The Contribution of Selected Manufacturing Practices to Achieve Product Customization in Supply Chains. A Cross-industrial Comparison

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1 The Contribution of Selected Manufacturing Practices to Achieve Product Customization in Supply Chains. A Cross-industrial Comparison Artur Swierczek Department of Business Logistics. University of Economics in Katowice, ul. Bogucicka 14, , Katowice, Poland. artuross@ue.katowice.pl, Tel POMS 22 nd Annual Conference Reno, Nevada, U.S.A. April 29 to May 2, 2011 ABSTRACT Many supply chains operating in different industries have currently entered the era of mass customization which requires them to manufacture and deliver products catering to individual and refined customers preferences. The aim of the paper is to identify and estimate the role of selected global manufacturing practices in product customization in supply chains. In order to achieve this goal, the relationships among the obtained levels of product customization and manufacturing practices in supply chains will be investigated. In the result of the analysis multiple regression models will be developed for specific levels of product customization in supply chains operating in different sectors. It will enable to make a cross-industrial comparison of the contribution to variance in product individualization. Keywords: material decoupling point, manufacturing, product customization 1

2 INTRODUCTION The supply chain, which in the last few years has been a subject of intense research both in theoretical and practical frameworks, is currently one of the most dynamically developing concepts (Kisperska-Moron and Swierczek, 2009). For the purpose of this paper supply chain is defined as a set of three or more companies directly linked by one or more of the upstream and downstream flows of products, services, finances and information from a source to a customer (Mentzer, 2001). In order to survive and possibly increase market share, supply chains have to generate sales income from products which should meet specific customers requirements. Therefore, from the supply chain standpoint, it is necessary to identify different generic groupings of the products which fulfill specific customers needs. One of these categorizations are highly customized products which are individually configured and appropriately designed. This enables customers to make a choice between components and assemble them to form many different and desired product offerings (Anderson, 1997). The essence of customization is to provide exactly what each individual customer requires at the right time (Pine and Gilmore, 1999). In practice, a wide range of companies in supply chains is involved in a production process. It is necessary to match specific characteristics of production process with the organization of material flow in supply chains. The paper is structured into several sections. Following the introduction, the issues concerning the organization of material flow in supply chains and major manufacturing practices have been discussed. In the next part of the paper, methodological framework and research model have been developed. Then, empirical findings derived from statistical analyses have been presented and discussed in relation to the research problems. Finally, conclusions of the research are drawn and the implications for further empirical studies are indicated. THE LEVEL OF PRODUCT CUSTOMIZATION IN A SUPPLY CHAIN The level of products customization may decrease or increase gradually in supply chains being determined by an appropriate location of customization point, more frequently referred to as material decoupling point. In the opinion of Hoekstra and Romme the decoupling point is the point that indicates how deeply the customer order penetrates into the goods flow (Hoekstra and Romme, 1992). The material decoupling point is a buffer between upstream and downstream partners in the supply chain. This enables them to be protected from 2

3 fluctuating consumer buying behaviors and therefore to establish smoother upstream dynamics, while downstream consumer demand is still met via a product pull from the buffer stock (Mason-Jones and Towill, 1999). The levels of product customization can be indicated by the location of material decoupling points which are reflected in the most popular classification of manufacturing types, namely: make-to-stock (MTS), assembly-to-order (ATO), make-to-order (MTO) and engineer-to-order (ETO). In make-to-stock manufacturing products are standardized, but not necessarily allocated to specific locations; the demand is anticipated to be stable or readily forecasted at an aggregate level. In assemble-to-order system products can be customized within a range of possibilities, usually based upon a standard platform. Make-to-order is characterized by raw materials and components which are common, but can be configured into a wide variety of products. In the last manufacturing system engineer-to-order products are specially designed from engineering specifications. While the products might use some standard components, at least some of the components or arrangements of components are specifically designed by the customer or by the customer working with the producer (Naylor et al., 1999; Goldsby and Garcia-Dastugue, 2003; Bozarth and Chapman, 1999). Supplier Manufacturer Assembler Retailer Consumer Pull ETO/ Pure customization Push Pull MTO/ Tailored customization Push Push Pull ATO/ Customized standardization Pull MTS/ Pure standardization Flow of goods Decoupling points / customization points Fig.1. Potential locations of material decoupling points indicating levels of product customization in supply chains Adapted from: S. Hoekstra, J. Romme: Integrated logistics structures: Developing Customer Oriented Goods Flow, McGraw-Hill 1999, London. 3

