Strategic Decision Making of the Product-Mix Using a New Demand Forecasting Model in the Manufacturing Industry

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1 Original Paper J Jpn Ind Manage Assoc 64, , 2014 Strategic Decision Making of the Product-Mix Using a New Model in the Manufacturing Industry Sumiyoshi OHTA 1, Ryuichi HIRAMOTO 2 and Akira KITAMURA 3 Abstract: In recent years, diversified market needs have led to the constant use of high-mix, low-volume production in the manufacturing industry. However, there has been little research on the development of models that appropriately consider everyday market needs or of product-mix decision making methodologies that strategically use such models. In this study, a new model was developed based on genetic programming (GP), using experimental forecasting factors derived from sales activities with customers in metropolitan areas as the decision variable. The authors identify the process used to achieve improved accuracy by solving product-mix decision-making and how it impacts the manufacturing industry using Profit Contribution Analysis. Furthermore, a method to quantitatively identify the need for introducing new technologies to realize product-mixes using the aforementioned method is examined. Finally, the effectiveness of this method is validated using actual data from small and medium-sized manufacturers located in a regional area. Key words: Product-mix,, Profit Contribution Analysis 1 INTRODUCTION In recent years, diversified market needs have led to the constant use of high-mix, low-volume production and the development of new technologies in the manufacturing industry. In addition, the need for business innovation to sustain these changes in internal and external environments is accelerating. Accordingly, there is an increased need for establishing a proper method of evaluating optimal product-mix decisions among many product lineups in the manufacturing industry. Profit Contribution Analysis is a leading benchmark for making decisions regarding the Product-mixes. However, calculations are based solely on past internal financial data. These are few cases where such an analysis been considered in conjunction with for future markets or new technology development in the manufacturing sector. Furthermore, there has been little research on the development of models that appropriately consider everyday market needs or productmix decision-making methodologies that use these models strategically. Previous research into performance assessment for high-mix, low-volume production includes Sharma (2007) [1]. This research presents a substitution model for production costs and production rates by product under constraints of a given total production time. Adachi (2010) [2] presents an assessment approach on the basis of hourly marginal profitability using a technical management approach toward the manufacturing and sales 1 Tottori University, Innovation Science Center, JAPAN 2 Tottori University, Graduate School Faculty of Engineering, JAPAN 3 Tottori University, Graduate School Faculty of Engineering, JAPAN Received: November 30, 2012 Accepted: October 31, 2013 divisions at a medium-sized manufacturing company located outside a major metropolitan area. Kagami (2010)[3] lays out the relationship between the necessity for production goods marketing by sales staff and new business development. However, one may point out the following problems with these papers. (1) Throughput is generated only through sales, never by production (Goldratt, 2002) [4]. Therefore, when determining a product-mix, it is important to focus on the relevance to sales and how the profits contribute to the overall company. (2) Annual demand for a product is given as an assumption, with no consideration of how the accuracy of such would affect production plans. Therefore, production plans are not necessarily adequately based on an accurate assessment of market needs. (3) The importance of forecast factors based on the experience of sales staff, with their intimate relationships to their markets, is granted; however, there are no forecast models incorporating that as a decision variable presented. (4) No consideration is given to the development of new technologies for production where demand is anticipated to expand in the future being linked to improvements in internal production processes and the kind of assessment approach used to prove the legitimacy of such strategic judgments. Considering the above, if these issues are to be proposed as an approach methodology based on more concrete case studies, the hypothesis holds that such research will be of more practical value than that conducted in the past. 614 J Jpn Ind Manage Assoc

