استفاده از شاخص تولید ناب در توضیح گذار به رقابت جهانی: بخش قطعات خودرو در آفریقای جنوبی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|12142||2004||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Technovation, Volume 24, Issue 3, March 2004, Pages 199–206
Globalisation means enhanced competition. This is a problem both for firms in rich countries, and for those in developing countries. In particular producers in high-income countries are increasingly being threatened by imports from low wage economies. Lean production techniques are therefore an urgent prerequisite for producers everywhere. But how can the degree of progress in the adoption of lean production be measured? And what factors determine the rate of adoption of lean production? This paper addresses both methodological issues (the construction of a lean production index) and the analysis of factors determining its adoption through an investigation of the South African auto components sector.
The reduction in trade barriers associated with globalisation has forced many formerly protected economies to open up to global competition and to confront the challenge of achieving global competitiveness. This has implications for a wide range of firm-level practices, including the effectiveness of manufacturing performance. Achieving this competitiveness not only effects emerging economies, but also high-income economy producers who facing growing competition from competitors in low-wage economies. The South African auto sector is a case in point. Assembly began in the 1920s, but until the mid-1990s, the industry was almost entirely inwardly-focused. A large range of cars were produced in small volumes. In 1987, seven assembly plants produced 22 platforms and this increased to 34 by 1993. Given a small and stagnant market, scales of production were low, even by the standards of other developing countries. Total car sales in 1997 were only 268 000, of which 54 000 were imported (compared to only 20 000 exports). In 1997, only four platforms exceeded volumes of 20 000 p.a. and none reached 30 000 (Black, 1998). In the mid-1990s, the average platform run was only 11 500, which contrasts with 30 000 in Brazil and 50 000 in Australia (Black, 1995). Thus, the prospects at the time of tariff liberalisation were bleak. Few believed that this heavily protected assembly sector, and its supply chain, would survive the icy winds of global competition. And yet, to widespread surprise, the South African auto industry has flourished. As can be seen from Fig. 1, the industry’s export performance has been extremely impressive. In domestic currency, exports quintupled between 1996 and 2001, although the growth in dollar value was much lower (as the domestic currency declined in value). However, even in dollar terms, exports quadrupled between 1994 and 2001, almost breaking the $2bn barrier. In some subsectors, South African exporters now account for significant shares of European and US imports—for example, between 1990 and 1999, the share of EU imports coming from South Africa rose from 2.9 to 72.7% for catalytic converters, and from 10.4 to 15% for leather car seats; its share of US imports of catalytic converters rose from nothing to 22.7% in the same period. VW Golfs, Mercedes Benz C Series and the BMW 3 series are being exported in increasing numbers (from 19 569 in 1997 to 68 031 in 2001).Figure options How did this transformation occur? And how would we know which firm-level practices delivered world class performance? These questions are not, of course, uniquely relevant to an analysis of the South African auto industry; they apply across the spectrum of countries and sectors. In the discussion which follows, we aim to provide a method for assessing the extent to which firms have achieved global best practices through introducing new manufacturing practices. Our intent here is to move beyond firm-level case-studies, and to develop a methodology which allows for a comprehensive quantitative assessment of performance and practice. The quantitative measure which is introduced—the Lean Production Index—combines both objectively measurable data-sets (for example, inventory-levels) and qualitative assessments made by key industry informants (Kojima, 2001). We are aware of the subjectivity which this involves, and of the difficulty of weighting different elements in a comprehensive index, and consequently have a sense of humility about the imperfect nature of this exercise. Yet we would argue that without trying to measure and aggregate a wide range of relevant factors, we have little scope for improving our capacity to generalise from firm-level observations. Therefore, to illustrate the potential power of this form of analysis, in Section 3 we use these Lean Production Index scores to test a number of hypotheses which help to explain the dramatic improvement in the competitive performance of the South African auto industry. The research is based on in-depth interviews and follow-up questionnaires with a stratified sample of 50 auto component firms who were members of the National Association of Automotive Component and Allied Manufacturers (NAACAM). (NAACAM producers accounted for more than 90% of industry output, and our sample, which was slightly biased in favour of large firms, comprises more than one-third of its membership). The survey was conducted in 1998, a period when the South African components sector was beginning the process of competitive restructuring.