4 On the left side of material decoupling point the activities are forecast driven, initiated by push strategy, in accordance with plans and forecasts. On the right side of material decoupling point the activities are order-driven, which means they are originated by pull strategy, initiated by customers market demand. Adapting the view of Minzberg and Lampel, those four stages can determine levels of product customization in a supply chain: make-to-stock MTS is typical for pure standardization, assembly-to-order ATO refers to customized standardization, make-to-order MTO is linked to tailored customization and engineer-to-order ETO corresponds to pure customization. These customization points develop a continuum indicating different degrees of product customization Fig.1. As the decoupling point is moved upstream in the supply chain, the degree of customization is expected to increase ceteris paribus, because customers would have the possibility to affect the production process at earlier stages (Blecker and Abdelkafi, 2006). Moving the customization point downstream in a supply chain results in manufacturing and delivering more standardized products and limiting the impact of customers on the extent of products functionality, its appearance and other characteristics. Therefore, the location of material decoupling point is often perceived as an important measure of the levels of product customization in supply chains. SELECTED GLOBAL MANUFACTURING PRACTICES Manufacturing companies differ in the way they meet demand for products. In practice, there are many different types of manufacturing processes. In their product-process matrix, Hayes and Wheelwright enumerate job shop (jumbled flow), batch, assembly line and continuous flow (Hayes and Wheelwright, 1979). This evolution begins with a fluid process and proceeds toward increasing standardization, mechanization and automation. Those types of manufacturing possess a number of distinctive characteristics concerning both the external conditions of operating environment and the internal attributes of production processes. Anderson and other authors argue that in the market conditions in which the company operates, there are several criteria that win orders against the competition, namely cost (price), product differentiation, amount produced, distribution, product and volume flexibility, delivery, innovation, quality, environmental and social responsibility (Strong, 1997; Anderson et al., 1989). The identification of companies competitive priorities should be the first step in the formulation of a manufacturing strategy (Swink and Way, 1995). This view is also 4

5 supported by Hayes and Wheelwright who claim that the composition of competitive dimensions is a starting point in the formulation of the manufacturing strategy defined as a sequence of decisions that, over time, enables a business unit to achieve a desired manufacturing structure, infrastructure, and set of specific capabilities (Hayes and Wheelwright, 1984). Among internal characteristics one can identify the following fundamental properties of a production process: the degree of specialization and differences in the variety of products being produced, performance productivity of manufacturing practices, grouping of equipment (Williams et al., 1994; Engstrom et al., 1996; Melcher et al., 2002), manufacturing process flexibility and a major resource used in a manufacturing process (Hill, 1992). Specialization refers to the breakdown of the total task into sub-tasks (Hill, 1992). From the perspective of production process, the four levels of specialization may be identified, namely: pure (producing a new design), tailored (modification to the existing design), standardized (selection from a set of designed options), none-standardized (take an existing design as it is) (Amaro et al., 1999). It is worth noting that specialization is tightly related to a product variety. The companies which produce complex products, especially products adjusted to the individual needs of customers, often modify their design. Many of these changes are formally initiated by the customer as new requirements, or by the company as modified specifications or manufacturing changes (Nadia et al., 2006). The product structure should meet certain given criteria by combining a number of parts, features or functions required by the ultimate customers. From the technological point of view there are two generic types of product architecture, namely integrated architecture and modular architecture. In the first one, each function is connected to a corresponding part, whilst in the latter a part may have more than one function (Helo, 2006). The way of combining specialized activities on both physical and organizational bases reflects another characteristics of grouping of the equipment. The criteria of grouping are a similarity of production equipment (grouping by machine type), and alternatively, units rationalized around a common task (Hill, 1992) that is, grouping by a product. Grouping the machines by product allows to decentralize production into manufacturing cells, which are the essence of flow shop islets in a job shop environment. By grouping the machines into clusters and the various parts into part families, the processing of each part family is allocated to a single machine cluster (Cheng, 1992; Kandiller, 1994). The adoption of a cellular layout is associated with reductions in performance obstacles of material delivery, the availability of resources and the quality of work information (Brown and 5