2 2 THE PROBLEM TO BE SOLVED Based on the above-mentioned background, the problems that should be solved in this study are as follows. 2.1 Development of a Model Considering Market Characteristics In the manufacturing industry, production schedules are made based on. An exaggerated forecast may lead to over-production and excess inventory. On the other hand, if the forecast is understated, product stock-outs will cause lost sales opportunities and possibly decrease in customer loyalty. Therefore, is one of the most important management problems. So the issue for the manufacturing industry is whether or not it is possible to develop a new model that considers everyday market characteristics such as economic trends and the experimental judgments of sales staff. The reasons for this are as follows. 1) In manufacturing, the needs of the market are the most important in deciding a product-mix, and highly reliable prediction becomes indispensable to grasp this exactly. 2) Looking at market potential factors seems obvious; however, in reality, the vast majority of traditional forecasting models are computed using past sales data only, with almost none virtually structured to include market business activity as a decision variable (Asada, et al.,2005) [5]. 3) Marketing strategies of manufacturers are very different from that of wholesale or retail enterprises. In other words, in contrast to the unspecified large number of buyers, and open sales channels in the wholesale/ retail world, manufacturers generally have a set number of buyers, and restricted sales channels. In addition, companies rely on internal sales staff to learn the needs of customers, a critical factor in. So the market characteristics are obtained through sales staff who interpret of the market needs, and are an important and determinative source. 4) In Japan, 99.7% of the companies are small and medium-sized businesses, and they usually have sales offices in metropolitan areas, including Tokyo and Osaka, and a production base somewhere in an outlying district. So it is necessary to consider business trends and the economic situation of the metropolitan areas in order to conduct more appropriate. 2.2 New Model and Decision Making of Product-Mix Using Profit Contribution Analysis The second issue is identifying the process by which improvements in accuracy can be achieved by solving the first issue above and how it impacts product-mix decision-making within the manufacturing industry. We must identify a means to appropriately evaluate product-mixes in the manufacturing industry. First is to show what metrics are used to make product-mix decisions, and second is to understand the impact that improvements to accuracy have on the structural elements of these metrics. 2.3 Need Judgment of New Technology Introduction to Realize an Effective Product-Mix In the manufacturing industry, the case of not being able to realize the appropriate production system to satisfy market demand due to extremely difficult production is good, even if there is a known high demand for the product. If new production technologies can be developed that improve product quality and reduce production lead times a tremendous impact on product-mixes within the company can be expected. Accordingly, as with the second issue, we must specifically consider the need for implementing new production technologies in order to achieve the desired product-mix. 3 APPROACH TECHNIQUE IN THIS STUDY For each of the three above-mentioned problems, we show below the approach technique in this study. 3.1 Development Method of the Model Considering Market Characteristics The new model proposed in this study uses a genetic programming (GP) model, shown as follows[6] Y i (t)= f(x oi, x 1i, x 2 ) (1) This model focuses on the characteristics of small and medium-sized manufacturers and market characteristics and then incorporates these factors into the model as decision variables. (1) On the basis of interviews of sales staff working for manufacturing companies in metropolitan of fices, we used the Delphi Method 1) to carefully select and extract empirical forecast factors that sales staff experience in the course of daily sales activities vis-à-vis customers, such as the number of customers or frequency of orders, and incorporate them into the Demand Forecasting model as decision variables. (2) Considering that the majority of a company s customers are large firms located in metropolitan areas, we select from the economic indicators 2), the macroeconomic indices published each month by the Cabinet Office, those most relevant to the company s business, and incorporate them into the model as decision variables. (3) Forecast revisions using fuzzy logic for expected rush demand due to price changes or other market events. Vol.64 No.4E (2014) 615

