ANALYSIS OF THE RELATIONSHIP AMONGST PRODUCTION EFFICIENCY, PRODUCT COMPLEXITY AND TARGET MARKET

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1 19 th International Conference on Production Research ANALYSIS OF THE RELATIONSHIP AMONGST PRODUCTION EFFICIENCY, PRODUCT COMPLEXITY AND TARGET MARKET P. C. Oprime, M. O. A. González, and J. C. Toledo (Federal University of São Carlos, Rod. Washington Luís - Km 235, São Carlos, São Paulo, Brazil) Abstract The objective of this article is to analyze the effect of both target market and product complexity on the production activities in an industrial goods company of Brazil. Nine projects developed and manufactured by the company were monitored and their pertaining data collected, according to the planning of a factorial experiment involving two independent variables (target market and product complexity) and two result variables used two measure production efficiency: percentage of production overtime actually spent as compared to planning, and rework man-hours production. Results showed that products for the American and European markets receive a differentiated treatment due to the market factor. Other factor statistically significant, was that high complexity products for the American and European markets showed cost prevision errors (under estimate) as long as low complexity products for the internal market, showed cost prevision errors (over estimate). Keywords: Quality, Product, Efficiency, Production, Industrial goods. 1 INTRODUCTION The objective of this research was to analyze how production efficiency is affected by both the product complexity and its target market, thru witnessed follow up of product development and manufacture carried out in an industrial goods company. Within this sector as well as in others, production efficiency is strategic for cost reduction, meeting deadlines and other requirements as expected by the client [1]. However, production efficiency is influenced by other company s areas, namely, product development, logistics, and production planning and control, whose activities may be reunited in a unique function, Operations [2]. The company under study belongs to a multi-national and has been operating in all the continents. The Brazilian plant serves the Latin American market. The main function of new product development is performed in the United States and Europe; in Brazil there are a manufacturing plant and an engineering office reponsible for nationalizing and customizing products for the internal market and, when requested by the headquartes, also develops solutions for other markets beyond Latin America, mainly for Europe, Asia and Africa. The main competitive advantages of products manufactured in Brazil and sold to external markets are quality, production costs and timing. As regards products quality, their high reliability and durability have been worldwide known as compared to the competitors. Therefore, product strategy wise, the company holds a good image in the world market, especially in Europe and United States, where the order-winning factor is product quality. The Brazilian branch considers strategically important to maintain an efficiency level resulting in production costs lower than those obtained by the branches located in other countries. Other challenges to the continuity of their competitiveness are to maintain the quality of the after sale services as well as meeting the contracted deadlines competitiveness. 2 VARIABLES WHICH CAN AFFECT PRODUCTION EFFICIENCY Variuous authors present factors of Production efficiency. By instance, [3] [4] have being studying the impact of the supply chain on production costs and product quality. Another research on factors affecting production efficiency was carried out by [5] who have studied the relationship between the product structure and the production materials management. Also, [6], analyzed the organizational structure impact and how it affects competitiveness. The supply chain is characterized as a set of activities which transform materials along the value-adding stages [4]. The supply chain is also constituded by the information flow throughout the chain parts up to the final market, what influences quality, deadlines, costs and inventory levels. The authors identified an existing correlation between product quality and some supply chain-associated factors. The study of [5], compares the amount of components and product structure levels to the sequence patterns and criteria to releaseproduction orders. Results showed the impact of the product structure on the procedures to release production orders, on the inventory levels and factory personnel appointment. Autors [6], have shown the small company has a lighter organizational structure, which allows greater response flexibility to changes provoked by demand and product, when compared to big companies. The studies shown above reveal some factors affecting a company s efficiency and competitiveness. However, other less objective elements exist, also affecting competitiveness, which should be taken into account when elaborating competitive strategies. For example, [7] point at the power of the brand name and product origin region on the client s purchasing decision. [7] investigated how the purchasing decision is affected by the brand name and product origin region. As a rule, when a multinational settles in a given country, not technologically reknown even when the brand is known as innovative, the client expects to pay lower prices as compared to products sourced in technologically traditional countries. The authors found out that traditionally good quality reknown brands affect possitively purchasers perception. Nevertheless, when the origin country or region does not