6 Mitchell, 1991). Implementation of cellular manufacturing also allows to reduce manufacturing through put and setup time (Ghosh, 1990) because each product is manufactured wholly or largely in one area. The other characteristics is manufacturing flexibility which can be grouped into four types, namely new product flexibility (related to the system s ability to introduce different products or modify existing ones), mix flexibility (related to the system s ability to manufacture a broad range of products within a given period of time), volume flexibility (related to the system s ability to change its aggregated level of output), and delivery flexibility (related to the ability of the system to change delivery dates). The level of manufacturing process flexibility is partly determined by a complexity of BOM (Bill of Materials). In the opinion of Chen and Wang the generic BOM provides an efficient way to describe a large number of variants with limited amount of data (Chen and Wang, 2007). BOM complexity reflects the number of levels in the bill of material and the typical number of items on each level (Wanstrom and Jonsson, 2006). It influences the number of parent and neighbor items (Ho and Li, 1997). It may be assumed that the greater BOM complexity, the higher probability of manufacturing process flexibility. More recently, the resource-based perspective has been applied to the issues of manufacturing process (Ketokivi and Schroeder, 2004; Schroeder et al. 2002; Bates and Flynn, 1995). In general, resources are assets which a firm possesses, controls or to which it has access. There are many diverse criteria of resource classification. One of the most commonly cited crieria distinguishes between infrastructural and structural resources. Hill defines infrastructural resources as the set of structures, controls, procedures, systems and communication combined with attitudes, experience and skills of people involved with the manufacturing system and structural resources as the technology, equipments and facilities of the manufacturing system (Hill, 1989). In the opinion of Slack resources can be classified into three generic groups, namely technological resources the facilities and technology, or the hardware side of the manufacturing system, human resources people in the manufacturing system, infrastructural resources the systems, relationships and information couplings which bind the operation together (Slack, 1989). Apart from the requirements of customers, the structure of the final product determines the changes that might be carried out. The significance of the mismatch between market requirements and manufacturing process design and its impact on the performance of manufacturing companies was indicated by several authors (Berry and Hill, 1992; Schroeder et al., 1995). 6

7 The performance dimension often linked to the manufacturing efficiency is productivity (Chew, 1988), defined as the ratio of actual output to input over a period of time. Inputs might include transforming and transformed resources, such as staff and equipment. Outputs are goods and services (Slack et al., 2001). In its simplest form, labor productivity could be defined as the hours of work divided by the units of work accomplished (Thomas, 1994). Another productivity dimension which has been studied for several decades is the productivity of manufacturing facilities. It is a metric used for measuring and analyzing the productivity of individual production equipment in a factory. Equipment productivity metric assesses internal efficiency and it is a measure of the value added in a manufacturing process by an equipment (Johnson and Lesshammer, 1999). Many empirical evidences report that there are manufacturing practice differences between the countries and indutries. For instance, a cross-industrial perspective has been documented by Whybark and Boone who studied the relationship between productivity and manufacturing practices in various industries (Whybark and Boone, 1995). In other studies Zhong and Hong focused on changes in manufacturing practices in Chinese machine tools industry (Zhong and Hong, 1994). Kovacevic, Lopez and Whybark examined manufacturing in Chilean companies from textile industry and machine tool sector (Kovacevic et al., 1994), whilst Flores and Macias compared manufacturing practices in the latter two industries in Mexico (Flores and Macias, 1994). The enumerated papers provide that there are manufacturing practice differences between the sectors. Therefore, it seems to be interesting to make a cross industrial comparison of the contribution of selected manufacturing practices in obtaining a specific level of product customization in supply chains. METHODOLOGY Research model, sample and data collection On the basis of literature review a research model has been developed Fig.2. As depicted in the model, the independent (explanatory) variables are manufacturing practices classified into several groups, namely: order winning criteria, productivity, flexibility of manufacturing, grouping of equipment, variety of products, resource orientation, manufacturing specialization. The response variables are four potential locations of material decopuling points indicating the levels of product customization in supply chains from pure customization, through tailored customization and customized standardization to pure 7

