THE EFFECT OF GLOBAL MANUFACTURING COMPETITIVE FACTORS ON GLOBAL MANUFACTURING EFFICIENCIES Dennis Krumwiede College of Business. Idaho State University, Pocatello, Idaho. 921 S. 8 th Ave. Stop 8020, Pocatello, Idaho, USA, krumdenn@isu.edu, Tel. 2082823506 Teri Peterson College of Business. Idaho State University, Pocatello, Idaho. 921 S. 8 th Ave. Stop 8020, Pocatello, Idaho, USA, peteteri@isu.edu, Tel. 2082824861 Artur Swierczek Department of Business Logistics. University of Economics, ul. Bogucicka 14, 40-007, Katowice, Poland. artuross@ae.katowice.pl, Tel. +48322577302 ABSTRACT Competitive advantage is important to every company that faces competition. The purpose of this paper is to analyze the relationships among identified competitive goals, potential core competencies embracing resources, capabilities, competencies and the obtained level of performance efficiency. Core competencies are characterized and studied as competitive factors, namely input investments, manufacturing typology, manufacturing planning, accuracy of plant records and product complexity. Keywords: competition, manufacturing companies, efficiency INTRODUCTION This paper analyzes the relationships among identified competitive goals, potential core competences, embracing resources, capabilities, competencies and the obtained level of performance efficiency. Five performance measures were used for this research. The core competencies used are characterized as input investments, manufacturing typology, manufacturing planning, accuracy of plant records and product complexity. These competencies are discussed below. Competitive goals There is general consensus regarding the composition of key elements determining the nature of competition. Several authors enumerated the following factors: cost (price), product differentiation, amount produced, distribution, flexibility, delivery, innovation, quality, environmental and social responsibility [2]. Input investments Input investments significantly support the ability of a company to accomplish competitive goals. Direct resources enable the achievement of synergy, providing realistic frameworks for forecasting strategic planning and goal setting [1]. In order to gain and sustain a competitive - 5431 -
advantage, companies have to acquire such resources. These specialized capabilities are exemplified by investments in a number of key areas which in turn increase the companies efficiency. Manufacturing typology Manufacturing companies differ in the way they meet their demand. Some deliver products to their clients from finished goods inventories as their production anticipates customers' orders, others, however manufacture only in response to customers' orders. Time competition being a driving issue requires an emphasis on time that should not be wasted and is supported by fewer and faster activities being performed. On the other hand, customization means performing some activities according to the unique requirements of an individual customer. Competition in terms of time and customization is reflected in the most popular classification of manufacturing types, namely: make-to-stock (MTS), assembly-to-order (ATO), make-toorder (MTO) and engineer-to-order (ETO). Manufacturing planning Manufacturing strategy requires that clear goals are formed and understood by all members of the organization. Some plans must be determined in order to reach the projected goals. Strategic goals enable the formulation of strategic, tactical and operational plans. Tactical and operational plans are closely tied to the management of the production processes while strategic plans have a considerable impact on the companies future. In the opinion of Hodgetts and Kuratko strategic planning can contribute to performance by generating relevant information, by creating a better understanding of the important environment, and by reducing uncertainty [6]. There is empirical evidence indicating a positive relationship between long-term formal planning, frequency of plan s modifications and the obtained performance level [7]. Research indicates that the time horizon of plans for high performers is longer than of plans for low performers whilst frequent plan revisions allow for a timely and thus cost-efficient adaptation of plans and strategies in case of a deviation between current and anticipated data [10]. Accuracy of plant records The accuracy of manufacturing records is the basis for maintaining an effective performance level. Lack of inventory accuracy often results in discrepancies which may ultimately lead to higher than preferred inventory levels and the costs associated with the quantity and value of inventory stored. Conversely, accurate inventory records results in lower inventory investment and are the foundation for forecasting, ordering, tracking, vendor evaluation, and dead stock administration programs [5]. Therefore, accurate inventory records are essential to not only controlling the inventory costs, but also for the analysis of the cost effectiveness. Product data accuracy entails data accuracy of the bill of material (BOM) and accuracy of routing file [13]. According to Chen and Wang the generic BOM provides an efficient way to describe a large number of variants with limited amount of data [4]. Performance efficiency of manufacturing practices Efficient production is a primary concern for manufacturing companies. This performance can be affected by manufacturing technology, labor, capital investment, etc. [3]. Traditionally business performance is considered to be the result of efficiency which means doing all the - 5432 -