3 We show the flow of the above-mentioned new Demand Forecasting model in Fig.1. Start Data Input Calculation Using GP Yes Sudden Demand Spike? Correct Using Fuzzy Logic Correct New Values from GP Model End No Market Characteristics Interpreted by Sales Staff using the Delphi Method as decision variable Market Characteristics derived from the Composite Indexes as decision variable Fig.1 Flowchart depicting the new model 3.2 New Model and Product-Mix Decision making Method Using Profit Contribution Analysis In this study, we use Profit Contribution Analysis as the judgment index for making decisions regarding productmix in the manufacturing industry. It is a leading benchmark for demonstrating the extent to which product sales continues to contribute to achieving improvements for the company as a whole, and generally calculated using Eq. (2). CP i = CS i GPR i 100 TO i (2) CP i :contribution to profits of item i, CS i :component ratio of sales revenue of item i (%), GPR i :gross profit ratio of item i (%), TO i :inventory turnover ratio of item i (times) The inventory turnover ratio of item i is calculated from Eq. (3). TOi=SRi/AIi (3) TOi:inventory turnover ratio of item i (times) SRi :actual sales revenue of item i (yen) AIi : average inventory of item i (yen) In this study we focus on inventory turnover ratio (TOi) at the right side of Eq. (2), from which profit contribution is calculated. We also validate how inventory turnover changes with improvements in accuracy, as well as the subsequent changes to product-mix decision-making that result from improved. The known facts are as follows. 1) In manufacturing, any discrepancies between forecasted and actual demands become de facto surplus stock (i.e., when forecasted values > actual values). So if the precision of improves, average inventory (AIi) in Eq. (3) decreases. 2) If AIi decreases, the inventory turnover ratio (TOi) in Eq. (3) improves. 3) If TOi improves, contribution to profits (CPi) in Eq. (2) improves. 3.3 Need Judgment Method for Introducing New Technologies to Realize an Effective Product-Mix This study picks a product for which high market demand is verified due to improved accuracy in Demand Forecasting yet is difficult to make. We quantitatively compare the effect on product-mix strategy before and after implementing new production technologies. As in 3.2, we focus on gross profit ratio (GPRi) at the right side of Eq. (2) from which profit contribution is calculated. We also validate how GPRi changes with the introduction of new production technologies, as well as the subsequent changes to product-mix decision-making. In manufacturing, the introduction of new production technologies leads to a reduction in manufacturing costs by shortening task times and requiring less staff and equipment, both of which are factors in increasing gross profit ratio and profit contribution. The gross profit ratio of item i can be represented as shown in Eq. (4). GPR i = (SR i -PC i ) / SR i (4) GPR i :gross profit ratio of item i (%) SR i :actual sales revenue of item i (Yen), PC i :total product cost of item i (Yen) 4 CASE STUDY In this study we use the actual data of Company X (solderless terminal manufacturer located in Tottori City,; capital: 98 million yen; employees: 55) collected between Nov.2009 and Feb.2011, and analyze the results. Company X is a typical medium-sized regional Japanese manufacturing business; with its headquarters outside of major metropolitan areas, but with sales offices in 616 J Jpn Ind Manage Assoc

4 metropolises such as Tokyo and Osaka, that sell electronics parts to major electronics companies. Fig.2 shows the solderless terminals that are the principal product of Company X. They are small electronics parts that attach to the tip of wires and facilitate the connection between the electric wire and terminal blocks of electronic equipment such as power distribution boards. Unit of Sales Volume (1,000 units) 4,000 3,500 3,000 2,500 2,000 1,500 1, Actual Forecast Fig.2 Solderless terminals (from left, R-,F-, and A-types) 4.1 The New Model Considering Market Characteristics We show below model-1 (traditional method) and model-2 (new method, GP Model) of Company X. 1) model-1: The simple moving average method of the most recent three months. 