See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/325929673
KPIs for for Manufacturing Operations Manageme Management: nt: driving driv ing the ISO22400 ISO224 00 standard towards towards practical practical applicabili appl icability ty Conference Paper · Paper · June 2018 CITATIONS
READS
0
39
5 authors authors,, inc luding luding:: Martina Varisco
Charlotta Johnsson
University of Rom e Tor Vergata
Lund University
5 PUBLICATIONS 3 CITATIONS
100 PUBLICATIONS 345 CITATIONS
SEE PROFILE
Massimiliano Schiraldi
SEE PROFILE
Li Zhu
University of Rom e Tor Vergata
Dali an Universi Universi ty of Technology
110 PUBLICATIONS 382 CITATIONS
14 PUBLICATIONS 10 CITATIONS
SEE PROFILE
SEE PROFILE
Some of the the authors of this publication are also working also working on on these related projects:
Automation, Autom ation, Control and Production System Systems s Vie View w project
Innovation and Entrepreneurship View project
All c ontent following this page was uploaded by Martina Varisco on Varisco on 10 July 2018.
The user has requested enhancement enhancement of the downloaded file.
Preprints of the 16th IFAC Symposium on Information Control Problems in Manufacturing Bergamo, Italy. June 11-13, 2018
KPIs for Manufacturing Operations Management: driving the ISO22400 standard towards practical applicability Martina Varisco1, Charlotta Johnsson2, Jacob Mejvik 3, Massimiliano M. Schiraldi4, Li Zhu5 1
Dept. of Enterprise Engineering, "Tor Vergata" University of Rome, Rome, Italy (e-mail:
[email protected]) 2 Dept. of Automatic Control, Lund University, Lund, Sweden (e-mail:
[email protected]) 3 Dept. of Automatic Control, Lund University, Lund, Sweden (e-mail:
[email protected]) 4 Dept. of Enterprise Engineering, "Tor Vergata" University of Rome, Rome, Italy (e-mail: schiraldi @uniroma2.it) 5 Dept. of Control Science and Engineering, Dalian University of Technology, Dalian, China, guest researcher at Department of Automatic Control, Lund University, Sweden (e-mail:
[email protected]) Abstract: A set of key performance indicators (KPIs) for manufacturing operation management is introduced in the ISO 22400 standard. However, KPIs are only defined at a high abstraction level and this hampers the standard’s practical adoption. In this paper, a framework is introduced in order to solve this weakness. Starting from the analysis of the relevant measurements used in the KPIs’ formulas, a classification model is introduced defining three possible application scopes - work orders, work units and production orders - to analyze each basic element of the standard. As a result, the KPIs defined in the ISO 22400 standard are more precisely specified and, as a consequence, also suitable to be straightforwardly implemented in performance measurement systems. Keywords: ISO 22400; enterprise system engineering; Key Performance Indicators (KPIs); performance evaluation; manufacturing, operations management.