8 standardization. Based on literature review, it may be assumed that there are some significant differences concerning manufacturing practices between industries. Fig.2. Conceptual model of cross-industrial effects of selected global manufacturing practices on product customization in supply chains The main research instrument used for this study was a questionnaire developed by the Global Manufacturing Research Group consisting of several sections examining manufacturing practices. There is no single meta-theory for guiding the development of GMRG survey. Instead, many aspects of general manufacturing practices were a subject of investigation. Data collected within the fourth release of the survey were gathered between 2006 and 2008 by researchers from several countries in Europe, North America, Asia, and Africa. The survey was a random sample of firms in a given geographical area (Vestag and Whybark, 2005). For the purpose of the research presented in this paper, a number of variables have been selected. Originally 27 (23 independent and 4 dependent variables) were a subject of initial analysis. The opinion items were gathered using 5-point Likert scale, and on the other hand, the descriptive measures (e.g. weight percentage, number of days, share in % etc.) were filled in directly. The preliminary analysis has shown that distributions of the selected variables were skewed. In order to preserve the observations with extreme points and not to weigh them too heavily, the data were split into quintiles (Vastag and Whybark, 2005). In this connection, 8

9 the values of items were converted by assigning 1 to the lowest 20% (bottom two deciles) of the firms and 5 to the highest 20% (top two deciles). In order to perform cross-sectoral comparisons, six industries were chosen for further analysis. The sample was compiled from surveys of manufacturing firms and originally consisted of 861 manufacturers. As a result of initial data analysis, screening and elimination of observations with missing values, 416 companies remained as the subject of further analysis. The respondents were mainly small and medium-sized companies. This group included manufacturers from Fiji (26 %), USA (25%), Poland (16%), China/Shaghai (14%), Hungary (12%), Korea (6%) and Australia (2%). 30% 20% 10% 0% Australia China Fiji Hungary Korea Poland USA Fig.3. The sample breakdown by country of origin The majority of the surveyed companies operate in electronic and other electrical equipment industry (40%), followed by industrial and commercial machinery equipment (19%), fabricated metal products (14%), food industry (13%), chemicals (8%), stone, clay, glass and concrete products (5%). The sample breakdown was graphically presented in Figures 3 and 4. 40% 30% 20% 10% 0% Food Chemicals Metal products Industrial machinery Electronics Stone, clay, glass Fig.4. The sample breakdown by industry type 9

10 Research problems and methods The aim of the study is to explore the contribution of selected manufacturing practices to obtain specific levels of product customization in supply chains operating in different industries worldwide. In order to realize the empirical aim of the study, two main research problems have been presented: RP1: Identification of different levels of product customization in supply chains, RP2: Investigation of the role of global manufacturing practices in obtaining a desired level of product customization in supply chains operating in different idustries worldwide. In order to solve the research problems, a two-step statistical analysis was employed. The first step was the reduction of 23 independent variables through factor analysis. Those variables reflected multidimensional manufacturing practices. In order to perform the factor analysis, a Principal Component Analysis (PCA) with Varimax Rotation was applied. The analysis was conducted on standardized variables. The inspection of anti-image correlation matrix has led to the elimination of 4 variables whose measure of individual sampling adequacy is below a nominal cut off point of 0.5. Additionaly, in the result of factor analysis 1 variable was excluded, as it indicated factor loading below a nominal cut-off point of Table 1. The Structure of the Constructs Obtained through the Factor Analysis Variables Factors Share of labor cost in manufacturing cost Share of material cost in manufacturing cost Cost (price) Quality (conformance to specifications) Delivery timeliness Product variety Environment/safety # items on typical Bill of Materials Flexibility to change output volume Flexibility to change product mix Labor productivity Equipment productivity Manufacturing cost index % of one of a kind products % of large batch products Number of manufactured product lines Equipment grouped by machine type Equipment grouped by products

