THE DYNAMICS OF BEST MANUFACTURING PRACTICES Bjørge Timenes Laugen1, Nuran Acur2, Harry Boer3 1
University of Stavanger, Department of Business Administration, 4036 Stavanger, Norway 2 University of Strathclyde, DMEM, James Weir Building, Glasgow, G1 1XJ, UK 3 Aalborg University, Center for Industrial Production, Fibigerstræde 16, 9220 Aalborg, Denmark
[email protected] ABSTRACT In order to stay competitive manufacturing companies continuously need to search for and implement best practices to improve their performance. Although the literature might suggest so, best practices are not a static set of improvement actions. On the contrary, as a majority of companies have implemented former best practices, one can expect that these lose their status and are replaced by new best practices. Analyzing data from 677 companies, this paper aims to shed light on the development and dynamics of best practices in manufacturing. Our findings suggest that integration of NPD and manufacturing, and improving manufacturing process maintain their status as best practices, while servitization and supply chain management are new best practices. Globalization and responsibility are proposed as promising practices, and technology is suggested as a qualifying practice. Keywords: Action programs, performance, survey
1.
BACKGROUND
Companies, especially those that are active in highly competitive industries such as car manufacturing and electronics, depend on well-developed continuous innovation capabilities for their survival. Not only do they have to develop new products all the time, they also need to continuously look for cheaper, better, more flexible and faster ways of launching, producing and delivering existing and new products or, in other words, best practices. Early academic contributions to the best practice quest are Hayes and Wheelwright (1984), who introduced the term World Class Manufacturing (WCM), and authors such as Schonberger (1986), Voss et al. (1997) and Flynn et al. (1999), who argued that the implementation of best practices will lead to superior performance. However, what is „best‟ changes over time. Previous best practices, e.g. TQM, ICT and flexible manufacturing systems, are now part of the day-to-day activities in most companies, and, hence, do not distinguish high from low performers (Laugen et al. 2005). Former best practices are replaced by new practices that may develop into best practices. Therefore, it is necessary continuously to look for emerging practices in order to assess whether they are promising or not.
Hence, one purpose of this paper is to replicate the study of Laugen et al. (2005), and investigate the dynamics of manufacturing practices – how do they, i.e. their role in industry, change over time? According to Mills et al. (1995), best practices “… can be considered as bundles of actions …, which tend to work well together”. Since that study, there has been a growing recognition that bundles of practices, rather than single practices, lead to high(er) performance improvement (Cua et al. 2001, Ahmad and Schroeder 2003, Laugen et al. 2005, MacDuffie 1995, Narasimhan et al. 2005, Shah and Ward 2003, Voss 2005). However, only few studies, notably Sun (2002), Cua et al. (2001) and Shah and Ward (2003), have empirically examined the effects of bundles of practices. Thus, the second purpose of this paper is to address if and how different practices reinforce, or work against, each other. 1.1
RESEARCH QUESTIONS AND OPERATIONALIZATION
Davies and Kochhar (2002) argue that best practices are those leading to improvement of performance. Further, they argue that it is necessary to assess the holistic performance effects of practices, because there might be trade-offs among practices and performance(s). Thus, arguing that best practices are those adopted in the best performing companies, this article investigates the adoption of action programs, i.e. attempts made to achieve performance improvement by improving the way day-to-day activities (practices) are conducted. This paper addresses the following questions: • To what extent and how does the adoption of bundles of action programs lead to performance improvement? The answers to these questions will lead to an updated, anno 2009, overview of best, supportive, qualifying and promising practices reported by Laugen et al. (2009). •
Does the nature of manufacturing practices change over time?
In order to address that question, the IMSS V (2009) based findings will be compared to the findings reported by Laugen et al. (2005) and Laugen et al. (2009), which were based on IMSS III (2001) and IMSS IV (2005) data, respectively, so as to replicate and extend Laugen et al.’s (2005) theory on the dynamics of (best) manufacturing practices. 2. 2.1
METHODOLOGY DATA
To analyze the research questions, we will use data from the fifth round of the International Manufacturing Strategy Survey (IMSS V). The data were collected in 2009, using a postal survey sent to production managers from manufacturing companies (ISIC 28-35) in a wide range of countries world-wide. The dataset comprises information from 677 companies from 19 countries world-wide. 2.2
OPERATIONALIZATION OF VARIABLES
The questions are measured on five-point Likert-scales, and ask for the level of adoption of action programs and the level of change in performance, both during the last three years. We investigate how the adoption of bundles of action programs relates to the
improvement of groups of speed/cost, quality and flexibility performance. We control for the direct influence of company size and production process type. 2.3
ANALYSES
We use regression analyses to analyze the statistical relationship between action programs and performance indicators. 3.
RESULTS
The results of the analyses are reported in Table 1. We label the practices best, qualifying and promising practices, based on their observed relationships with performance improvement. 3.1
BEST PRACTICES
According to Davies and Kochhar (2002, p.303), best practices should be evaluated based on their ability to improve overall performance, rather than the improvement of one specific area. Four practices lead to significant improvements in several areas of performance and can, thus, be regarded as current best practices in manufacturing. Improving the manufacturing process shows strong positive relationships with improvement of all performance areas (beta = 0.156 – 0.200, p < 0.01). Improving the management of the supply chain is positively related to performance improvement of 6 of the 7 performance areas, 3 of which are significant. The strongest relationship is found for flexibility performance (beta = 0.155, p < 0.01). Programs aimed at improving the NPD function are positively related to performance improvement, 3 of the 7 relationships are significant. We find the strongest relationship for quality performance (beta = 0.105, p < 0.05). The implementation of servitization programs has positive relationships with all areas of performance improvement (beta = 0.085 – 0.122, p < 0.01-0.05). 3.2
QUALIFYING PRACTICES
We label improvement programs that used to be regarded as best practice but have weak and mixed performance effects today as qualifying practices. We find technology programs to fit into this category, having mixed and insignificant relationships with all performance areas. Despite the lack of, or even negative, performance impact, these programs have an important status in that they enable firms to stay (qualify for) and, moreover, remain competitive in the markets in which they operate. Investing in technology (e.g. CAD/CAM-systems, ERP and process automation) does not make companies perform better, relative to their competitors – all companies invest in state-of-the-art technologies – but are essential in order to stay competitive. Hence, these programs are not best practices but rather qualifying practices, as suggested by Laugen et al. (2005). 3.3
PROMISING PRACTICES
The next category, which we propose to label promising practices, consists of improvement programs that are relatively new and have a mixed or slightly positive relationship with most areas of performance improvement and could develop into new best practices. Due to their relative newness, they are still rare, or perhaps more widely
Responsibility Manufacturing process Supply chain management Technology Globalization NPD Servitization Size Mass
Performance indicators Flexibility SCQ -0,085 0,065 0,156*** 0,196***
Speed/cost -0,019 0,178***
Quality 0,157*** 0,200***
0,103
-0,011
0,155***
0,064
0,055 -0,032 0,077 0,137*** -0,037 0,085**
-0,026 0,013 0,105** 0,161*** -0,088** 0,122***
0,020 0,027 0,052 0,157*** -0,051 0,085**
0,022 -0,012 0,093* 0,160*** -0,065* 0,105***
SCF -0,049 0,173***
QF 0,038 0,193***
SCQF 0,017 0,192***
0,145**
0,077
0,100*
0,043 -0,006 0,068 0,159*** -0,049 0,087**
0,000 0,023 0,086** 0,171*** -0,078** 0,112***
0,023 0,001 0,084 0,168*** -0,066* 0,104***
Table 1. Regression analysis. Sign. levels: ***: p