2 have a technological tradition, the purchasing decision is negatively affected. The product brand reputation of a given company has relationship with the the company s capability to innovate products and processes. This can be observed in [8] who studied the effect of product and processes innovation and improvement on competitiveness. These empiric studies results state that product quality is beneficial for companies ranking related to their diferentiation strategies. The authors show the relationship amongst product innovation as difererentiation strategy, market leadership and focus on client. Products demanding greater degree of innovation are those whose markets are competitive and technologically dinamic. The products and processes innovation issue seems, though, to be relevant when a better competitiveness is sought for. However, according [9], little importance is given to continuous improvement in products development. To those authors, the product development process is not focused on continuous improvement in some companies, despite their impact in other sectors like logistic, production planning and control and the manufacturing process. Quality management systems have had a relevant effect on production performance improvement. [8] state that Total Quality Management - TQM performs as a strategic factor when products and services differentiation from competitors are sought for. Advancements in TQM methodologies and approaches may significantly contribute in organizations performance. Researches show the TQM approach trend, mainly its continuous improvement focused in the administrative areas of the manufacturing companies, as well as public administration sectors [10] [11]. A subject of interest, mainly due to the object studied in this reserach, is the analysis of factors which help attract investments directly from the multinational companies to the country or a given region. The globally competing multinational company assesses, within its competitive strategy, differential advantages offered by countries to improve its overall performance. During the process of deciding on a plant settlement in a given region, it is of upmost importance to have available a set of reliable factors dealing with international and organizational aspects. However, an inefficient local infrastructure, inadequate supply sources, unskilled labor, are the main obstacles to product quality development in developing countries [12]. The research object of this paper is the Brazilian branch of a multinational company, manufacturing industrial goods. The company s manufacturing strategy in Brazil consists in adapting products according to regional characteristics. As regards new products development, the headquarters design the new products and define their platforms, involving the Brazilian Branch so as to carry out some product line derivatives and extensions tasks. From the managerial point of view, the Brazilian branch has held autonomy to develop internal product projects, complementing knowledge, in some cases, with other branches. 3 RESEARCH METHOD In 2005, nine projects developed in the Brazilian branch were traced, they were platform derivatives initially designed by the headquarters. This activity involved from the project development up to the delivery in the client s site. All the projects were manufactured as make-to-order. Beginning with the number of components and product levels, resulting from the customization to meet specific markets, each project was ranked according to ist complexity degree. The projects which were analyzed had two destinations: the internal market (Brazil) and the external market (USA and Europe). The result variable was the production efficiency, measured according to goals (labor overtime hours which had not been foreseen in the project) and rework labor hours spent during the fabrication. Data were statistically treated thru variance analysis and confidence interval analysis, to check relationship between the variables under study. The variables were named as 1) product complexity, and 2) target market. 3.1 Design of experiments A design of experiments, further than a set of test conditions, is a set of sequential activities which are to be performed to achieve the intended objectives. The main stages are: experiment planning, phenomenon analysis and interpretation, and conclusions. In order to analyze the data with statistics tools, a 2x2 experiment was designed two factors with two levels, as shown in Table 1. Complexity, the independent variable, had levels: 1 low complexity; 2 medium complexity. The variable market had levels: 1 internal; 2 external. All the experiment combinations were satisfactory, nevertheless, the repetitions number was not the same for each combination. Three repetitions were obtained for combination 1, four repetitions were obtained for combination 4, and no repetition took place for combinations 2 and 3. Table 1: Experiment matrix (Ri,j,k : result of result i variable, combination j and repetition k) Comb. Market Complexity Goals 1 Internal Low R111 R211 1 Internal Low R112 R212 1 Internal Low R113 R213 2 External Low R121 R221 3 Internal Medium R131 R231 4 External Medium R141 R241 4 External Medium R142 R242 4 External Medium R143 R243 4 External Medium R144 R244 4 DATA ANALYSIS AND RESULTS INTERPRETATION At this point, it is important to go beyond the formal statistical analysis, and analyze every detail of what happened during the experiment, from contrasting the initial assumptions against the experiment results up to observation of the new learnings achieved on the process, verify presumptions and choose the winner treatment. 4.1 Not randomized table Table 2 shows the percentage results of the nine projects under study, according to the experiments matrix shown in table 1. Goals fulfillment evaluates whether the foreseen labor hours were accomplished; should the spent hours be less than foreseen, the result is negative, what indicates the company spent less hours than foreseen.

3 19 th International Conference on Production Research Table 2: Research results Comb Market Complexity Goals 1 Internal Low -8,5% 13,1% 1 Internal Low -18,7% 9,3% 1 Internal Low -24,5% * 4,8% 2 External Low -2,6% 3,5% 3 Internal Medium 5,3% 18,1% 4 External Medium 29,1% 9,2% 4 External Medium -12,6% 9,8% 4 External Medium 29,2% 18,2% 4 External Medium 26,9% 13,7% The rework indicates that the percentage of labor hours spent in adjustments and parts repairs (resulting from non conformances or due to engineering changes) in relation to the total labor hours spent in the product fabrication. The possitive values in the column of the result variable Goals indicate that the cost targets of labor hours were exceeded. However, the negative values indicate that the foreseen labor hours were below the actual labor hours; in table 2, for example, (-24, 5%) means 24,5% below the planned value. 4.2 Descriptive analysis Table 3 shows mean values resulting from the combination of the studied variables. As it was expected, data show that low complexity products affect the production targets less than the higher complexity products. From table 4, it is possible to verify that higher complexity products resulted in production targets exceeded by 11,73% and their repair jobs increased by 15,41%. Table 3: Mean results Comb Market Complexity Goals 1 Internal Low -17,23% 9,07% 2 External Low -2,60% 3,50% 3 Internal Medium 5,30% 18,1% 4 External Medium 18,15% 12,7% Table 4: Mean results for the variables combination Complexity Goals Low -9,92% 6,28% Medium 11,73% 15,41% Market Goals Internal -5,97% 13,58% External 7,78% 8,11% 4.3 Variance analysis A statistic evidence exists, for a 5% significance level (chart 2), that product complexity affects the rework. However, only for the 16,7% significance level, does the product complexity statistically affect the production targets (chart 1). Chart 1: Variance analysis of the variables product complexity and target market for the response variable goal fulfillment Variables: 1-COMPLEXITY, 2 TARGET MARKET Variable Mean Mean Distribution F Significance Level (p) 1 1 0, ,0279 2,6017 0, , ,0279 1,0473 0, , ,0279 0,0047 0,9481 Chart 2: Variance analysis of the variables product complexity and target market for the response variable rework Variable Variables: 1-COMPLEXITY, 2 TARGET MARKET Mean Mean Distribution F Significance Level (p) 1 1 0, ,0017 7,4210 0, , ,0017 2,6510 0, , ,0017 0,0005 0, Confidence interval analysis The confidence interval analysis shows the interaction effects between independent and dependent variables. Chart 3 presents a confidence interval analysis for the response variable Production goal. Statistical evidence is noticeable showing that a more complex product, meant to be exported (Europe and USA), exhibits production labor hours above foreseen hours. On the other side, when the product is to be traded in Brazil and has low complexity, production goals are achieved.

