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There are five distinct research components in this project.  Two are outside of the scope of the original proposal.  The findings of five the components are summarized in I - V below.

I.  General Findings

The first phase of the study collected data on technology use through a survey.  Questionnaires were sent to members of the National Association of Manufacturers (NAM), Washington D.C. who belonged to industries covered by SIC 34-38 (Metal fabrication; machinery including computers; electrical; transportation; and instruments and photo goods).  A total of 1,025 responses were useable for a response rate of 21.4% (see, Swamidass, 1998).
This research studied manufacturing technology use, changes in its use since 1993, benefits of technology use, computer integration between internal and external units of a factory, training budgets, training effectiveness, causes and duration of delays in technology use, and the profile of operators in U.S. factories.
 The study showed that labor productivity increased at the rate of 3.5% per year, compounded between 1993 and 1997, and return on investment stood at 16.9 percent in 1997 compared to 13.9 percent in 1993.  Inventory turns improved to 9.7 from 8.0 between 1994 and 1997.  Overall, manufacturing performance was very healthy.
 The 17 technologies investigated included hard technologies (automated inspection, CAD, CAM, CNC, CIM, LAN, FMS, and robots), and soft technologies (bar codes, concurrent engineering, manufacturing cells, MRP, MRPII, SQC, simulation, and TQM).  During 1993-1997, Local Area Network (LAN) use increased more than any of the other 17 technologies studied.  The implications are that manufacturing plants are investing more on computer integration.  Manufacturing cell use has also grown substantially since 1993.
 A greater proportion of larger plants (employment > 100) use technologies than smaller plants.  Exporters use more technologies than non-exporters.  On the average, manufacturers spend five percent of payroll for operator training.  On-the-job training is the most frequently used training technique.  The lack of trained operators delays skilled use of technology by about 4.9 months.

II.  A Neural Model of Factory

 Using the data collected above, a neural model of the factory was developed by Dr. Nair, University of Missouri under a subcontract.  The model developed is an extension of the model developed by Swamidass, Nair and Mistry (1999) using an earlier dataset.  The model’s purpose was to investigate the effect of soft-and hard- technology use and selected variables on key outputs such as, inventory turns, quality, sales per employee, lead time, and an overall evaluation of manufacturing.
 Several neural models were successfully completed.  A sample of the results are described below where inventory turns are used as the measure of factory performance.  Note that, when inventory turns increase, inventories decreases, which is desirable.  Large inventory turns indicate efficient production.

Line Production

 As soft technology use increases inventory turns increases.  When soft technology use was varied from -1 to +1 standard deviations, inventory turns increased from 6 to 14.  On the other hand, inventory turns decreased from 21 to 6 when hard technology use was varied from -1 to +1 standard deviations of the average.
 Inventory turns increased from 7.5 to 28 when training expenses were varied -1 to +1 standard deviations of the mean.

Job Shop

 In a job shop, when hard technology use is increased from -1 to +1 standard deviations of the average, inventory turns increase from 6.5 to 8.  This effect is quite the opposite in line production.
 As soft technology use increases, there is no appreciable change in inventory turns, while there is significant increase in inventory turns in line production under similar circumstances.
 As computer integration between the shop floor and production/material planning increases, from -1 to +1 standard deviations of the average, inventory turns increases from 6 to 9.
 When training expenses are increased, inventory turns increase in a manner identical to the increase in line production.

III.  Efficient users of Technology (Beyond the scope of the original proposal to NSF)

 Dr. Pat McMullen of Auburn University was invited to use the data collected in I above to conduct Data Envelopment Analysis (DEA) to find efficient and non-efficient users of manufacturing technology.
 Some large plants are the most efficient users of technology; that is, they use less technology but report more benefits than all other plants.  However, not all large plants are efficient users.  The findings show that the efficient users of technology (51 plants; about 5% of the total) use less technology than non-efficient users, and report:
1.  better inventory turns 26.4 vs. 8.8; and
2.  better return on investment 23.1% vs. 16.5%