activities in the process using the least possible resources, whether these are people, equipment or the inventory. Many researchers have studied and identified variation in manufacturing processes that reduce product quality and increase the overall costs of operation. Subsequently, several indices were presented to assess the efficiency of the manufacturing process. Maull et al. argue that the value of the products/items affects the volume by value of the items being phased-out, and, thus, the potential scrap costs [9]. General manufacturing scrap embraces the following manufacturing process characteristics: materials supply scrap level, manufacturing scrap level and final product scrap level. They are used to assess the performance manufacturing efficiency. The other performance dimension often linked to the manufacturing efficiency is productivity. It is 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 [11]. In its simplest form, labor productivity could be defined as the hours of work divided by the units of work accomplished [12]. Another productivity dimension or metric is the productivity of manufacturing facilities. The equipment productivity metric assesses internal efficiencies and is a measure of the value that is by equipment in a manufacturing process [8]. In this paper labor and equipment productivity are measured as an index comparing the current productivity to that of two years prior to the survey response. RESEARCH METHOD Data from the Global Manufacturing Research Group round 4 survey was used for this research. The Global Manufacturing Research group consists of academic colleagues from around the world. The group has currently administered four rounds of surveys. This latest survey was used for analysis in this research effort. Initial data collection involved over 800 companies from 8 countries. Variables The independent or explanatory variables are input investments, competitive goals, manufacturing typology, inventory accuracy, forecast duration, and bill of material (BOM) effects. Three factors for investment inputs were defined as the scale variables of shop floor control investment, process control investment, and environmental investment. All had Cronbach alphas of.75 or greater. The competitive goals variables consist of the relative weight given by top management to the six variables cost, quality, delivery timeliness, product variety/volume, new product design/innovation, and environment/safety. Manufacturing typology is measured by the percentage of orders that are engineered to order, made to stock, and assembled to order. Inventory accuracy is measured by the percent accuracy of the plant s inventory records, bills of material and routings. Forecast duration is measured by the number of weeks ahead the plant s production plan extends, how many times per year the plant s production plan is revised and how many weeks ahead the plan freezes the production schedule. Bill of materials effects are measured by how many items are on a typical bill of material and how many annual permanent changes are made to this plant s bills of materials. - 5433 -
Five response variables were considered: manufacturing cost index (an approximate index comparing current costs to costs two years ago), labor productivity index, equipment productivity (comparing productivity now to two years ago), % of rejects during processing, and % of rejects during final inspection. The impacts of the explanatory variables on the manufacturing efficiency variables were expected to be differentially important based on the manufacturing sector. Therefore, manufacturing sectors were created from the GMRG codes established as part of the GMRG data collection process. These sectors are clothing, petrochemical, fabrication metal, big equipment, natural products, human consumption, and electronic and measuring. PRELIMINARY DATA ANALYSIS AND RESULTS Data were analyzed separately for each of the sectors using SPSS release 15.0, (2007). Multiple regression using backwards selection was performed separately for each of the five response variables. Only variables with an observed significance level (p-value) of less than 0.10 were kept in the model. Multicollinearity was assessed for each model using variance inflation factors (VIFs). All equations had VIFs less than 5, a commonly accepted level for detecting multicollinearity. As this was an exploratory approach too many models were produced to discuss in a paper of this size. The models were reviewed for strong overarching patterns. No such patterns were detected; therefore this paper will discuss the specific results from a subset of the models built. Each sector had at least one model with significant explanatory variables and adjusted coefficients of determination (R 2 adjusted) ranging from.038 to.850. However, the fabrication metal sector had coefficients of determination ranging only from.038 to.127 with few significant explanatory variables. Clothing and leather (n=35), fabrication metal (n=72), big equipment manufacture (n=81), and the electronics (n=123) sectors had weak models, therefore we will not discuss those sectors further in this paper. The strongest models, as measured by R 2 adjusted, were in the petrochemical, natural products, and human consumption sectors. Details for one model from each of these sectors is presented next. Natural products sector The best model in the natural products sector was the one predicting the labor productivity index with an adjusted R 2 of 0.451. The standardized regression coefficients, or betas, indicate the relative strength of each of the significant variables and demonstrated by (beta, p) at the end of each statement. As The investment into environmental and safety processes (0.22, 0.08) increased the labor productivity also increased. As the importance of the goal of cost to the CEO increased the labor productivity index decreased (-0.58, 0.00). As the number of weeks ahead the production plan extends increased the labor productivity index decreased (-0.25, 0.06). As the accuracy of the BOM increased the labor productivity decreased (-0.35, 0.01). As the number of items on the BOM increased the labor productivity decreased (-0.23, 0.09). The strongest positive effect on natural products is environmental and safety investments. The strongest negative effect on labor productivity is the CEO s desire for competitive cost. - 5434 -