2) model-2 (GP Model): Three inputs shown in Eq. (1) above. Y i (t)= f(x oi, x 1i, x 2 ) (1) Yi(t): number of units sold of item i (in the t month) x 0 : frequency of orders of item i (by month) x 1 : number of customers of item i (by month) x 2 : index of shipment in small- and medium sized enterprises (by month) Fig.3 and Fig.4 compare the forecasted results from two models each. They show the variances between forecasted and actual values for Company X s top-selling products for a particular period (Nov Feb. 2011). The smaller the variance the higher the accuracy of the model. This study uses the average absolute error rate for evaluating accuracy, as calculated by Eq. (5) below. error : average absolute error rate y(t) : actual value (in the t month) y (t) : forecast value (in the t month) N : number of datapoints The average absolute error rate of two method is shown by Table 1. (5) Fig.3 Results of model-1 Unit of Sales Volume (1,000 units) 4,000 3,500 3,000 2,500 2,000 1,500 1, Sales Month Sales Month Fig.4 Results of model-2 Table 1 Average absolute error rate of Demand Fore casting Average Absolute Error Rate (%) No correction for rush demand Actual Forecast Correction for rush demand model model The above results demonstrate the effectiveness of the model, for small and medium-sized manufacturers outside metropolitan areas, market potential factors in metropolitan areas are taken into account and incorporated as decision variables. 4.2 New Model and Product-mix Decision making Using Profit Contribution Analysis We verified the influence that a new model has on the decision-making process for productmix. Table 2 shows the results of comparing the prediction rates of the models -1and 2 for the top five items of Company X (item A E). It shows that inventory turnover is significantly different when comparing the model-1 and model-2, and the results reflect this as a contribution to profit. Vol.64 No.4E (2014) 617

5 Table 2 Comparison of the results of for the top five items Component ratio of Sales Revenue A Rate of Return B Inventory Turnover Ratio C model-1 model-2 Profit contribution A*B*C model-1 item A 5.3% 29.9% item B 5.5% 32.1% item C 3.3% 25.1% item D 1.7% 31.1% item E 1.6% 29.1% Of particular note is item D in Table 2. The profit contribution from traditional model-1 for this product was lowest among the top five products, but it rose to number three in profit contribution with the new model-2. These results show that developing a new Demand Forecasting model that considers market factors not only improves forecasting accuracy but also causes products previously hidden in the product-mix to come to the forefront in terms of profit contribution. 4.3 Need Judgment of New Technology Introduction to Realize an Effective Product-Mix We examined the need of being able to judge the introduction of new production technologies to realize an effective product-mix, as mentioned above using the actual data of Company X and profit contribution analysis Of the top five items of Company X, while there is strong market demand as verified by the improved accuracy of our model, three products cannot yet meet market demand entirely due to compromising production technology issues: 1) item C and D Large in size and the brazing process is extremely difficult. Therefore, introducing new technology is expected to greatly shorten the production time. 2) item E This is a rod-type terminal (rightmost in Fig.2), where the level of difficulty in processing is extremely high compared to other products, thereby resulting in low yield. However, being an insertion-type terminal, the market need is high because of ease of attachment and detachment. Thus, if the technical issues can be resolved, a sharp increase in future demand is expected. Company X is planning to limit the implementation of electron-beam welding as a new production technology exclusively for the three items discussed avobe. Electronbeam welding has already been proven successful in the model-2 automotive industry, but there is no precedent in the solderless terminal industry. Fig.5 shows a comparison of product processes after introduction of the new technologies, for the top five items of Company X. Accordingly, Company X realized shorter manufacturing times, which means reducing labor cost and improving the gross profit ratio. machines Type High-Speed Press Machine Single-Action Press P1 5 Single-Action Press P5 Product A,B C,D E Speed of Operation 5,000 units 20~50 units 20 units machines Workers Degreasing Process Outsourcing Company A Speed of Operation Total time 6 hours machines Type Gas Before Press Process Brazing Process Gas Gas After Electron Beam Product A,B C,D E C~E Product Type F type R type A type R,A type Speed of Operation 3,300 units 8~67 units 8 units 30 units machines Workers Plating Process Outsourcing Company B Speed of Operation Total time 24 hours Inspection Process Speed of Operation Total time Workers hours Transport Fig.5 Comparison of product processes after introducing new technologies for the top five items Next, we validate how implementation of new production technology for the bottleneck process will affect profit contribution and the changes in product-mix decisionmaking. Table 3 shows the impact of introducing new technologies in terms of contributing to the profit of Company X. Of particular note is item E. The profit contribution of item E rose from 1.59 to 3.18 because of new technology development, pushing it up to No.2 of the five leading products. Therefore, it will have a substantial impact on the product-mix strategy of small and medium-sized business with 618 J Jpn Ind Manage Assoc