1
INTRODUCTION
A performance measurement system (PMS) consists of a set of procedures and indicators that precisely and constantly measure the performance of activities, processes and the whole organization, and is a vital aspect in regard to the management of companies (Neely et, 2005). A PMS should be able to provide data for monitoring the past and the future performance, to strengthen the strategies to avoid introducing the conflicting indicators and support in providing data for benchmarking. PMSs do not only focus on financial procedures and indicators, they also often relate toconsumers’ aspects or internal processes (Lohman, 2004). Key performance indicators (KPIs) are considered the core of the PMS: they are defined as a set of measures that focus on the main critical activities (Parmenter, 2007). With the help of indicators, companies can prove an existing gap between the actual and desired performance. KPIs allow managers to identify the progress in activities and those to be improved, support the setting of new goals, help decision-making in order to reach the desired performance and improvement, allow the translation of a company’s missions into concrete actions, and to evaluate how well the company is pursuing its objectives (Weber, 2005). The growing competition and complexity results in an ever-increasing demand for more accurate performance monitoring and controlling, especially in manufacturing firms (Hwang et al., 2016). In this context, KPIs are used to evaluate the efficiency and effectiveness of the actions in the production process, part of the processes, or also the entire production system (Braz et al., 2011). KPIs in production lead, judge and assist the decision process (Neely
et al., 1996). Although performance measures in the manufacturing context have been widely studied for many years, further enhancements are required to help companies pursue their goals. Indeed, Lindberg et al. in 2015 state that many industries still don’t have proper indications on how their performance should be measured and improved (Lindberg et al., 2015). Moreover, the problem in sharing information between different factories, in order to be able to benchmark, is critical to compete (Fukuda & Patzke, 2010). KPIs should be properly selected to adapt the industry specificity, but general enough to be able to compare different operations. The actual technologies allow the collection of massive amount of data, and the sharing of this data between different sources. What data the sources should share is a critical decision. The International Organization for Standardization (ISO) has recently dealt with this topic in ISO 22400 standard “Automation systems and integration — Key Performance Indicators (KPIs) for manufacturing operations management”. The scope of ISO 22400 standard (henceforth, just “ISO 22400”) is ambitious, as it proposes a set of KPIs that aims to be industry and process independent. The ISO 22400 aims at defining the most important and commonly used measures for a manufacturing industry, and therefore it has been recognized for its potential contribution on manufacturing automation system development (Fukuda & Patzke, 2010). Nevertheless, in its current version, the standard shows some weaknesses. The understanding and interpretation of some concepts are difficult, resulting in inconsistences and imprecisions. As a result, there is an improvement potential to deal with before the standard can leverage its full potential.
16th IFAC Symposium - INCOM 2018, Bergamo, Italy. June 11-13, 2018
In this paper, a model to improve the application of the standard in simple production systems is introduced. A study for adapting it to model complex production systems is currently ongoing, working together with the members of the ISO workgroup which edit the ISO 22400 standard. However, this much more challenging objective is out of the scope of this paper. Here, the presented model - based on an analysis of the ISO 22400 - consistently focuses on a subset of elements and KPIs, providing more details for their computation. In the following paragraphs, the ISO 22400 is briefly recalled along with its strength and weakness (Chapter 2). Then, the proposed framework is presented (Chapter 3) along with an example of its application (Chapter 4). Finally, a discussion (Chapter 5) and a conclusion (Chapter 6) are presented. 2
measure the performance of work units, work centres and work areas. However, according to Lindberg et al., most of the KPIs can be used to measure performance at the “individual equipment, sub- processes, and whole plants” levels (Lindberg, et al., 2015).
THE ISO 22400 STANDARD: AN OVERVIEW
ISO 22400, developed by the International Organization for Standardization, is a multi-part standard. The ISO 22400 part 1 presents the overview, concepts and terminology. ISO 22400 part 2 covers the guidelines for computing and for measuring the components of a KPI, introducing 34 KPIs. Both Part 1 and 2 were officially published in 2014, while parts 3 and 4 are still in draft. Another 4 KPIs have recently been added in the amendment for energy KPIs in 2017. The standard aims to specify “an industry-neutral framework for defining, composing, exchanging, and using key performance indicators (KPIs) for manufacturing operations management […] for batch, continuous and discrete industries”. The method and general concepts of how to build KPIs, as well as the required terminology, are presented. Next, KPIs are defined. KPIs’ computations are based on the combination of basic elements. ISO 22400 aims at being industry-neutral and process independent. The