Key performance indicators for manufacturing operations management in the process industry

Full text

Abstract
-
The international standard ISO 22400 has
defined a set of Key Performance Indicators (KPIs) to
evaluate the performance of manufacturing operation.
However, the defined KPIs seem to be inspired from the
discrete production context, and hence do not automatically
fit the process industry context. The process industry is
defined as the industry in which the raw material undergoes
conversion during a continuous process in order to become
finished products. To make the defined KPIs more suitable
for evaluating the operational performance in process
industry, this paper analyzes the different characteristics of
process industry and discrete industry, and proposes a new
framework for organizing KPIs in process industry. Some
modifications are discussed to make the proposed ISO 22400
KPIs fit to the process industry. Such a study can provide
useful ideas for manufacturing engineers and decision-
makers to define and measure suitable KPIs for
performance evaluation in process industry.
Keywords
-
Key Performance Indicators (KPIs),
manufacturing operation management, ISO 22400, KPIs
framework, process industry
I. INTRODUCTION
With the rapid scale expansion and increasing diverse
market incentive in modern manufacturing industry,
production enterprises have to maintain sufficient
flexibility, high quality standards, productivity and
sustainability to combat these emerging challenges and
achieve their full economic potential. Therefore,
manufacturing industries nowadays implement
performance measurement system to evaluate the
operating state of their manufacturing activities
[1, 2]
. In
order to quantify the efficiency and effectiveness of
manufacturing operations management, a set of detailed
indicators are defined to realize the strategic goals of
process management and improvement, which are called
Key Performance Indicators (KPIs
)[3, 4]
.
KPIs are defined as the quantifiable and strategic
measurements that reflect enterprise’s critical success
factors
[5]
. The modern tools of information technology
provide opportunities to gather a large set of necessary
data and estimates, supporting the acquisition and
calculation of KPIs. Hence, the KPIs used to evaluate the
performance of manufacturing production systems have
been given more attention both in the academia and
industry in recent years. Margret et al.
[6]
uses KPIs as an
interface between scheduling and control to maximize
plant performance. Gokan et al
[7]
focuses on measuring
energy efficiency performance of manufacturing plants
and provides a seven step method to develop production-
tailored and energy-related key performance indicators,
using these supports the identification of weaknesses and
areas of energy efficiency improvements related to the
operation management. Arinez et al
[8]
combines the
traditional production KPIs with energy KPIs through a
discrete event simulation modeling method and gives
benchmarks to assess the energy performance of
production system, process and facility. Zhu et al
[9]
discusses the relationship of multi-KPIs when evaluating
manufacturing equipment and proposes a multi-KPI
coordination model to discern and balance the relationship
among multi-KPIs.
However, the most critical issue of evaluating the
manufacturing operations management is not defining the
KPIs in general, but identifying and selecting the most
useful KPIs according to the application. The appropriate
selection and improved understanding of the KPIs could
help the manufacturing enterprises fulfill the desired
business objectives
[10]
. Addressing this issue, the
international standard series ISO 22400 in its latest update
– ISO 22400-2:2014/Amd1:2017 – presents a total of 38
KPIs to evaluate the performance of manufacturing
operations. Four out of these 38 KPIs, are however
introduced in an amendment and will not be considered in
this article. In the standards, the KPIs are described by
means of their formula, corresponding elements, unit of
measure, timing and other characteristics. Despite the
efforts to make the standards generally applicable to all
manufacturing industries, it seems to be primarily
designed for evaluating performance of the discrete
industry. Indeed, in some cases the ISO 22400 explicitly
indicates that the indicators are unsuitable for continuous
processes. However, the indications are often imprecise
and ambiguous, and the information provided is
sometimes fragmented; thus, the indications of the KPI
applicability in the standards should not be considered as
rigorous constraints.
