A TECHNOLOGY ASSESSMENT OF MAINE'S METALS, ELECTRONICS, AND INSTRUMENTATION INDUSTRIES (PART III): DOES TECHNOLOGY AFFECT PERFORMANCE?

by Bruce H. Andrews, Professor of Business Administration; John B. Jensen, Assistant Professor of Business Administration; and Tracy Gowen, M.B.A. Graduate Assistant, all at the University of Southern Maine

Technology Use among Precision Manufacturers in Maine

In an earlier issue of Maine Business Indicators the results of a manufacturing technology survey conducted by The Center for Technology Transfer (CTT), the Maine Metal Products Association (MMPA), and the Center for Business and Economic Research (CBER) were explored. The survey was designed to assess the level of manufacturing technology used in Maine's metal products, electronics, and instrumentation industries. The 15 hard and soft technologies studied were enumerated as follows:

Table 1

TECHNOLOGY (including abbreviation)

In a statewide mail survey, metal products, electronics, and instrumentation manufacturers in Maine were asked to rate (as high, medium, low, or none) their current skill levels in these fifteen advanced manufacturing technologies. In this final installment, the focus is on examining five key performance measures and the extent to which they relate to technology adoption. Due to inherent differences in their nature, job shops and repetitive process shops are examined separately. Of the 68 Maine manufacturers who responded to this survey, 43 declared themselves to be job shops and 22 declared themselves to be repetitive process shops. (The remaining three could not be classified as either and were dropped from the survey.)

Table 2 profiles the responding job shops and repetitive process shops in terms of their current size-dependent and efficiency-dependent performance levels. By all seven size-dependent metrics, repetitive process shops handily outsize job shops. On average, repetitive shops employ over five times more individuals, manufacture almost three times more products, serve almost five times more customers, and produce nearly seven times more sales when compared to job shops. Contrasting the average efficiency-dependent performance levels of job shops with repetitive process shops in Maine reveals further dissimilarities. While the average job shop turns its inventory more often and correspondingly enjoys shorter product lead times, both sales per employee and pretax R.O.I. are substantially higher in repetitive process shops. Interestingly, the average rejection/rework rates for the two types of firms are identical.
 
Table 2
CURRENT SIZE AND PERFORMANCE LEVELS
OF JOB SHOPS VS. REPETITIVE PROCESS SHOPS
                     
    Job Shops Repetitive Process Shops Ratio of Averages
  Measure Avg. Min. Max. Range/Avg. Avg. Min. Max. Range/Avg. Repet./Jobs
A. Size                  
1 Non-mgt. employees 35 1 160 4.5 210 1 1,392 6.6 6.0
2 Mgt. employees 7 1 30 4.1 23 1 100 4.3 3.3
3 Total employees 42 2 176 4.1 233 2 1,412 6.1 5.5
4 Products manufactured 512 2 4,100 8.0 1,428 3 7,400 5.2 2.8
5 Customers 242 2 1,600 6.6 1,140 3 15,000 13.2 4.7
6 Annual orders 1,915 18 10,000 5.2 5,817 32 45,000 7.7 3.0
7 Annual sales ($000) 4,236 30 30,000 7.1 28,523 210 191,000 6.7 6.7
                     
B. Performance Level                  
1 Inventory turns per year 26 4 100 3.7 6 1 24 3.8 0.2
2 Production lead time (weeks) 5 1 14 2.6 7 1 28 3.9 1.4
3 Rejection / rework rate (%) 3 0 30 10.0 3 1 10 3.0 1.0
4 Sales per employee ($000) 88 10 346 3.8 139 30 300 1.9 1.6
5 Pretax R.O.I. 12 0 60 5.0 29 1 160 5.5 2.4
Since Maine job shops tend (on average) to be much smaller and much less profitable than Maine repetitive process manufacturers, it is not surprising that job shop technology adoption rates tend to lag repetitive process shop technology adoption rates. Without exception, job shops appear to be less prone to adopt advanced manufacturing technologies (see Figure 1). Not only may job shops be less able to afford the installation and maintenance of advanced manufacturing technologies, but by their very nature they are probably less likely to enjoy the benefits of a sharpened focus which accompanies specialization. Thus, the job shops may be presented with fewer opportunities to install advanced technologies profitably.
 

Performance Differences between Technology Adopters and Non-Adopters

Treating job shops and repetitive process shops separately, Figures 2, 3, 4, 5, and 6 focus on efficiency-related performance differences between firms that have adopted each specific technology with those firms that have not. The intent is to see whether adopters significantly outperform non-adopters across five important efficiency-related measures. For each of the fifteen manufacturing technologies, the average level of performance of non-adopting manufacturers is subtracted from the adopter group's average level of performance. For example, Figure 2 displays differences in inventory turns for technology adopters and non-adopters. In terms of the relationship between the use of CAD and the number of times a firm turns its inventory, job shops that adopt CAD tend to turn their inventory 16 fewer times per year on average than job shops that have not adopted CAD. Alternatively, repetitive process shops that have adopted CAD turn their inventory 4 more times per year, on average, than those repetitive process shops that have not.  Figures 3, 4, 5, and 6 show the same difference comparisons for the remaining four performance measures.

Of the 150 (2x15x5) possible differences in average performance levels presented in Figures 2, 3, 4, 5, and 6, seven could not be reported due to missing data. Of the 143 computable differences, 74 (51.7%) showed adopters outperforming non-adopters and 69 (48.3%) showed non-adopters outperforming adopters. While it is not reasonable to assume that all technologies will simultaneously improve all performance measures, only 10 of the 143 computable differences were statistically significant at the 10% level. In other words, significantly different performance between firms that employ a specific technology from those that have not adopted that same technology were shown in less than 10% of the comparisons made. At a 10% significance level, 14 (10% of 143) significant differences would be expected even if the technology usage and performance were unrelated. Further, only four of these 10 statistically significant differences resulted in adopters outperforming non-adopters. In the remaining six cases non-adopters actually outperformed adopters. Thus, it is difficult to argue from these data that the adoption of manufacturing technologies leads to improved performance levels in either job shops or repetitive process shops.

Figure 5 (Sales per Employee) and Figure 6 (Pretax R.O.I.) present difference patterns which, while not statistically significant, do support the assertion that, on average, higher performers are heavier users of manufacturing technologies. It is not clear from these data, however, that the higher average levels of performance are a result of higher levels of adoption of technology. To the contrary, it may simply be the case that the firms with better financial performance are in a better position to afford these technologies.