relevance and usefulness of this standard has already been recognised in the literature. Lindberg et al. in 2015 proposed a method based on process signals to improve performance management, based on the indicators introduced by the ISO 22400 (Lindberg et al., 2015). Hwang et al., in 2016, introduced a framework that takes into account the hierarchical structure of a manufacturing system and its business activities in accordance with ISO 22400 and IEC 62264 (also known as ISA 95) (Hwang et al., 2016). Bauer et al., 2016, showed how the KPIs introduced by the standard can be used as “the interface between scheduling and control” to reach high performance in a process production plant. However, the standard is not without some disadvantages: although the general contents of the standard potentially allow for wide applicability, their generality can also act as an impediment. The high-abstraction level of the general descriptions of elements and KPIs make it difficult to understand precisely what they relate to. The elements and KPIs context are often imprecise and ambiguous, and the provided information is sometimes fragmented or implicit. For example: the ISO 22400 is intended for the manufacturing operation management (MOM) level of the functional hierarchy as introduced and explained in IEC 62264 standard (enterprise-control system integration). ISO 22400 uses the hierarchical structure introduced in IEC 62264, where the equipment structure is explained. Considering the hierarchies that the ISO 22400 relates to, the KPIs should be able to
Fig. 1 – ISO 22400 role-based equipment hierarchy related to MOM level, adapted from IEC 62264 The same authors stated that the KPIs introduced by the ISO 22400 are mainly proposed for the discrete manufacturing and are not suitable for the process industry. However, only three of the introduced KPIs by the standard are defined unsuitable for continuous production. Endrass (2013) identified a list of weakness the standard presents, and underlined that the data required to compute the indicators is vaguely described and sometimes missing. Moreover, he complained about the lack of guidelines to set goals and to improve performance. Nevertheless, the author recognized that presenting a unique architecture for any kind of individual production process is unrealistic (Endrass, 2013). On top of this, Kang et al. (2016) underlined the need for introducing other KPIs and elements able to better suit with a multi-stage production system (Kang et al., 2016). Because of these and other inaccuracies, the effort required by the reader to understand the standard and its applicability potential is therefore substantial. 3
A FRAMEWORK FOR IMPROVING THE ISO 22400
Despite the ISO 22400 provides a series of information about the elements and KPIs, those appears to be fragmented and often implicit, giving for granted many connections and relations that are instead difficult to highlight by the reader. In this paper, we aim to clarify and extend the existing elements and KPIs of ISO 22400, while maintaining their generality. Our contribution is to make the standard easier to understand and, most at all, more practically applicable. To this extent, three main improvements are proposed: 1) A classification model is defined, which highlights whether an element or a KPI is related to one or more work units and one or more production or work orders. 2) Subscript indices are introduced both for elements and KPIs, specifying their class and allowing a more formal definition of the computation formula. 3) The formulas for KPI computation in each class are refined. By understanding whether the KPI basic elements are referred to a work unit, to a work order or to a production order, or to
16th IFAC Symposium - INCOM 2018, Bergamo, Italy. June 11-13, 2018
combinations of these, it is possible to adapt the original generic formula reported in ISO 22400 to the desired context, thus enabling a precise computation of the KPI. In Paragraph 4 an example is provided for the computation of the Direct Energy Consumption Effectiveness ( DECE ) KPI. In order to obtain this result, we followed a structured approach, described in the following workflow. Two workflow segments can be identified, vertically depicted in Figure 2 below: the analysis of basic elements and of KPIs. Both the segments include phases where the elements/KPIs are first classified, then specified with the introduction of subscript indices. The term original indicates that the existing definition provided by the ISO22400 is used; the termextended indicates that the applicability scope of the element/KPI has been enlarged according to our results. Elements Analysis
Classification model
Subscript indices model
KPIs Analysis
Original elements classification
Original KPIs formulas specification
Original elements specification
Classification model Original KPI classification
Subscript indices model
Classification model Elements extension
Subscript indices model
Extended elements specification
Original KPIs specification
Classification model KPIs extension
Subscript indices model
Extended KPIs specification
Fig. 2 The workflow for improving the ISO 22400 More precisely, the workflow steps are the followings: 1) Identifying a first classification of the elements according to their original definitions in ISO 22400. 2) Specifying the adequate subscript indices of the elements according to their original definitions in ISO 22400. 3) Defining the KPIs formula with the subscript indices according with the original definitions of the elements in ISO 22400. 4) Identifying a first classification of the KPIs according to their original definitions in ISO 22400, by considering the original elements specification in step 2. 5) Specifying the adequate subscript indices of the KPIs according to their original definitions in ISO 22400, by considering the original elements specification in step 2 6) Proposing the eventual extension of the KPIs in those classes where they are missing as to the original