نتیجه گیری انگلیسی
Our analysis of the competitive restructuring of the South African automobile components sector has both specific and general implications. Before beginning with the discussion of the specific case of the South Africa experience and the auto components sector, it is important to begin with caveats about the statistical methods used. 4.1. Problems with statistical analysis First, as with all statistical analysis, there is the problem of imputing causality from statistical association. In some cases, this is relatively unambiguous. For example, it is entirely plausible that it is the foreign ownership of subsidiaries which forces them to recruit more educated workers—we know from other contexts that this is often the case (Humphrey et al., 1998). In other cases the conclusions are more ambiguous—for example, it is entirely possible that global component firms buy those South African subsidiaries which are most competitive, so that the causality goes not from ownership to good LPI score, but from good LPI scores to ownership. In yet other cases, the causality may in fact go in the opposite direction to that which is often postulated—for example, instead of export orientation leading to competitive upgrading (a widely-held view amongst development economists), it may be that the only firms who are able to export are those which have already introduced LP practices. A second caveat on statistics concerns the use of aggregate indexes, particularly those which combine parametric and non-parametric measures. We have argued that capturing real-world events requires a combination of measured and ‘judgmental’ data. The first allows for parametric testing (regressions, correlations, etc.). However, the second requires different categories of tests and for this reason we have been eclectic in our use of statistical methods (making more use of analysis of variance tests). Allied to this is the problem of weighting. This is much more troubling since weightings are inevitably subjective. For example, our comprehensive LPI gives equal weights to the three categories of LP—flexibility/logistics, quality and CI. Moreover, within each of these three subindexes, we have used different numbers of variables, some of which are weighted on a five-point scale, and others on a three-point scale. We have tried to be as transparent as possible in this, partly in order to throw the spotlight on methodological issues. However, we are aware of the arbitrary nature of these weightings. At the same time we are aware of the inherent arbitrariness of virtually all measurement scales. To be philosophical for the moment, our conventional system of numbering assumes that the value of the difference between 73 and 74 is the same as that between 1 and 2. Yet as soon as we assume non-lineal relationships (for example, in economics, the diminishing marginal utility of consumption means that a dollar of income means much more to a poor person than a rich person), then we need to question the ‘neutrality’ of even our everyday numbering system. In other words, the problems of weighting are intrinsic to all measurement systems; what we have tried to do is to make the value judgements as transparent as possible. 4.2. Specific conclusions The analysis which we have undertaken addresses the competitive restructuring of the South African auto components sector. We are able to show not just the degree of competitive performance of a sample of firms (and changes over time, if we revisit firms), but also to identify the factors which have facilitated this restructuring. Key amongst these are: • The role played by foreign ownership; • The role played by Toyota with its more demanding purchasing policies and the use of its supplier development team; • The role played by firm-specific training programmes. Less clear is the role played by demanding customers in foreign countries in forcing LPI upgrading as a consequence of exporting. In part, this ambiguity arises, as pointed out above, because of directions-of-causality problems in the analysis. However, in addition, we also found a number of firms which exported despite having low LPI scores. This arises for a combination of factors—they are in sectors (such as aluminium) where South Africa has a comparative advantage; they are distress exports (due to domestic recession); and, they are driven by the auto components policy regime which gives duty drawbacks for foreign exchange earnings (Barnes and Kaplinsky, 2000b). Another ambiguous case is that of the role played by foreign licences. These may be forced by buyers as a condition of supply, rather than being a factor which in itself forces the introduction of LP practices. In a third category of conclusions with regard to the factors leading to competitive restructuring in South Africa, we find some factors which have little or no influence, notably firm-size or the particular practices of domestic buyers (other than Toyota). It is also worth noting the insignificance of the degree of education of the workforce, although this is probably very specific to South African operating conditions. The specificity of this analysis arises not only with respect to the South African context, but also to the subsector in question, auto components. Some of our measures—notably the use of VDA6 as a quality criterion—are clearly industry-specific. Others, such as the emphasis on set-up time reduction are relevant to small batch production discrete products industries rather than to mass production or process sectors. 4.3. General conclusions Yet, despite these specific conclusions, we believe that our analysis is of much wider relevance. All firms and all industries need to benchmark their performance, both over time and comparatively. Whilst not a unique form of measurement, our LPI does provide such a tool. It is, of course, highly specific to the South African environment of the late 1990s. For example, we were forced to use quality-of-output rather than quality-at-source indicators in the quality index; the CI Index has no measure of benefits, and the Flexibility Index gives a muted recognition to internal kanbans, cellular layouts and so on. These ‘modifications from the ideal’ reflected the local circumstances, but in other more sophisticated environments, we would want to use measures which more accurately reflected the principles of lean production. This means that although the principle of our LPI can be used in different countries, care needs to be taken in comparing scores across these boundaries. Nevertheless, we believe that the methodology which we have utilised advances the discussion on lean production. Suitably adjusted, similar procedures can be used across environments and across sectors. At a minimum, it allows for progress to be assessed over time (and across boundaries where the indexes used are the same). But we also have a more ambitious aim. An aggregate index is insightful because it allows us to test a number of hypotheses concerning the factors which promote competitive restructuring. We live in a world of assertion—for example, that exporting leads to upgrading (our conclusions are ambivalent on this), or that the existence of ‘supplier development’ programmes amongst the major auto firms has an effect on supplier performance (whereas we found that in reality this only applied to Toyota). But it is only through rigorous testing that we can sort assertion from reality. We hope that our methodology goes some way along this path. Finally, it is important to bear in mind that however sophisticated the LPI may become, and however rigorous the research enquiry may be, lean production is only one factor determining competitive success. Other factors such as product innovation (Wheelwright and Clark, 1992; Bessant and Bruce, 2001) and access to final markets (Kaplinsky and Morris, 2001) may in the final analysis determine the competitive outcome. Lean production is only a necessary outcome for success; it is not sufficient.