Human consumption sector The best model in the human consumption sector was the one predicting the equipment productivity index with an adjusted R 2 of 0.709. The standardized regression coefficients, or betas, indicate the relative strength of each of the significant variables and demonstrated by (beta, p) at the end of each variable. As the investment into shop floor control increased (0.28, 0.02) the equipment productivity also increased. As the % made-to-stock (0.20, 0.05) and % engineered-to-order (0.21, 0.03) increased the equipment productivity index increased, but as the percent assembled-to-order (-.38, 0.00) increased the equipment productivity index decreased. As the importance of the goals of cost (-0.31, 0.01) and delivery timelines to the CEO (-0.18, 0.09) increased the equipment productivity index decreased. As the number of weeks ahead the production plan extends (-0.59, 0.00)increased the equipment productivity index decreased. As the number of times per year the plan was revised (0.71, 0.00) increased so did the equipment productivity index. As the accuracy of the BOM (-0.33, 0.01) increased the equipment productivity decreased, but as the accuracy of the route information (-0.2, 0.06) increased the equipment productivity index also increased. Finally, as the number of changes to the BOM per year (-0.6, 0.00) increased the equipment productivity decreased. The most positive effect on equipment productivity is # of times per year the plan is revised. That is, as the plan is revised more frequently, it will negatively affect productivity. The most negative affect was planning further into the future with the production plan. Petrochemical sector The best model in the petrochemical sector was the one predicting percent rejects during production with an adjusted R 2 of 0.850. The standardized regression coefficients, or betas, indicate the relative strength of each of the significant variables and demonstrated by (beta, p) at the end of each variable. As the investment into process control (0.31, 0.00) and environment and safety (-0.28, 0.00) increased the percent rejects during production decreased. As the percent assembled-to-order (0.61, 0.00) increased the percent rejects during production index increased. As the importance of the goal of quality to the CEO (0.20, 0.01) increased the percent rejects during production increased. Finally, as the number of changes to the BOM per year increased (0.46, 0.00) the percent rejects during production increased. The most positive affect in this sector on percent rejects during production is percent assembled to order and the most negative affect is process control investment. CONCLUDING REMARKS, FURTHER RESEARCH AND LIMITATIONS Analysis of all sectors combined led to no significant predictive models. Therefore, it is the authors belief that each sector is subject to different relationships and pressures and should be analyzed individually. This greatly reduces the n, yet leads to more significant models. All six classes of variables were significant predictors of the manufacturing efficiency variables in at least one sector with at least one response variable. However, the relationship did not always go in the expected direction. Some sectors such as the human consumption and petrochemical sectors demonstrated the strongest relationships between the efficiency response variables and the suggested explanatory variables, while some sectors such as fabrication metal, despite a larger sample size, consistently showed little or no relationships with the explanatory variables. This demonstrates the need to develop models within each - 5435 -
sector incorporating manufacturing challenges inherent to the individual sectors. This research is still in the exploratory stage and further consideration of country, and therefore culturally specific influences needs to be incorporated as well. This study s potential use to practitioners could rest in the development of a model in their sectors to determine the way their particular sector behaves in terms of core competencies and organization outcomes as measured by their desired response variables. As the presented study is in the initial stages, the obtained results of the analysis suggest further directions of research. It would be important to conduct an in-depth analysis of circumstances determining the strength and direction of dependency among performance measures and the explanatory variables as well as to demonstrate the results of regression modeling for other sectors and to compare them with presented industries. Limitation to this research exists as dividing the data into sectors caused the sample sizes to decrease by a large amount. Thus, these models should be viewed as exploratory models to assist in further theory development. REFERENCES [1] Abell, D.F., Hammond, D., John, S. Strategic Marketing Planning, Prentice Hall, Hamel Hampstead, UK 1979. [2] Adam, E.E., Swamidass, P.M. Assessing operations management from a strategic perspective. Journal of Management, 1989, 15 (2), 181-203. [3] Chen, L.-H., Liaw, S.-Y. Measuring performance via production management: a pattern analysis. International Journal of Productivity and Performance Management, 2006, 55 (1), 79-89. [4] Chen, Z., Wang, L. A generic activity-dictionary based method for product costing in mass customization. Journal of Manufacturing Technology Management. 2007, 18 (6), 678-700. [5] Harrington, T. C., Lambert, D. M., Vance, M. P. Implementing an Effective Inventory Management System. International Journal of Physical Distribution & Logistics, 1990, 20 (9), 17-23. [6] Hodgetts, R.M., Kuratko, D.F. Effective Small Business Management, Dryden, Fort Worth, TX 2001. [7] Jennings, D., Disney, J. J. Designing the strategic planning process: does psychological type matter? Management Decision, 2006, 44 (5), 598-614. [8] Johnson, P., Lesshammer, M. Evaluation and improvement of manufacturing performance measurement systems the role of OEE. International Journal of Operations & Production Management, 1999, 19 (1), 55-77. [9] Kraus, S., Harms, R., Schwarz, E. J. Strategic planning in smaller enterprises new empirical findings. Management Research News, 2006, 29 (6), 334-344. [10] Maull, R., Hughes, D., Bennett, J. Role of the BOMs as a CAD/CAPM interface and the key importance of engineering change control. Computing & Control Engineering Journal, 1992, 3 (2), 63-70. [11] Slack, N., Chambers, S., Johnston, R. Operations Management, 3rd ed., Prentice-Hall, Harlow 2001. [12] Thomas, R.H. Effects of scheduled overtime on labor productivity. Journal of Construction Engineering and Management, 1994, 118 (1), 60-67. [13] Wanstrom, C., Jonsson, P. The impact of engineering changes on materials planning. Journal of Manufacturing Technology Management, 2006, 17 (5), 561-584. - 5436 -