definitions in ISO 22400. 7) Proposing the eventual extension of the classification of the elements in those classes which are missing as to the original definitions in ISO 22400. 8) Specifying the adequate subscript indices of the elements according to their extended definitions. 9) Specifying the adequate subscript indices of the extended KPIs. In this step, KPIs formulas have been eventually reviewed. 3.1 Definitions and concepts in ISO 22400 The ISO 22400 provides explicit, implicit or obtainable evidences for classifying elements and KPIs. The standard presents each KPI in a table where its name, ID, descriptions, scope, formula, unit of measure, range, trend, timing, audience, production methodology, and notes are provided. Indeed, in the ISO22400 – Part 2, two “time models” are introduced. A time model is defined as a partition of the reference time. The first model is related to a work unit (Fig.1): a work unit is “any element of the equipment hierarchy under a work center”. Precisely, “work units are the lowest form of elements in an equipment hierarchy that are typically scheduled by Level 3 functions” (IEC62264-1, 2013). A work unit may process several production orders. Consequently, the introduced elements in this time model are related to a single work unit and one or more work orders. The second time model is related to a production order, which is “a fixed quantity of a single product and requires one or more work orders”, and the introduced time intervals are related to a specific production order sequence. The production order sequence (also referred as production order position) “defines the successive manufacturing steps within a production order ”. By considering all the order sequences, the order path is obtained. Each production order may follow a different sequence path. Different manufacturing steps can b e executed by the same unit in different moments. Hence, the introduced time intervals are related to a production order and its manufacturing steps. It is not clear how all of these concepts in ISO 22400 are linked to elements and KPIs, thus ambiguities often arise when reading the standard and trying to perform a precis computation of KPIs in real contexts. This is the reason why we proposed the classification model. 3.2 Classification model and indices Based on the analysis of elements and KPIs information, a classification model is here proposed to represent the applicability of an element or a KPI to a certain context. To allow a more formal definition and the computation, subscript indices have been here introduced: the generic Work Unit (WU) is referred with subscript i the generic Production Order (PO) is referred with subscript j - the k-th Work Order (WO) part of the j-th PO is referred with subscript k . The elements and KPIs are classified into classes that are not mutually exclusive.
-
16th IFAC Symposium - INCOM 2018, Bergamo, Italy. June 11-13, 2018 Table 1 - Classes of KPI and elements Class
Subindices
Definition
the single work order k of a production order j on a single work unit i the single production order j on a single work unit. It means considering all the work orders WU-PO related to the same production order that are executed by the same work unit all the production orders on a single work unit. It means considering all the work orders WU-POs related to different production orders that are executed by the work unit the single production order through all the WUs-PO work units included in the production order sequence. all the possible combinations of production WUs-POs orders on all the work units WU-WO
i,j,k
i,j
3.3 Elements analysis workflow segment i
j out of scope
Table 2 - Classification Model Scheme ELEMENT or KPI
) U W ( t i U n U W k r o W
e l g n i S
i
Work Order (WO) or Production order (PO) WO k PO j Single Multiple Single Multiple Part of a Part of single different WU-PO WU-POs PO POs WU-WO (or WU-POs WU-WOs) WU-PO (or WU(or WOs) )
e l (not p i t admis l u M sible)
(out of scope)
WUs-PO
(out of scope)
The subset of work units that are part of the execution process (production order sequence, POS) of the production order j is indicated as Ij. Then, if the unit i is included in the execution path of the production order j, then i ∈ Ij The subset of production orders that are executed by the work unit i is indicated as Ji. If a production order j is processed by the work unit i, j ∈ Ji. The subset of work orders that are part of the production order j is indicated as Kj. If the work order k belongs to the production order j, then k ∈ Kj. The subset of work orders that are part of the execution process of the production order j and are executed by the same work unit i is indicated as Kij. If the work order k belongs to the production order j and is executed on the work unit i, then k ∈ Kij. Note that:
-
-
The WUs-WO case does not exist because a specific WO is executed on no more than one specific WU. - The WUs-POs case brings back to the WUs-WOs case, relates to the analysis of a complex production systems which, as stated, is out of the scope of this paper. For this reason, the WUs-POs case (basically indicating a unique class for the entire production system), is out of scope of the analysis.