11 Finally, the factor analysis which was carried out on 18 items, revealed the following structure of constructs (Table 1): Factor 1: Order winning criteria (cost, quality, delivery, product variety, environmental safety), Factor 2: Productivity (manufacturing cost index, labor productivity, equipment productivity), Factor 3: Flexibility (flexibility to change output volume, flexibility to change product mix), Factor 4: Grouping of equipment (by machine type, by assembly line), Factor 5: Level of manufacturing specialization (% of one of a kind, % of large batch), Factor 6: Resource orientation (labor, material), Factor 7: Products variety (number of manufactured product lines or product families). The number of factors was determined according to the analysis of the percentage of variance explained and the Kaiser criterion (Aczel, 1993). KMO coefficient score indicating the suitability of the sample for factor analysis in a space of 18 variables is which is a very good result (Bryma and Cramer, 1999). Bartlett s test of sphericity demonstrated sufficiently high value for the extracted factors at p <= (Approx. chi-square , df = 210). In the second stage of the analysis multiple regression models were developed. It enabled to make a cross-sectoral comparison of the contribution to variance in product customization. Only variables with observed p-values of less than 0.05 were kept in the model. Multiple regression models were developed for each of the four response variables indicating specific levels of product customization in supply chains. Those variables are: pure customization, tailored customization, customized standardization and pure standardization. Corresponding response variables were defined as a percentage of manufacaturing orders falling into four categories: engineer-to-order, make-to-order, assembly-to-order and make-tostock respectively. First, descriptive statistics was used to make a cross-industrial comparisons of the levels of product customization in supply chains. Afterwards, the regression models were developed. Data were analyzed seperately for 6 sectors. RESULTS OF ANALYSIS Identification of the levels of product customization in supply chains Table 2 shows comparisons of the levels of product customization in supply chains operating in various industries worldwide. 11

12 Table 2. Cross-industrial comparison of the levels of product customization in supply chains (median scores) Levels of product customization Industry Pure Tailored Customized Pure customization customization standardization standardization Food % 37.9% 18.4% 21.9% Chemicals # 19.1% 34.1% 20.3% 29.7% Metal products # 15.5% 52.2% 12.0% 14.2% Industrial machinery # 22.5% 43.9% 16.5% 18.5% Electronics # 19.9% 46.5% 19.3% 15.6% Stone, clay, glass # 26.1% 46.7% 22.5% 16.5% The obtained results suggest that higher levels of customization reflected in pure and tailored customizations dominate in all examined industries. Although the distribution of medians indicating specific levels of product customization in industries is rather even, some differences may be observed. The highest levels of pure and customized standardization are indicated by companies from stone, clay, glass sector. The leading role in tailored customization is played by supply chains from metal industry, whereas pure stanardization is the domain of chemical sector. It is worth noting that two sectors, namely industrial machinery and electronics are very similar regarding median scores for specific levels of customization. It may suggest that operating environment as well as manufacturing practices are very similar for product customization in these two sectors. Contribution of selected global manufacturing practices to obtain a product customization in supply chains Table 3 demonstrates the results of regression analysis for five industries, namely food and kindred products, chemicals, fabricated metal products, industrial/commercial machinery and computers, electronic/electrical equipment. The analysis of the companies manufacturing stone, clay, glass showed that there are not any significant explanatory variables against the 4 response variables. The regression analysis for product customization showed that each analyzed industry has at least one model with significant independent variables and adjusted coefficients of determination (R 2 adjusted) ranging from.096 to.667. The strongest models, as measured by R 2 adjusted, are in the chemical, fabricated metal industries and human consumption sector. 12

13 There are two models predicting pure customization with an adj. R 2 of.667 and.096 indicated by supply chains from chemical industry and electronics, three models explaining tailored customization with adj. R 2 ranging from.105 to.389 reported by organizations from human consumption, chemical and industrial machinery sectors. The customized standardization is predicted in the following four models with adj. R 2 ranging from.210 to.629 reported by the organizations from chemical industry, fabricated metal products, industrial/commercial machinery sector and electronics. Finally, pure standardization is explained by one model with adj. R 2 of.219 indicated by supply chains from fabricated metal industry. It is also worth noting that the regression models developed for fabricated metal sector and industrial/commercial machinery industry have only one significant explanatory variable. There is also one model with an independent variable in the electronic equipment sector. Table 3. Comparison of regression models for product customization in considered industries Industry Product customization Manufacturing practices Food and kindred Products Chemicals Fabricated metal products Industrial/Commercial Machinery/Computer Electronic/Electrical Equipment/Components Tailored customization Pure customization Tailored customization Customized standardization Customized standardization Flexibility Grouping of equipment Order winning criteria Flexibility Grouping of equipment Grouping of equipment Manufacturing specialization Order winning criteria Flexibility Std. Coef p- value * R square ** Grouping of equipment Pure standardization Resource orientation Tailored customization Manufacturing specialization Customized standardization Resource orientation Pure customization Productivity Customized standardization Order winning criteria Flexibility Manufacturing specialization * p-value < 0.05 ** adj. R sq. significant at