6 fewer management resources such as Company X. It enables them to get the most effective item, with the proof of a quantitative effect. Table 3 Calculation of profit contribution as the result of Introducing new technologies 6 CONCLUSION model-2 item A 5.3% 29.9% 29.9% item B 5.5% 32.1% 32.1% item C 3.3% 25.1% 27.6% item D 1.7% 31.1% 33.9% item E 1.6% 29.1% 58.2% This study examines how product-mix strategy decisions are made within the manufacturing industry, where high-mix, low-volume production is becoming the norm, and considers a company s external factors of Demand Forecasting as well as internal factors like the introduction of new production technologies. An investigation was done using actual data from a medium-sized manufacturer located outside the major metropolitan areas of Japan. We have shown that profit contribution analysis, one of the methodologies for measuring the level of contribution to profit using manufactured products, can have a strong influence on product-mix strategy. Fig.6 summarizes, for five main items of Company X, the relationship between the new model and the introduction of new technologies on the one hand, and profit contribution on the other. This led to changes in profit contribution for the main products, which greatly influences the product-mix strategy Component ratio of Sales Revenue A Rate of Return B Traditional Gas New Technology Development Inventory Turnover Ratio C Profit contribution A*B*C Fig.6 Influence on profit contribution by and new technology introduction Traditional Gas New Technology Introduction itema itemb itemc itemd iteme New Technology Development 3) New model-2 old In addition, in the business world, profit contribution analysis has been found to be useful primarily in wholesaling/retailing rather than manufacturing. Calculations have relied on internal historical financial data, so there has been little discussion of its relation to future market forecasts. However, we noted the characteristics of the manufacturing segment that differ from those of wholesaling/retailing and considered the relationship between Demand Forecasting and new technology introduction. We have shown that profit contribution analysis can not only be a valuable analytical tool in manufacturing but also lead to optimal product-mix strategies. NOTES 1) The Delphi Method was developed by Rand Corporation, an American think tank in the 1950s. It is an opinion convergence technique, in which an iterative questionnaire is converged and refined organizationally to explore the intuitive opinions, experiences, and judgment of a group of experts. It is a method commonly used in qualitative research to make predictions about factors such as technological innovation and social change. In the aggregation of opinions, the median and inter-quartile ranges are often used. 2) There are two types of business conditions: The diffusion index (DI) shows the direction of economic expansion or contraction, and the composite index (CI) shows the quantitative degree of economic expansion or contraction. A reference year is set for CI, and the percentage of change is indexed according to established rules. REFERENCE [1] Sharma, S.: A Fresh Approach to Performance Evaluation in a Multi-item Production Scenario, Eur. J. Oper. Res., Vol 178, pp (2007) [2] Adachi, H.: Conversion of the Business Structure and NEMA Campaign in EYETEC Co., Ltd, J. Fukui Pref. Univ., Vol.10, pp (2010) [3] Kagami, T.: A New Business Development and the Building Process of the Marketing Capability, J. Fukui Pref. Univ., Vol.10, pp (2010) [4] Goldratt, E. M. (Translated by Sanbongi, T.) Change the Rules: We Should Be Able to Make a Profit, So Why Can t We?, Daiamondo-sha, pp.97(2002) [5] Asada, K., Iwasaki, T. and Aoyama, Y.: Introduction to for Inventory Management, Toyokeizai Shinpo-sha, pp (2005) [6] Ohta, S., Hiramoto, R. and Kitamura, A.: For Smalland Medium-sized Manufacturer Model and Optimization of Corporate Profitability by considering Marginal Profit, International Academy of Business and Economics 2012 Las Vegas. (2012) Vol.64 No.4E (2014) 619