-
The WU-PO case brings back to the WU-WO case or the WU-WOs, because considering the information related to a single PO on a single WU would imply considering the information of one or more WO of the given PO on the same WU (A PO may require a WU to perform multiple WOs). Analogously, the WU-POs case brings back to the WUWOs case, because considering the information related to a multiple POs on a single WU would imply considering the information of those WOs included in the specific POs, on the same WU.
By the analysis of the ISO 22400, each element can be classified according to the introduced classes, and the same element can appear in more than one of those. The original classification can be identified by merging the existing information provides by the ISO22400. Explicit, implicit and obtained information have been used. Next, the subscript indices can be introduced to specify the classes an element is part of, and thus, distinguish the same element in different classes. In this way, the initial context of an element according with ISO 22400 - is clearly and immediately underlined. After that, the classes with missing elements are analysed. Based on its specific characteristics or on the KPI needs, an element can either be extended or left as it initially is. Indeed, the extension of an element means that the element can be introduced also in the classes where it wasn’t initially defined in ISO 22400. The elements extension in those classes where they were not originally defined were analysed and introduced. Sub-indices must be introduced also in the Moreover, the formula to compute each element on the basis of the value of the same element in the lower classes can be also identified. 3.4 KPIs analysis workflow segment Once the elements are defined, the KPIs can be considered. As each KPI is computed based on a combination of elements, each KPI’s formula can be written by considering the specific elements it includes. In some cases, the KPIs description provided by the ISO 22400 explicitly specifies the class of the elements that it relates to. If it does not, the initial classification of the elements included in the formula is considered. Once the KPI formulas have been rewritten including the elements subscript indices, those formulas must be analysed. If a KPI formula is based on homogenous elements – all part of the same class – the KPI is classified in the same class. Otherwise, additional considerations are needed. Indeed, if the formula is not based on homogenous elements, the KPI needs to be extended in other classes. The classes with missing KPIs must be investigated because those resulted to be necessary in order to characterize the KPI or because its extension can be reasonably useful to performance management. In both the cases, the formula may need some refining - possible thanks to the use of the sub-indices introduced for the elements. Note that even if the elements were introduced in order to suit a particular class, no all the KPIs must be necessarily extended in the same way. Sub-indices are introduced also in KPI names to indicates their classes.
16th IFAC Symposium - INCOM 2018, Bergamo, Italy. June 11-13, 2018
4
AN APPLICATION EXAMPLE
Because the specification of all the elements and KPIs in the ISO22400 standard would require several pages, only one example of the application of the method to a specific KPI (Direct Energy Consumption Effectiveness - DECE ) is presented. This KPI has been recently added to the standard in the ISO 22400-2:2014/Amd1:2017 “KPIs for energy management” amendment. DECE is computed by the use of the planned direct energy consumption ( PDEI ), the produced quantity ( PQ), and the actual direct energy consumption ( ADEC ). The ISO 22400 defines DECE “the relation of the planned direct energy consumption to the actual direct energy consumption. Using this KPI, the produced quantity of an order during the measurement period is considered. The ratio gives information how effective is the planning of the energy consumption for manufacturing the quantity produced PQ. This ratio is used in applications where energy is used for production.” Therefore, the KPI generically relates to order, but doesn’t specify if it relates to work order or production order, neither to single or multiple units. Indeed, ISO 22400 merely presents the following generic expression:
=
∗ ∗ 100
In order to understand the KPI applicability, the steps proposed in the framework were performed. First of all, the original definitions of the elements must be analysed and, consequently, the related classes are identified. In this case, the elements to be considered are:
-
-
The Produced Quantity (PQ), defined as “the quantity that a work unit has produced in relation to a production order” The Actual Direct Energy Consumption (ADEC), defined as “the measured direct energy consumption per work unit and during actual unit busy time (AUBT). In turn, the actual unit busy time is defined as “the actual time that a work unit is used for the execution of a production order”. The Planned Direct Energy Consumption (PDEI), defined as “the planned energy consumption in average for producing one product item”.