Taking the characteristics of the process industry into
account, improvements and corrections of these 34 KPIs
should be made in order to evaluate the performance
properly and accurately. Though it should be noticed the
standards explicitly indicate that some KPIs are unsuitable
for continuous processes. Based on a survey conducted at
a large process industry in Sweden, this paper evidences
the difference between the process and discrete industry
in relation to the ISO 22400 standard. In doing so, the
KPIs requiring modifications are identified and amended
Key Performance Indicators for Manufacturing Operations Management in
the Process Industry
Li Zhu
1,2
, Charlotta Johnsson
2
, Jacob Mejvik
2
, Martina Varisco
3
, Massimiliano Schiraldi
3
1
Department of Control Science and Engineering, Dalian University of Technology, Dalian, China
2
Department of Automatic Control, Lund University, Lund, Sweden
3
Department of Enterprise Engineering, University of Rome Tor Vergata, Rome, Italy
1
li.zhu@control.lth.se
978-1-5386-0948-4/17/$31.00 ©2017 IEEE 969

accordingly. Hence, the ISO 22400 standard becomes
suitable for the process industry while maintaining its
generality.
To support these contributions, this paper is organized
as follows. The second section recalls the typical
characteristics of the process industry and points out the
difference between process and discrete industry. A novel
framework for organizing KPIs in the process industry is
proposed to help structuring the KPIs and associated
measurement elements in section 3. The section 4 presents
some adjustment and modifications to make the ISO
22400 KPIs more suitable for the process industry. The
section 5 discusses the contribution and the future
research directions. Finally, concluding remarks are given
in section 6.
II. CHARACTERISTICS OF PROCESS INDUSTRY
Two main types of manufacturing industry are
generally identified by the way the products are realized,
which are called process industry and discrete industry
[11]
.
The process industry is defined as the industry in which
the raw material undergoes conversion during a
continuous process in order to become finished
products
[12]
, alternatively referred to as continuous
industry. While the discrete industry is related to the
process where successive changes of discrete and analog
states occur during the whole process, and the production
is based on discrete time moments of instant and non-
instant controls
[13, 14]
. The process and discrete industries
have different characteristics, and hence can have
different needs for performance measurements and,
therefore, different needs for the KPIs. By investigating
the differences between the typical process and discrete
industries, some general conclusions can be drawn. To
clarify what is meant by the respective terms in this paper,
a summary of the difference is presented in Table 1.
T
ABLE I
The difference characteristics of process and discrete industry
Difference Process industry Discrete industry
Production modality Continuous
outflow
Discrete entity
Production orientation Disassemble assemble
Production process Continuous Discontinuous
Production form Fluid-based Pieces and parts
Work unit Unit Work cell
Production transparency Invisible Visible
Production runs Open-ended Well defined
Operation manner Steady state On-off
Furthermore, the characteristics of the production
industry can be described based on three central
perspectives: object-type, mode-type and driver-type
[15]
.
Firstly, the object transformed in the flow can be of
two different types, discrete object (DO) or continuous
object (CO), including several intermediate alternatives of
production batching.
Secondly, the transformation can be performed in
different modes ranging from one-time occurrence mode
(OM) to continuous mode (CM), including several
intermediate alternatives of intermittent production.
Thirdly, the driver of the flow depends on the origin of
the production trigger: either production is triggered by an
actual customer order (CD) or not, which is the case when
production is expected to meet future orders (SD),
including several intermediate alternatives of production
cases, among which assembly-to-order production.
From these three perspectives, the characteristics of
the industry can be clearly described and distinguished.
Figure 1 illustrates the three perspectives in a cube where
the typical characteristics for the process industry are
shaded in dark grey.
Fig. 1. The production characteristics from three perspectives.
III. A FRAMEWORK FOR ORGANIZING KPIS IN
THE PROCESS INDUSTRY
A framework can provide useful ideas for
manufacturing engineers and decision-makers aiming to
define and measure suitable KPIs for performance
evaluation in process industry. The framework will help
structuring the KPIs and the associated measurement
elements.
Basic measurements, called measurement elements,
have to be collected in order to calculate the KPIs.
According to ISO 22400 Part2, these elements – i.e. the
“relevant measurements for use in the formula of a key
performance indicator” – can be divided into three main
categories; time, quality and logistical. The logistical
elements contain both quantity and inventory
measurements. In this paper, the focus will be on the two
categories time and quantity measurement elements.