4 Chart 3: Variance analysis of the variables product complexity and target market for the response variable goal fulfillment. (Homogeneous groups, alfa=0,05). Variables: 1- COMPLEXITY, 2- TARGET MARKET Mean Group 1 Group 2 Low Internal {1} -0,1725 xxxx Low External {2} -0,0260 xxxx xxxx Mean Internal{3} 0,0532 xxxx xxxx Mean External {4} 0,1814 xxxx Chart 4: Variance analysis of the variables product complexity and target market for the response variable rework. (Homogeneous groups, alfa=0,10). Variables: 1-COMPLEXITY, 2- TARGET MARKET Mean Group 1 Group 2 Low External {2} 0,0352 xxxx Low Internal {1} 0,0904 xxxx xxxx Mean External {4} 0,1272 xxxx xxxx Mean Internal {3} 0,1811 xxxx By comparing the variance analysis (chart 1 and 2) and confidence interval analysis (chart 2 and 3), product complexity imposes statistically significant effect on rework. Thus, the more complex the product, the more rework, independently of the target market. Based on the data obtained in this research, it is possible to draw some conclusions on the causes of the target market effect on production goals fulfillment, measured in labor hours spent during the fabrication. Products meant to serve more technologically advanced markets, specifically in the studied company, tend to carry more stringent quality requirements, resulting in more severe procedure and decisions involving products and processes conformance and fitness to use, thus causing more additional labor hours. The statistical results as regards product complexity on production labor hours goals fulfillment as well as the rework increase, can be explained and may be due to the following factors: a) Products derived from a platform, defined by the headquarters, need customized accessories, what calls for Project engineering. Similarly, a new project for the branch increases the degree of uncertainty in the previsions of costs, production and the supply chain as a whole; b) s are bulkier when a new product is produced. The learning curve concept (Render & Heizer, 1996) is applied in such cases, and, for the same reason failures diminish over time as the same product production is repeated. This learning process also takes place within the organization s supplier s plants. 5 CONCLUSIONS In this research, the study concentrated on the relationships between two independent variables (target market and product complexity) and the two dependent variables associated to production efficiency (Percentage of Overtime production labor hours above prevision and labor hours). The statistical results showed that product complexity (differentiated by the number of components in its structure), directly affects the production performance (measured in spent labor hours ). What actually occurs is that the higher product complexity in terms of more structure components, imposes more efforts on projects, supply and suppliers. Consequently, the probability of failures throughout the process, beginning with sales up to delivery, is greater. Since the company produces few units of a given model, having a low repeatibility rate, this calls for more capacity of the people involved throughout the value adding process. In the present case study, the importance of the new products development process on the supply chain is significant, specially when the product is more complex and newer. In such case, the rework es will be higher and, consequently, will the fabrication costs within the whole supply chain. Other factors have been identified in the literature, which can be compared to this paper results, are the influence of brand name and product origin region on client s purchasing decision. It was observed that more demanding clients urge the company to search a better performance in product conformance quality, what leads to more severe decisions on fitness to use. This hypothesis was found statistically valid in this case study. European and American markets are more stringent as regards products quality. Therefore, the impact of the independent variables studied in this research, has a statistically significant effect on the company s efficiency, measured by the independent variables. Consequently, the sales strategy is related to the factory s technical capacity to meet requirements of more technologically advanced markets. Thus, the sales strategy as regards cost and lead times for more complex products for more demanding markets, should differentiate them from the low complexity products for the internal market. The limitation of this paper lies on the fact of having considered only one industrial goods company. Consequently, in order to generalize the preceeding conclusions, it is recommended to study other cases, in different companies and check if the variables behavior is the same as presented hereby. 6 REFERENCES [1] Hill Terry. Operations Management. New York: Palgrave Macmillan Ltd, p.

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