14 There are two significant models predicting pure customization demonstrated by the companies from electronic/electrical and chemical industries. The standardized regression coefficient for a response variable in the first model indicates that as productivity increases, pure customization decreases. This result may suggest that firstly, the factor of productivity is important for pure customization in the analyzed industry and secondly, there is a negative association between productivity and pure customization. The latter conclusion seems to be logical, as the concept of pure customization in its nature is not cost-oriented. It is rather linked with customer-focused strategies more concentrated on delivering a superior value for customers which leads to the increase of costs. The second model predicting pure customization demonstrates three significant response variables, namely flexibility, order winning criteria and grouping of equipment by machine type. The results may suggest that the higher level of flexibility, the greater extent of application of pure customization concept. This confirms previous studies which demonstrate that flexibility is widely used as an instrument for achieving mass customization. Another significant factor of order winning criteria is also positively associated with pure customization which suggests that if the importance of winning order criteria rises, the extent of pure customization increases. This result seems to be consistent with the fundamentals of pure customization, indicating the superior role of customers and the necessity to adopt performed operations to the requirements of ultimate clients. The results showed three models predicting tailored customization reported by the companies from human consumption sector, chemicals, industrial and commercial machinery. Although each model represents different industries, all of them indicate a similar set of significant response variables. In food sector, tailored customization is predicted by grouping of equipment and flexibility. This may suggest that the more equipment in a production process is grouped by machine type, the greater extent of tailored customization in the human consumption industry. It is interesting to observe that the standardized coefficient Beta for flexibility indicates a negative value in the investigated sector. It means that as flexibility increased, tailored customization decreased. The result is not much consistent with literature, as flexibility most often indicates a positive association, with customization being its instrument. However, flexibility does not contribute to achieve tailored customization in the food sector. It should be noted that food is a very sensitive product whose qualitative parameters affect people s health and sometimes even lives. Therefore, it is important not only to keep the standards during manufacturing of food, but also to meet certain criteria of quality while storing and transporting groceries in the distribution process. This may partially 14

15 explain why the companies are not willing to introduce, manufacture and deliver a very broad range of food products on aggregated scale within a given period of time. Grouping of equipment is also a significant factor for tailored customization in the chemical industry. However, in contrast to food sector, machines grouped by assembly line contribute to tailored customization in the chemical industry. Manufacturing specialization is another significant factor for tailored customization in chemical and industrial/commercial machinery sectors. The standardized regression coefficients demonstrate positive strength in both branches between tailored customization and manufacturing specialization, as they represent similar industrial characteristics. The results suggest that the more specialized production, the greater extent of tailored customization. It may confirm that customization requires products to be more individualized, which in consequence entails specialized manufacturing. The concept of standardized customization is predicted by four models indicated by companies from the chemical industry, fabricated metal products, industrial/commercial machinery sector and electronics. It is interesting to observe that two rather distinct sectors, namely chemicals and electronics, demonstrate a similar set of response variables significant for standardized customization. In both industries order winning criteria play a significant role in the application of customized standardization, though its standardized coefficients Beta indicate opposite values. Order winning criteria are negatively associated with customized standardization, which may partially be the result of institutional dimension of supply chains operating in this industry. It may be assumed that the majority of purchasers in these supply chains are other companies and their cooperation is mostly based on long-term contracts. Therefore, the product offerings addressed to the clients of these supply chains do not need to be enriched with soft elements of marketing-mix. Contrary to the chemical industry, companies from electronic sector report a significant contribution of order winning criteria to achieve customized standardization. The results demonstrate that if the importance of order winning criteria increases, the extent of customized standardization rises. There is a huge group of ultimate customers who purchase the final products delivered by the companies from this sector. It may partially explain a positive role of that factor in achieving customized standardization. Another significant factor is flexibility, negatively associated with the level of customized standardization in both industries. As flexibility is not a typical instrument, the decreasing role of that response variable may directly stem from the essence of customized standardization. Also manufacturing specialization is less significant for customized standardization, as the companies from electronic industry indicated a negative association between them. Therefore, the higher level of manufacturing specialization, the lower extent of 15