Thus PQ is initially classified as WU-PO; ADEC is initially defined as WU-WO; PDEI is initially defined as a WU-WO or WU-POs (step 1). Consequently, the subscript indices can be introduced (step 2). DECE formula can be written by using the elements original definitions in two different ways (step 3) 1)
=
∗
∗ 100
2)
=
∗
∗ 100
The elements used to compute the KPI are not homogeneous according to their original definitions provided by the ISO 22400. Hence, it is not possible to directly specify, and thus classify, the KPI formula (step 4), by simply introducing the sub-indices based on the original definitions of the elements without modifications. By considering also its original
definitions, DECE is related to an order in general, thus it can be classified to a work order or to a production order (step 5), and thus the indices introduced in the KPI name (step 6). Moreover, the DECE computation can be also extended in the WUs-PO and WU-POs class (step 6). Hence, it’s at least necessary to extend PQ in WU-WO class and ADEC and PDEI in WU-PO class to determine the KPI in its original classes, and to WUs-PO and WU-POs classes to in its extended classes (step 7). The final elements classification and specification is shown in Table 4 Table 3 - Elements used by DECE
WU-WO
s e s s a l C
WU-PO WU-POs WUs-PO
PQ (necessary) (initial) (possible) (possible)
ADEC (original) (necessary) (possible) (possible)
PDEI (original) (necessary) (possible) (original)
By simply inserting the elements related to the correspondent class, it’s possible to compute DECE in all the classes. Further considerations about the formula can also be performed, and the DECE can be computed in all the classes by the only use of the elements in the WU-WO class, as it’s shown in Table 4 (step 8). Table 4 – DECE KPI formula DECE WU-WO
(original) s e s s a l C
WU-PO
(original) WU-POs
(extended) WUs-PO
(extended)
= = = =
∗
∗ 100
∑ ( ∗ )
∑
∗ 100
∑ ∑ ( ∗ )
∑ ∑
∑ ∑ ( ∗ )
∑ ∑
∗100
∗ 100
Now DECE KPI can be more accurately computed according to the scope one wants to refer to: - Direct Energy Consumption Effectiveness of a single Work Unit i in a single Work Order k of the Production Order j. Then use the Produced Quantity (PQ), as well as the Actual Direct Energy Consumption (ADEC) as well as the Planned Direct Energy Consumption (PDEC) that are related to the execution of th e specific Work Order k . - Direct Energy Consumption Effectiveness of a single Work Unit i in executing the entire Production Order j. Then sum the Produced Quantity (PQ), as well as the Actual Direct Energy Consumption (ADEC) as well as the Planned Direct Energy Consumption (PDEC) of all the work orders belonging to the Production Order j performed by the Work Unit i. - Direct Energy Consumption Effectiveness of a single Work Unit i in executing the all the production orders. Then sum the Produced Quantity (PQ), as well as the Actual Direct Energy Consumption (ADEC) as well as the
16th IFAC Symposium - INCOM 2018, Bergamo, Italy. June 11-13, 2018
-
Planned Direct Energy Consumption (PDEC) of all the work orders of the Work Unit i in any production order. Direct Energy Consumption Effectiveness of a single Production Order j. Then sum the Produced Quantity (PQ), as well as the Actual Direct Energy Consumption (ADEC) as well as the Planned Direct Energy Consumption (PDEC) of all the work orders executed by all the work units involved in processing the production order j. 5
DISCUSSION
The aim of the ISO 22400 standard is to define the main key performance indicators in manufacturing operation management. Even though the standard can positively support and facilitate companies in performance management, it shows a main flaw. In fact, the aim of the standards is to be general enough to guarantee the KPI applicability. Thus, the ISO 22400 current edition appears to be partially imprecise, vague, incomplete and somehow inconsistent. Hence, a framework has been proposed to overcome its weaknesses and to improve its applicability, and an application example was shown. The framework starts from analysing the relevant measurements using in the KPI formulas (so called “elements”) and ends with the defining and improving the KPI formulas. At the same time, the framework makes the definitions more accurate by introducing subscript indices related to three basic application scopes: work orders (WO), work units (WU) and production orders (PO). However, the presented