identified, to be remedied and measures taken to prevent their
reoccurrence.
 CSN 01 0265 merely states that the systematic influences can run concurrently with some random influences
and act upon the values regulated quality indicator. Thus the concept of ISO 8558 more comprehensive, and
further new term "identifiable cause" shall characterize above formulated requirements: identification->
correction-> Prevention
Through detection and reduction of such identifiable causes we therefore strive to eliminate systemic non-random
variability (i.e. Variability). These non-random causes are caused by different factors, which vary according to the
nature of the production process, for example:
 Influence of environment (humidity, temperature)
 Influence of the measuring device (wrong calibration, damage)
 Influence of machines (poor alignment, adjustment, maintenance)
 Influence of material (defects, poor delivery)
 Influence of employee (shift influence on the measurements, the new operator)
 Etc...
There are many factors that can influence and distort the results. Some changes are coming gradually, and therefore
they are even more difficult to detect (gradual wear and tear of machine or tools ...).
Among the methods for revealing fragility / process variability and helping to find causes belongs e.g.:
 Analysis of Repeatability and Reproducibility
L. Bebr et al. / Procedia Manufacturing 13 (2017) 608–615
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





611
FMEA
FTA
Diagram of causes and effects, also Ishikawa diagram
Pareto analysis
8D-Report
...
We can also meet various types of FMEA like FMECA where different scales are assigned to values S, O, and D.
In some analyzes, using a scale 1 to 4 or 5, in some, such as FMECA widely used in the automotive industry to analyze
the proposal known as DFMEA (Design FMEA) and analysis of the production process known as PFMEA (Process
FMEA) the same scale for all three attributes 1 to 10 is used.[4]
Besides, to all processes the basic methodology known as PDCA can be applied: "Plan - Do - Check - Act"
PDCA can be briefly described as follows:
 Plan: establish the objectives and processes necessary to deliver results in accordance with customer
requirements and the organization's policies;
 Do: apply processes;
 Check: monitor and measure processes and product against policies, objectives and requirements for the
product and report the results;
 Act: take actions to continually improve process performance.
Revealing of identifiable causes is always followed by measures such as:

employee training, sorting of materials, new machine, machine adjustment or maintenance, Etc...
2.5. Process capability
We also have to deal with eligibility of process, respectively whether the process is capable of producing products
within the tolerance limits, which are based on customer requirements.
Process capability can be defined as the ability to consistently achieve predetermined quality criteria. When
determining eligibility several important indicators are determined, which describe the process and are based on
comparing the natural fluctuation of the actual process against technological specification. They are sometimes
considered a quality "grade" that the customer demands. [10,11]
The process, where all the measurements are within the tolerance limits, is viewed as capable and may be illustrated
by Fig. 3 below:
Fig. 3. Gaussian curves with USL and LSL [6]
If to a normal distribution we add time, then we can monitor variability in the process and its capability, as shown
in Fig. 4.
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LEGEND:

