THE LEAN INDEX: OPERATIONAL "LEAN" METRICS FOR THE WOOD PRODUCTS INDUSTRY

No standard definition for lean production exists today, especially specific to the wood products industries. From a management point of view, even the more straightforward management issues surrounding the concept of "lean" are complex. This exploratory research seeks to develop a methodology for quantitative and objective assessment of the leanness of any wood products operation. Factor analysis is a statistical approach that describes the patterns of relationships among quantifiable predictor variables, with the goal of identifying variables that cannot be directly measured, such as the leanness of a company. Using this technique, a factor model was identified and a factor score, or "Lean Index," was developed. For the nine wood products companies included in this study, the average Lean Index is demonstrated to be 5.07, ranging from a low of 2.33 to a high of 12.00. Based on the quantified standards of lean production developed in this study, (1) primary wood products operations are inherently leaner than secondary wood products operations; (2) process throughput variables explain approximately twice the total variance of all consumed resources, compared to process support variables; and (3) energy consumption is shown to be the single most significant contributor to the leanness of any wood products company.

[1]  B. Everitt,et al.  Cluster Analysis (2nd ed). , 1982 .

[2]  Deborah J. Nightingale,et al.  Development of a Lean Enterprise Transformation Maturity Model , 2002, Inf. Knowl. Syst. Manag..

[3]  G. W. Milligan,et al.  A study of standardization of variables in cluster analysis , 1988 .

[4]  Daniel T. Jones,et al.  STRATEGIC APPLICATION OF LEAN THINKING , 1998 .

[5]  R. Bush,et al.  A Multivariate Model and Analysis of Competitive Strategy in the U.S. Hardwood Lumber Industry , 1991 .

[6]  H. Charles Romesburg,et al.  Cluster analysis for researchers , 1984 .

[7]  Paul F. Wilson,et al.  Performance-Based Assessments: External, Internal, and Self-Assessment Tools for Total Quality Management , 1995 .

[8]  V. Clark,et al.  Computer-aided multivariate analysis , 1991 .

[9]  Robert R. Sokal,et al.  Distance as a Measure of Taxonomic Similarity , 1961 .

[10]  M F Janowitz Cluster Analysis Algorithms for Image Segmentation. , 1981 .

[11]  Martin Kenney,et al.  Beyond Mass Production. , 1994 .

[12]  Norman Bodek,et al.  Toyota Production System: Beyond Large-Scale Production , 1988 .

[13]  Kenneth G. Brown,et al.  Designing Management Training and Development for Competitive Advantage: Lessons from the Best , 1998 .

[14]  P. Sopp Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[15]  Deborah Nightingale,et al.  Lean Enterprise Value , 2002 .

[16]  Joseph Moses Juran Juran on leadership for quality : an executive handbook , 1989 .

[17]  R. Nelson Why do firms differ, and how does it matter? , 1991 .

[18]  James P. Womack,et al.  Lean Thinking: Banish Waste and Create Wealth in Your Corporation , 1996 .

[19]  Judd Harrison Michael,et al.  Employee strategic alignment at a wood manufacturer: An exploratory analysis using lean manufacturing , 2003 .

[20]  N. Lackey,et al.  Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research , 2003 .

[21]  John Allen,et al.  Lean Manufacturing: A Plant Floor Guide , 2001 .

[22]  W. T. Williams,et al.  An Objective Method of Weighting in Similarity Analysis , 1964, Nature.

[23]  Anil K. Jain,et al.  Clustering Methodologies in Exploratory Data Analysis , 1980, Adv. Comput..

[24]  K. Clark,et al.  Dynamic Manufacturing: Creating the Learning Organization , 1988 .

[25]  Implementing Lean Thinking - An Interview with William C. Kessler , 1999, Inf. Knowl. Syst. Manag..

[26]  Bengt Klefsjö,et al.  The machine that changed the world , 2008 .

[27]  A. J. Barr,et al.  SAS user's guide , 1979 .