classification model which defines four classes: WU-WO, WU-PO, WU-WOs, WUs-PO - does not perfectly suit all the elements cited in the ISO 22400 and therefore not all the KPIs. The element and KPI information and description provided by the standard is not specific enough to understand their scope and further considerations should be done to specify them. On top of this, the applicability of the introduced formulas should be verified in discrete, continuous and batch production processes, and this goes beyond the scope of this paper. Moreover, the framework still does not consider multiple-work-units and multiple-production-orders level. But by combining multiple orders on multiple work units, it may be possible to compute KPIs for the work centres. and, by combining the work centres KPIs, the work area KPIs can be computed. In this way, the scopes of the MOM context will be covered. Hence, the proposed model represents the starting point to build a hierarchical structure to pursue the goals to which the original standard aspired. 6
CONCLUSION
This paper considers the international standard ISO 22400 Automation systems and integration — Key Performance Indicators (KPIs) for manufacturing operations, recently published by the ISO to address the performance management in manufacturing industry. By analysing the standard, it is found that some further development is possible. The proposed framework aims at making ISO 22400 more understandable and, moreover, directly applicable by providing more details in the KPI definitions. This research presents the base on which the ISO 22400 can be extended and hence, it also contributes to the industrial acceptance of the standard. The research, and the proposed framework, support manufacturing
companies in the area of performance management. 7
REFERENCES
Bauer, M., Lucke, M., Johnsson, C., Harjunkoski, I., & Schlake, J. C. (2016). KPIs as the interface between scheduling and control. IFAC-PapersOnLine 49-7 (2016) 687 – 692. Braz, F. R., Scavarda, L. F., & Martins, R. A. (2011). Reviewing and Improving Performance Measurement Systems: An Action Research. International Journal of Production Economics, 133 (2), 751 – 760. Endrass, F. (2013). Performance measurement using shop floor data - Integrating information to enhance performance of manufacturing operations management. Master’s thesis, KTH Industrial Engineering and Management, Stockholm, Sweden. Fukuda, Y., & Patzke, R. (2010). Standardization of Key Performance Indecator for Manufacturing Execution System. SICE Annual Conference. Taipei, Taiwan. Hwang, G., Lee, J., & Chang, J. P.-W. (2016). Developing performance measurement system for Internet of Things and smart factory environment. International Journal of Production Research. IEC62264-1. (2013). Enterprise-control system integration Part 1: Models and terminology. ISO22400-1. (2014). Automation systems and integration Key performance indicators (KPIs) for manufacturing operations management - Part 1: Overview, concepts and terminology. ISO22400-2. (2014). Automation systems and integration Key performance indicators (KPIs) for manufacturing operations management - Part 2: Definitions and descriptions. ISO22400-2/Amd1. (2017). Key performance indicators for energy management. Kang, N., Zhao, C., Li, J., & Horst, J. A. (2016). A Hierarchical structure of key performance indicators for operation management and continuous improvement in production system. International Journal of Production Research, 54(21), 6333-6350. Lindberg, C.-F., Tan, S., Yan, J., & Starfelt, F. (2015). Key performance indicators improve industrial performance. The 7th International Conference on Applied Energy – ICAE2015. Lohman, C. F. (2004). Designing a perfromance measurement system design: a case study. European Journal of Operational Research, 267-286. Neely, A. G. (2005). Performance meaurement system design: a literature review and research agenda. International Journal of Operations and Production Management , 1228-1263. Neely, A., Mills, J., Platts, K., Gregory, M., & Richards, H. (1996). Performance Measurement System design: shoul process based approaches be adopted? International Journal of Production Economics, 46-47, 423-431. Parmenter, D. (2007). Key Performance Indicators Developing, Implementing, and Using Winning KPIs. Weber, A. T. (2005). Key Performance Indicators (KPI), Measuring and Managing the Maintenance Function. Burlington: Ivara Corporation.