LSL/USL:
lower/upper select limits

LCL/UCL:
lower/upper control limits

LWL/UWL
lower/upper warning limits
Fig. 4. Gaussian curves in the time.
Assuming statistical mastering of the process can be expressed by its capability indicators (capability indexes) CP
and CPK.
Regulated process is automatically set on the major characters. If the value of preliminary capability Cpk:
> / = 1.33 customer demands mostly use SPC
is lower than 1.33 customer requires corrective action.
In the short term may be cp = Cpk, and therefore it sometimes simplifies quality requirement, eg. In the automotive
industry, we want CPK to be> / = 1.67 and we mean short term horizon.
Capability indexes are based on a comparison of the natural variation in the actual process against technological
specification. Sometimes, they are considered a quality grade that the customer requires.
Capability index cp generally expresses a potential degree of process capability to ensure that monitoring quality
characteristic are lying within the tolerance limits. The ratio of actual and allowable variability character values of
quality, regardless of the location within the tolerance field.
Cpk capability index tracks the variability not only of the observed sign of quality, but also of its position relative
to tolerance limits.
The movements of the Gaussian curve between limits according to different values of Cp and Cpk and thus influence
the process may be followed on the following figures with examples:
Fig. 5. To get an idea of the value of the Cp and CpK statistic for varying process.
L. Bebr
et al. /Manufacturing
Procedia Manufacturing
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Fig. 6. To get an idea of the value of the Cp and CpK statistic for varying process - easy explained.
3. Results and Discussion
CP and CPK values and influencing Gaussian tolerance limits brings us back to our ppm value. The following
comparative table shows ppm and CPK assuming normal size distribution process, where we can expect under the
relevant index capability Cpk number of defects in million pieces -> ppm.
Table 1. Conversion table of CpK – ppm.
The comparative table then brings us to the concept of sigma. It is the standard deviation of the process
characteristics. It can be described as a deviation indicating the extent of the differences or dissimilarities in a selected
group of items or process data. How we may see on the following figure, we try the process to reach the lowest ppm
and the highest values sigma values for highest quality.
Fig. 7. Rule 6 Sigma indicating indicator ppm and cpK [12]
Here we must note that the higher sigma we require, the lower the ppm value becomes and consequently the number
of non-conforming products resulting from the process. However, on the other side it increases the cost of
management, control and process regulation. Therefore for example, required index of process capability CpK of 1.33
is used for classical companies, allowing us to automatically generate 66 ppm. Within the automotive industry CpK of
1.67 is required, where we should approach the approximate "zero" Zero ppm or defect.
L. Bebr et al. / Procedia Manufacturing 13 (2017) 608–615
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7
In the following table we have discussed the connection sigma / PPM / cost / quality competitiveness in Table 2.
And then what are the typical measures that are taken during the process variability displayed see Table 2:
Table 2. Cp and sigma with typical actions
4. Conclusions
Finally, it shows an example of the use of all connections with the ppm described in the contribution.
Example:
Capability
Based on the stability of the process confirmed by control charts, we can therefore evaluate other indicators of the
manufacturing process, and its capability. Table I. (see above) shows individual capability indexes depending on
Sigma - process level. The process capability is usually understood from Cp = 1.33, i.e. from the the level of 4σ.
As stated above, the normal distribution is usually considered by value of 1.33. The value of 1.33 seemingly appears
to be a basic rule, but for the automotive industry today it can be seen as too insufficient. For this area, the value
greatly increases, which is therefore more stringent criterion for capability. Therefore, in this case the value of 1.67 is
chosen. From the perspective of the manufacturer it is tried to increase this criterion i.e. to tighten it.
Evaluation of eligibility is the following:
The minimum value ≥ 1.67 => capable X Minimum value <1.67 => not capable
Indicator Cp
Indicator Cp (capability index) generally expresses "what we can achieve." Cp is thus defined as the ratio of the
specified range and the range of the real process. This indicator can be expressed as:
�� �
�������
�
��
��
�
�����������
��������
�����
��
(2)
� �����
LSL USL are the lower and upper technical limit that are set by customer ̅ R is the average margin calculated from
measured values, and d2 is a constant varying depending on the extent subgroups.
Indicator Cpk
Cpk indicator expresses the general, "what we have actually achieved." Calculate pointer Cpkl and Cpku and the
smaller one is equal Cpk.
��� �
���������� ��������
���� �
�
������
��
�
��
��
��
�
(3)
� �����
���������������
��������
��
�����
� ����� �4�
���� �
������
�
��
��
�
���������������
��������
�����
��
In this case, the assessment of capability is as follows:
The minimum value ≥ 1.67 => 5.695 ≥ 1.67 => It can be said that this process is capable.
� �����
(5)
8
L. Bebr et al. / Procedia Manufacturing 13 (2017) 608–615
L. Bebr/ Procedia Manufacturing 00 (2017) 000–000
615
Prerequisites and recommendations
Before identifying key indicators there is a generally accepted convention that the limits for technological
specification are set at a distance of ± 3σ from the nominal value (σ is the standard deviation of the process). This
corresponds to the limits of conventional control diagrams by which the process is monitored and it is to be brought
into statistically controlled state.
I.e. Determination of capability is assuming confirmed process stability, which is confirmed by the graphs of
control charts.
If the values are favorable is explained in Fig. 5, where is illustrated the rule 6 Sigma with the ppm index and CpK
capability values for individual bands interpreting the visual appearance of the Gaussian curve. Here in the article we
can see the return to ppm and continuity of the ppm indicator with the entire process capability. According to the
calculated CpK we can see that the ppm value falls to level 3 SIGMA, and this is satisfactory when the customer's
requirements will be CpK, i.e. process capability of 1.67.
As a follow-up, the main idea of the paper is to show the relationship of the ppm indicator with the other quality
tools to see what the ppm value interprets and how the indicator is related to the process itself.
Acknowledgements
This post was created under the project SGS-2016-005: Research and development for innovations in the field of
mechanical engineering technology - machining technology.
References
[1] Keki R. Bhote, The power of ultimate Six Sigma, AMACOM Div American Mgmt Assn, 2003.
[2] Engineering Statistics Handbook. Available at: http://www.itl.nist.gov/div898/handbook/pmc/section1/pmc16.htm (2017, January).
[3] Concept Of Six Sigma. Available at: http://slideplayer.com/slide/10771476 (2017, January).
[4] Ing. Jaroslav Skopal: Uplatnění technických norem v malých a středních strojírenských firmách Příručka č.4
[5] Available at: https://cs.wikipedia.org/wiki/Parts_per_million (2017, January).
[6] Available at: http://www.itl.nist.gov/div898/handbook/pmc/section1/pmc16.htm (2017, January).
[7] ČSN ISO 8258, Shewhart control charts, April, 1994.
[8] Available at: http://www.chaloupka-kvalita.cz/spc-merenim (2017, January).
[9] Available at: https://www.slideshare.net/WithAdrian/spc-7558258 (2017, January).
[10] R. Meran, A. John, O. Roenpage, Ch. Staudter, Six Sigma + Lean Toolset (Mindset for Successful Implementation of Improvement Projects),
Springer, Berlin, 2013.
[11] StatSoft, Analýza způsobilosti procesu. Available at: http://www.statsoft.cz/file1/PDF/newsletter/
13_08_07_StatSoft_Analyza_zpusobilosti_procesu.pdf (2014, July).
[12] Six Sigma Workshop. Available at: http://www.slideshare.net/nandigama/six-sigma-workshop-for-world-bank-chennai-india (2016, June).