Methodology for uncertainty characterization of performance measures

Purpose The purpose of this paper is to present a methodology to represent the uncertainty generated in performance measures (PMs) during the operational step or “use step” of the performance measurement process (PMP). The different steps of the methodology are described and exemplified through an application example. Design/methodology/approach An index that reflects the level of uncertainty originated by the factors and the strength of their inter-relationships is developed through the use of graph theory. A graph is developed considering all sources or factors of uncertainty that may be present in this process. Based on the graph, the methodology includes the use of a matrix and the determination of the associated permanent function which is used for determining the uncertainty index. Findings During the development of the methodology, it was found that the use of a scale that includes zero for assigning values of the elements of the matrix is not appropriate when using graph theory and permanent function calculation, since in this case the permanent function is insensible to changes in some matrix elements. Originality/value This paper identifies all the sources that can affect the quality of performance measurement values during the operational step of the PMP and proposes a method to characterize the strength of this uncertainty. Beyond alerting decision makers to the level of uncertainty associated with a PM, it also allows defining appropriate actions to improve PMs’ quality.

[1]  Stephan M. Wagner,et al.  Assessing the vulnerability of supply chains using graph theory , 2010 .

[2]  Elaine Aspinwall,et al.  Development of a performance measurement framework for SMEs , 2010 .

[3]  Luiz Felipe Scavarda,et al.  Reviewing and improving performance measurement systems: An action research , 2011 .

[4]  Carlo Batini,et al.  Methodologies for data quality assessment and improvement , 2009, CSUR.

[5]  J. Schalkwyk,et al.  Total quality management and the performance measurement barrier , 1998 .

[6]  Diane M. Strong,et al.  AIMQ: a methodology for information quality assessment , 2002, Inf. Manag..

[7]  Andy Neely,et al.  Performance measurement system design , 1995 .

[8]  M. Bourne,et al.  Factors that play a role in “managing through measures” , 2003 .

[9]  Andy Neely,et al.  Designing, implementing and updating performance measurement systems , 2000 .

[10]  Dev Nikhil,et al.  Graph Theoretic approach (GTA) - A Multi-Attribute Decision Making (MADM) Technique , 2013 .

[11]  P Kueng,et al.  Building a Process Performance Measurement System: Some Early Experiences , 1999 .

[12]  Maryam Darvish,et al.  Application of the graph theory and matrix methods to contractor ranking , 2009 .

[13]  R. Venkata Rao,et al.  Selection, identification and comparison of industrial robots using digraph and matrix methods , 2006 .

[14]  Rhian Silvestro,et al.  Challenging the paradigm of the process enterprise : a case-study analysis of BPR implementation , 2002 .

[15]  Malcolm Macpherson,et al.  Performance measurement in not‐for‐profit and public‐sector organisations , 2001 .

[16]  R. Venkata Rao,et al.  A decision-making framework model for evaluating flexible manufacturing systems using digraph and matrix methods , 2006 .

[17]  Shlomo Globerson,et al.  Issues in developing a performance criteria system for an organization , 1985 .

[18]  R. Kaplan,et al.  The Balanced Scorecard: Translating Strategy into Action , 1996 .

[19]  Robert Winter,et al.  Aligning Process Automation and Business Intelligence to Support Corporate Performance Management , 2004, AMCIS.

[20]  R. Venkata Rao,et al.  Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods , 2013 .

[21]  A. Amirteimoori,et al.  Performance measurement of decision-making units under uncertainty conditions: An approach based on double frontier analysis , 2015 .

[22]  O. P. Gandhi,et al.  Quantification of human error in maintenance using graph theory and matrix approach , 2011, Qual. Reliab. Eng. Int..

[23]  R. Rao A material selection model using graph theory and matrix approach , 2006 .

[24]  Ron Basu,et al.  New criteria of performance management , 2001 .

[25]  Sandeep Grover,et al.  A graph theoretic approach to evaluate the intensity of barriers in the implementation of total productive maintenance (TPM) , 2014 .

[26]  Thomas J. Crowe,et al.  An integrated dynamic performance measurement system for improving manufacturing competitiveness , 1997 .

[27]  A. Gunasekaran,et al.  A framework for supply chain performance measurement , 2004 .

[28]  Ravi Shankar,et al.  GTA‐based framework for evaluating the feasibility of transition to FMS , 2010 .