The time measurement elements are comprised of
data related to the duration of the production process,
including planned operation time and actual operation
time. Moreover, the actual operation time can be divided
into production time and down time to increase the level
of detail. The time elements are similar to the definitions
in ISO 22400.
Since in the process industry it is likely that the
product being processed in a certain production unit is not
visible for the operators and it is also difficult to measure
what is happening inside the equipment, input and output
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Proceedings of the 2017 IEEE IEEM

quantities often become the only available logistical
measurement elements.
The input includes both raw material as well as the
energy medium, which are the fundamental components
for producing the product. Likewise, the output can be
divided into the desired product, by-product and scrap.
When doing this division it is however important to also
consider expected future customer orders. Furthermore,
since the process industry is dominated by raw material
and energy costs, the production, quality and energy
aspects are the three most important when evaluating the
performance. It should also be noted that even though it
is continuous outflow and disassembly-oriented
production, the performance of the whole process is
directly influenced by the performance of the production
equipment. Therefore, the KPIs for process industry
could be analyzed from two levels, process KPIs and
equipment KPIs.
Figure 2 presents the proposed framework for
organizing the KPIs in the process industry.
Fig. 2. The framework for organizing the KPIs in the process industry.
3.1. Measurement elements
The measurement elements, middle part of the
Framework in Figure 2, are comprised of the data directly
monitored and collected during the production process.
These are used to describe and calculate the equipment
KPIs and process KPIs. Below the list of the basic
measurement elements according to characteristics of the
process industry company is provided.
3.1.1. Time elements
Planned operation time (POT): the scheduled time
during which the equipment or process can be utilized.
Actual busy time (ABT): the actual time for execution
and production.
Actual production time (APT): the actual time in
which the equipment or process is producing.
Actual down time (ADT): the time in which the
equipment or process is delayed due to malfunction-
caused interruptions, minor stoppages and other
unplanned events.
We obtain:
ABT = APT + ADT
The relationship among the time elements is presented
schematically in Fig 3.
Fig. 3. The relationship among the time elements.
3.1.2. Quantity elements
Input quantity (IQ): the material used for equipment or
process to produce the output.
Raw material quantity (RMQ): the feedstock used to
produce the output.
Energy medium quantity (EMQ): the energy material
used to provide the necessary energy for production.
Output quantity (OQ): the items that satisfy the
requirements of production, including products and
energy. It should be noted that the commonly used term in
industry is produced quantity.
Desired product quantity (DPQ): the primary product,
which the production process is originally designed for.
By-product quantity (BPQ): the secondary product
generated during the production of the primary product.
Scrap quantity (SQ): The result of the production
process which cannot be transformed in product,
including waste materials.
Thus, IQ is comprised of RMQ and EMQ while OQ is
a set of DPQ, BPQ and SQ. Hence, the relationships can
be expressed as:
{
}
IQ RMQ,EMQ=
{
}
OQ DPQ, BPQ,S Q=
The relationships among the quantity elements are
presented in Fig 4.
Fig. 4. The relationships among the quantity elements.
3.2. Equipment KPIs
The equipment KPIs, lower part of the Framework in
figure 2, are calculated based on the measurement
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elements. Based on the survey of ISO 22400 KPIs used in
the process industry, the high-relevance equipment KPIs
include allocation ratio, utilization efficiency and
equipment load ratio. Due to the characteristics of process
industry, some of the calculations of these KPIs are
different from the one defined in the ISO 22400 and
should be adjusted. According to the measurement
elements, the equipment KPIs can be descried as follows:
Allocation ratio (AR): the percentage of actual
production rate and maximum production rate. It provides
the valuable information on the allocation of available
capacity.
()
()
Actual OQ ABT
AR
M
aximum OQ APT
=
Utilization efficiency (UE): the ratio between actual
production rate and planned production rate.
()
()
Actual OQ ABT
UE
P
lanned OQ POT
=
Equipment load ratio (ELR): the ratio of actual
produced quantity in relation the maximum equipment
production capacity (EPC), which shows what the
equipment parts actually produce when it is in progress to
the load it could achieve.
