A bottom-up approach for productivity measurement and improvement

Purpose - The steady incline in oil prices combined with the recent credit crisis and downturns in financial markets has driven organizations to re-evaluate their manufacturing processes and bottom line. The purpose of this paper is to suggest a bottom-up approach that may be used by firms in planning, managing and forecasting productivity improvements. Design/methodology/approach - A multiple-case study approach was used: two comprehensive cases and seven short cases were used to illustrate the model. Findings - The lack of understanding of the relationship between productivity, profitability and performance has led to the application of piece-meal solutions for problems in productivity. Bottom-up approach in improving productivity will provide better results than top-down approach. Originality/value - This paper describes the bottom-up approach which has been successfully used for managing productivity improvement initiatives.

[1]  Arash Shahin,et al.  Designing an integrative model of leagile production and analyzing its influence on the quality of auto parts based on Six Sigma approach with a case study in a manufacturing company , 2011 .

[2]  Mario G. Beruvides,et al.  A Study on the Quality–Productivity Relationship and its Verification in Manufacturing Industries , 2007 .

[3]  Sameer Kumar,et al.  Consumer purchase process improvements in e‐tailing operations: A case study , 2010 .

[4]  Saad H.S. Al-Jibouri,et al.  Modelling construction project productivity using systems dynamics approach , 2009 .

[5]  Morten T. Hansen,et al.  Different knowledge, different benefits: toward a productivity perspective on knowledge sharing in organizations , 2007 .

[6]  Li-Hsuan Huang,et al.  Does labour quality matter on productivity growth? The case of the Taiwanese manufacturing industry , 2008 .

[7]  Toni L. Doolen,et al.  Kaizen events and organizational performance: a field study , 2008 .

[8]  Kathleen M. Eisenhardt,et al.  Theory Building From Cases: Opportunities And Challenges , 2007 .

[9]  Syed H. Masood,et al.  A management information system for team productivity improvement , 2005 .

[10]  Mattias Elg,et al.  The practice of the Balanced Scorecard in health care services , 2011 .

[11]  F. Turco,et al.  Modelling plant capacity and productivity: conceptual framework in a single-machine case , 2005 .

[12]  L. Simar,et al.  Stochastic FDH/DEA estimators for frontier analysis , 2008 .

[13]  Rodger Edwards,et al.  Performance measurement based on a total quality approach , 2011 .

[14]  Kevin J. Dooley,et al.  Technological knowledge maturity, innovation and productivity , 2002 .

[15]  Mile Terziovski,et al.  Quality management practices and their relationship with customer satisfaction and productivity improvement , 2006 .

[16]  Mohan P. Rao A performance measurement system using a profit-linked multi-factor measurement model , 2006, Ind. Manag. Data Syst..

[17]  Vipul Jain,et al.  An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis , 2012 .

[18]  K. Eisenhardt Building theories from case study research , 1989, STUDI ORGANIZZATIVI.

[19]  Thomas Lindsay Jackson,et al.  Hoshin Kanri for the Lean Enterprise: Developing Competitive Capabilities and Managing Profit , 2006 .

[20]  Yuan-Duen Lee,et al.  Applying integrated DEA/AHP to evaluate the economic performance of local governments in China , 2011, Eur. J. Oper. Res..

[21]  R. Grafton,et al.  Contribution of productivity and firm size to value-added: Evidence from Vietnam , 2009 .

[22]  Sameer Kumar,et al.  Operational impact of employee wellness programs: a business case study , 2009 .

[23]  Marco Garetti,et al.  A structured approach to process improvement in manufacturing systems , 2010 .

[24]  Vinh Sum Chau,et al.  Balanced scorecard and hoshin kanri: dynamic capabilities for managing strategic fit , 2007 .

[25]  Syed Tarmizi Syed Shazali,et al.  Determinants of manufacturing productivity: pilot study on labor-intensive industries , 2011 .

[26]  Charles B. Brown,et al.  Transformation From Batch to Lean Manufacturing: The Performance Issues , 2006 .

[27]  A. Dawson,et al.  A case study of impact measurement in a third sector umbrella organisation , 2010 .

[28]  Carmine Ornaghi,et al.  Spillovers in product and process innovation: Evidence from manufacturing firms , 2006 .

[29]  K. Weick The Generative Properties Of Richness , 2007 .

[30]  Benny Tjahjono,et al.  Supporting shop floor workers with a multimedia task-oriented information system , 2009, Comput. Ind..

[31]  Junwook Chi,et al.  Railroad productivity analysis: case of the American Class I railroads , 2011 .

[32]  Sérgio P. Santos,et al.  Formative evaluation of electricity distribution utilities using data envelopment analysis , 2011, J. Oper. Res. Soc..

[33]  Stefan Tangen,et al.  Demystifying productivity and performance , 2005 .

[34]  Jiju Antony,et al.  Reducing employees' turnover in transactional services: a Lean Six Sigma case study , 2010 .

[35]  Kalinga Jagoda,et al.  Canadian oil sands: How innovation and advanced technologies can support sustainable development , 2010 .

[36]  R. Cooper,et al.  Maximizing Productivity in Product Innovation , 2008 .

[37]  P. Broedner,et al.  Productivity Effects of Outsourcing - New evidence on the strategic importance of vertical integration decisions , 2009 .

[38]  Braden R. Kattman,et al.  Visual workplace practices positively impact business processes , 2012 .

[39]  William B. Abernathy A behavior‐based employee performance system , 2003 .

[40]  M. Punniyamoorthy,et al.  Identification of benchmarking service units through productivity and quality dimensions , 2010 .

[41]  Paul Rouse,et al.  Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models , 2012 .

[42]  Andrew Lee‐Mortimer A lean route to manufacturing survival , 2006 .

[43]  Thomas Grünberg,et al.  Performance improvement: Towards a method for finding and prioritising potential performance improvement areas in manufacturing operations , 2004 .

[44]  Y. Kondo,et al.  Hoshin kanri ‐ a participative way of quality management in Japan , 1998 .

[45]  M. T. Hides,et al.  Factors affecting successful implementation of total productive maintenance , 1999 .

[46]  J. S. Khamba,et al.  Total productive maintenance: literature review and directions , 2008 .

[47]  Endre Bjørndal,et al.  Productivity change and innovation in Norwegian electricity distribution companies , 2012, J. Oper. Res. Soc..

[48]  Léopold Simar,et al.  Statistical inference for DEA estimators of directional distances , 2012, Eur. J. Oper. Res..