16 customized standardization. The results seem to be logical as, similarly to flexibility, specialization is also not perceived as a typical instrument for the lower levels of customization. Two other models predicting customized standardization are reported by the companies from fabricated metal and industrial machinery sectors. The first model demonstrates a significant association with a single variable describing a grouping of the equipment by machine type. In the second model customized standardization is predicted by a factor of resource orientation. The results suggest that the higher resource orientation, the larger extent of customized standardization may be achieved. The resource orientation also plays a significant role in a model predicting pure standardization developed by the companies from fabricated metal industry. The conducted analysis enabled to investigate a contribution of selected manufacturing practices in achieving specific levels of product customization in supply chains operating in different industries worldwide. The obtained results provide an adequate basis for solving two research problems. The first research problem concerned the identification of different levels of product customization in supply chains. The study revealed that the levels of product customization in supply chains depend on industrial perspective. There is a prevailing share of pure and tailored customization in a sample, however a closer investigation provides some interesting differences. For instance, the companies from classical industries (e.g. chemical) report a larger share of standardization than the supply chains operating in more modern sectors like electronics. On the other hand, the organizations from the sectors with an assembly production e.g. industrial machinery and electronics indicate the domination of customization. The second research question dealt with the role of manufacturing practices in achieving product customization in the supply chains. The obtained results confirmed that regarding industrial perspective, there are different global manufacturing practices contributing to a desired level of product customization. Although the sets of manufacturing practices for specific product customization in the supply chains are not purely homogenous in terms of industry, some observable tendencies may be identified. For instance, there is a similar group of practices contributing to tailored customization in human consumption sector, chemicals, industrial and commercial machinery. The parallel tendencies are revealed by the supply chains from two rather distinct sectors, namely chemicals and electronics. They both demonstrate a similar set of manufacturing tendencies predicting customized standardization. Apart from similarities, the differences explaining specific levels of product customization 16

17 may also be identified. For instance, there is an utterly different set of practices contributing to customized standardization in the supply chains from fabricated metal industry and industrial/commercial machinery sector. Significant differences may also be observed in manufacturing practices affecting pure customization in the organizations from chemical industry and electronics. FUTURE DIRECTIONS AND FURTHER RESEARCH The empirical study, apart from providing some insights into the contribution of selected global manufacturing practices to the specific levels of product customization in a supply chain, also highlights potential areas of future research. It would be important to conduct a dedicated study based on the presented conceptual model with a more specific research instrument, employing a greater number of manufacturing variables. It would enable to develop more complete and coherent regression models, thus not restricting them only to the group of selected manufacturing practices. An important element is the investigation of the role of other functions performed in a supply chain, for instance marketing, finances, human resources, whose multidimensional aspects, apart from manufacturing, may also significantly contribute to customization. The other issue requiring in-depth studies are external circumstances determining the strength and direction of dependency among manufacturing practices and the levels of product customization in a supply chain. Another element which needs investigation is also conducting the regression modeling for other sectors and comparing them with presented industries. References 1. Aczel, A.D., 1993, Complete Business Statistics, Second Edition, Boston, Massachusetts. 2. Amaro, G., Hendry, L. and Kingsman, B., 1999, Competitive advantage, customization and a new taxonomy for non make-to-stock companies, International Journal of Operations and Production Management, Vol. 19, No.4, pp Anderson, D.M., 1997, Agile Product Development for Mass Customization, Irwin Professional Publishers, Chicago, IL. 4. Anderson, J.C., Cleveland, G. and Schroeder, R., 1989, Operations strategy: a literature review, Journal of Operations Management. Vol. 8 No. 2, pp Bates, K. and Flynn, J., 1995, Innovation history and competitive advantage: a resource-based view analysis of manufacturing technology innovations, Academy of Management Journal, Best Paper Proceedings, pp

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