()
Actual OQ
ELR
M
aximum OQ APT
=
3.3. Process KPIs
The process KPIs, upper part of the Framework in
figure 2, are calculated based on the measurement
elements. Based on the survey at the process industry
company of ISO 22400 KPIs used in process industry, it
is concluded that the KPIs can be divided into three
categories: production, quality and energy process KPIs.
Production process KPIs includes throughput rate and
technical efficiency. Quality process KPIs includes
quality ratio, actual to planned scrap ratio, scrap ratio and
finished goods ratio, and comprehensive energy
consumption is the energy process KPIs.
3.3.1. Production process KPIs
Throughput rate: measures the produced quantity per
order in relation to the actual busy time, and often used in
the name of production rate (PR) in process industry.
Technical efficiency (TE): the relationship between the
production time and the busy time.
3.3.2. Quality process KPIs
Quality ratio (QR): the relationship between the
desired product quantity and the produced quantity.
Actual to planned scrap ratio (APSR): the ratio relates
the actual quantity of scrap to planned scrap quantity.
Scrap ratio (SR): the relationship between the scrap
quantity and the produced quantity
Finished goods ratio (FGR): the ratio of the desired
product quantity produced in relation to the input
material, including energy material quantity.
3.3.3. Energy process KPIs
Energy consumption (EC): the ratio between all the
energy consumed in a production cycle and produced
quantity.
E
MQ
EC OQ
=
IV. DISCUSSION
The ISO 22400 defines and describes a set of KPIs
for manufacturing operation management in general.
Although the standard intends to make the defined KPIs
generally applicable to manufacturing industries, the
indicators result to be primarily designed for the discrete
industry. No matter which perspective of analysis, such as
production objects, modes and drivers, there are vast
differences between process industry and discrete
industry. In order to identify and redefine KPIs in the ISO
22400 to properly evaluate performance in a process
industry setting, the different characteristics of process
and discrete industry are analyzed. In addition, the
practical requirements in the process industry are acquired
through the survey of the process industry company in
Sweden. Based on the characteristics, the framework of
KPIs in the process industry is proposed to structure the
OQ
PR ABT
=
A
PT
TE
A
BT
=
D
PQ
QR OQ
=
actual SQ
APSR
p
lanned SQ
=
SQ
SR OQ
=
D
PQ
FGR
I
Q
=
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Proceedings of the 2017 IEEE IEEM

KPIs and related measurement elements. Such a KPI
framework provides useful ideas for manufacturing
engineers and decision-makers to measure, analyze and
define KPIs for performance evaluation in the process
industry.
Based on the study in this paper, further research can
be done in future work:
The relationship between different KPIs on different
levels. The process KPIs and equipment KPIs affected
each other. Dealing with the influence relationships is
important to evaluate the performance of process industry.
Some related KPIs need to be in-depth analyzed, such
as energy related KPIs. According to the specific
requirements of manufacturing enterprises, using suitable
KPIs to obtain the evaluation results.
 Apply the defined KPIs in the practical industry. Use
the actual data to verify and demonstrate the effectiveness
of the redefined KPIs in process industry. More case
studies should also be carried out.
V. CONCLUSION
This paper has two deliverables; on one hand, a KPI
framework for organizing the KPIs in the process industry
is proposed, and on the other hand, some KPIs defined in
the ISO 22400 are modified and adjusted in order to make
them more suitable for evaluating the performance of the
process industry. The KPI framework focuses on the
measurement elements and on two levels of KPIs in the
process industry, including the process KPIs and
equipment KPIs. According to the characteristic analysis
of the process industry, the corresponding adjustments
and modifications are made for the KPIs in the process
industry. By analyzing some typical KPIs applied in the
process industry, it is shown how this proposed
framework can make the ISO 22400 KPIs more
reasonable and appropriate for evaluating performance in
the process industry.
ACKNOWLEDGMENT
The authors gratefully acknowledge financial support
from China Scholarship Council. This work was
supported by the Fundamental Research Funds for the
Central Universities (DUT17RC(4)08) and the Swedish
Research Council through the LCCC Linnaeus Center.
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