Borodin, Dmitriy

De Bruyn, Wim

Van Vreckem, Bert

Zeltzer, Luiza

Ghent University Academic Bibliography

Key Performance Indicators: linking with ISA-95 and moving toward KPI-driven factory Wim DE BRUYN, Dmitriy BORODIN1, Luiza GABRIEL, Bert VAN VRECKEM University College Ghent, University of Ghent, Ghent, Belgium  This paper provides a summarized overview of key performance indicators beginning with the definition, use in the production process, linking with ISA-95 factory modeling standard, discussion on the overall KPI and statements for the building of KPI-driven factories.  Introduction According to [4], term KPI is defined as follows: “Key Performance Indicators, also known as KPIs or Key Success Indicators (KSI), help an organization define and measure progress toward organizational goals. “ In other words, KPIs are quantifiable measurements reflecting the critical success factors (CSF) of an organization. Different types of organizations have different success factors, that is why every organization may define their own KPIs. Here are some examples: one of the main KPIs for a business may be the percentage of the income that comes from the customers. A school may focus its KPIs on the ratio of entered/graduated students and students’ graduation rates. An oil refinery may consider the amount of crude oil losses during its production.  The main points of KPIs, according to [2, 4], is that KPIs must reflect the organization's goals, they must be key to its success, and they must be measurable.  The examples of possible KPIs are shown on Figure 1. A range of techniques has been developed for defining KPIs, for more details see [2]. Just to mention the main steps: “using the PMI™ technique, once the functional analysis is completed, CSFs must be identified to ensure the overall stakeholder needs, or goal will be attained. Using a “functional breakdown structure” (FBS) separates the need from the actual solution used to fulfill this need.” [2]  Linking KPIs and ISA-95 Using the ISA-95 standard, operations and financial managers are able to achieve alignment between strategic expectations, capital expenditure spending and expected operations KPI measurement. ISA-95 information exchanges aggregate key metrics from operations and production into enterprise planning and supply chain algorithms and data models. Leveraging KPIs derived from ISA-95 Part 3 manufacturing operations exchanges, the resulting operational metrics measure and then align true operational and financial benefits sought tactically by the organization. Relevant production measures for KPI construction are defined                                                  1 Correspondence: Dmitry Borodin, Faculty of Business Information and ICT, Schoonmeersstraat 52, 9000 Ghent, Belgium. Tel:+32486335470. email: dmitriy.borodin@hogent.be from manufacturing processes. The “high-performance organization” aims at achieving operational excellence by minimizing low-level process variability through the Part 3 manufacturing operations analytics. Manufacturing analytics aggregate the large number and quantity of low-level, real-time I/O measures.  Several organizations like Supply Chain Council and it SCOR Model, the Project Management Institute (PMI™), authors like Kaplan and Norton (Balance  Scoreboard) and business/manufacturing intelligence software companies are all involved in defining and using enterprise-level KPIs. This ISA-95 best practice document intends to facilitate the use of ISA-95 standard when applying and constructing these enterprise-level KPIs and operations metrics.  As ISA-95 is a standard for building object models for enterprises, the object-model interrelations are very important. Fig. 2 shows an example of such relations.   KPI-driven Factory and Overall KPI In this short paragraph we want to open a discussion about the KPI-driven factory and its possible mathematical interpretation. Regarding the factories, it is still in progress with implementing KPIs in the production process and company management. And, moreover, there is a problem with monitoring these KPIs in the real-time mode. This is connected with achieving the desirable values of certain KPIs, which brings us to the idea of KPI-driven factory. From the mathematical point of view, KPI-driven factory means using KPIs as objective functions. As a KPI is a complex value dependant on many processes and parameters, we can speak of it in terms of function, which makes it possible to formulate optimization problems, taking function parameters as variables and applying relevant constraints to them. The theory of multi objective optimization leads us to the idea of Overall KPI, a value that takes into account all other KPIs.  One of the most simple formulations of the Overall KPI may be: FOverallKPI = C1*FK1 + C2*FK2 + … + Cn*FKn, where FKi is a function of i-th KPI and Ci is a weighting factor. Ci can be KPI priorities, for an example see fig. 3.  KPI problems Defining and monitoring of KPIs can be expensive or difficult for organizations. Indicators such as staff morale may be impossible to quantify. Another serious issue in practice is that once a measure is created, it becomes difficult to adjust to changing needs as historical comparisons will be lost. Conversely, measures are often of dubious relevance, because history does exist. Furthermore, since businesses with similar backgrounds are often used as a benchmark for such measures, measures based only on in-house practices make it difficult for an organization to compare with these outside benchmarks. Measures are also used as a rough guide rather than a precise benchmark. Also, as mentioned in [1], together with timelines and Gantt charts the production process or project overview should contain respective KPIs.  To summarize, KPIs should be standardized characteristics, with possible absolute values and measuring methodology. Tools for incorporating them in the ERP, project management systems, scheduling software should be developed.    Figure 1. Examples of KPIs [2].  Figure 2. Object Model Inter-Relations [2].   Figure 3. KPI-priorities [2].     References  (1) Bart´ak, R.; Little, J.; Manzano, O.; and Sheahan, C. (2008). From enterprise models to scheduling models: bridging the gap // Journal of Intelligent Manufacturing.13 pp. (2) Yves C. Dufort (2006) ISA-95-Based Operations and KPI Metrics Assessment and Analysis WHITE PAPER 24 A Mesa International, ISA and Invensys Wonderware co-branded white paper. Available online at  http://global.wonderware.com/EN/PDF%20Library/MESA%20Whitepaper_ISA-95-Based%20Operations%20and%20KPI%20Metrics%20Assessment%20and%20Analysis.pdf Free access, last accessed on October 18, 2010 (3) David Parmenter (2007), Key Performance Indicators. John Wiley & Sons, ISBN 0-470-09588-1. (4) F. John Reh. Key Performance Indicators (KPI). How an organization defines and measures progress toward its goals. http://management.about.com/cs/generalmanagement/a/keyperfindic.htm Free access, last accessed on October 18, 2010 (5) F. John Reh. Key Performance Indicators (KPI). Key Performance Indicators Must be Key To Organizational Success  http://management.about.com/cs/generalmanagement/a/keyperfindic_2.htm Free access, last accessed on October 18, 2010 (6) Carol Taylor Fitz-Gibbon (1990), "Performance indicators", BERA Dialogues (2), ISBN 9781853590924.  

Key performance indicators: linking with ISA-95 and moving toward KPI-driven factory

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Key performance indicators: linking with ISA-95 and moving toward KPI-driven factory

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Ghent University Academic Bibliography