IV.  A comparison of Technology use in the U.S. and U.K. (Beyond the scope of the original proposal to NSF)

 There is interest among U.S. manufacturers and policy makers to compare technology use in the U.S. with other developed nations.  To fulfill this need an opportunity to study U.K. companies was developed.  Through the Joint US-European Technology Adoption Project created  by the Thomas Walter Center for Technology Management, Auburn University and the University of Plymouth, Dr. Graham Winch, Professor of Strategic Analysis, University of Plymouth, U.K. collected identical data from U.K. manufacturers in similar industries employing the questionnaire developed and used in the U.S.A. under this grant (see I above).  A total of 274 responses were received and processed at Auburn.  Results show that generally, factories are similar in character and in technology use in the two countries.  However, U.S. companies have an across-the-board lead over U.K. plants in computer integration between internal and external units of a plant.  This is the first such technology use comparison between the U.S. and a western European nation.

V.  In-depth study of Factors promoting and hindering Investment in Manufacturing Technology.

 The second year of this grant was meant for the in-depth study of 10 plants (A mix of heavy users and light users of technology) to find the factors that promote or hinder investments in manufacturing technology.  Plants for visit and in-depth study were chosen from the responses to the survey described above (I).  Plant visits were made to ten plants for face-to-face interview with key personnel such as plant managers and the head of manufacturing to gather data.  Preliminary findings show that:
1.   A steady capital budget of 2-3 percent of sales enables manufacturing to invest steadily in new hard and soft technologies.
2.   Most investments in manufacturing technology are recovered in one or two years.  Given this return, it is unwise not to invest.
3.   Investment in manufacturing technology is not made for cost reduction alone.  Manufacturing lead-time reduction and flexibility increase are strategic reason for investment.
4.   When CEOs have engineering backgrounds, investments in technology occur easily.
5.   The first use of certain technologies including CNC machines and robots may require a long learning phase.  Subsequent investments in similar equipment are easier to install and reap benefits.
6.  A dedicated staff of engineers and technical programmers are used to support hard-technologies such as CNCs, robots, etc.  The effective and productive use of these technologies is a function of the support staff.

VI.  Publications based on NSF grant

1. Swamidass, P.M., A.Nair. "What top management thinks about the benefits of hard and soft manufacturing technologies," IEEE Transactions in Engineering Management, 51(4), November, 2004.

2. Swamidass P.M. "Modeling the adoption rates of manufacturing technology innovations by small US manufacturers: A longitudinal investigation.", Research Policy, Feb. 2003, pp. 351-366.

3. Swamidass, P.M. and G.W. Winch. "An exploratory study of the adoption of manufacturing technology innovations in the USA and the UK," International Journal of Production Research,Vol.40(12) 2002,pp.2677-2703.

4. Swamidass, P. M., “Improving Productivity through Technology: A  U.S. Perspective”, Search: The Industrial Sourcebook (India), January 2001, pp. 346-348.

5. Swamidass, P.M., S.S. Nair and S.I. Mistry.  "The use of a neural factory to investigate the effect of product line width on manufacturing performance", Management Science, 45(11) Nov. 1999  - uses an earlier dataset, and describes the precursor to the neural model of factory developed under this grant.

6.  Swamidass, P.M.  Technology on the Factory Floor III: Technology use and Training in the U.S.  The Manufacturing Institute of the National association of Manufacturers, Washington, D.C., 1998.

7.  Swamidass, P.M.  "Manufacturing Technology use in the U.S. and benefits," in the Encyclopedia of Production and Manufacturing Management.  Edited by P.M. Swamidass.  Boston;  Kluwer Academic Press.  2000.

8.  Swamidass, P.M.  "Benchmarking Manufacturing Technology use in the U.S. " in Innovations in Competitive Manufacturing.  Edited by Paul  M. Swamidass, AMACOM, 2002

Media use of the research
a.   "U.S. industry back on its feet in a wobbly world, "New York Times, Business Day section, May 15,1999 (C1).
b.   A New York Times web version of the above titled "Manufacturing thriving even as employment falls," May 15, 1999.
c.   "Inventory Control helps stabilize U.S. economic growth levels," Asian Wall Street Journal, Sept. 2, 1997.
d.   "How Plants Increase Productivity" Manufacturing Engineering, 11/98, p. 18-22.
e.   "Technology makes manufacturing more productive and profitable," Alabama Today, Oct.-Nov. 1998, p.6-7.
f.    "Use of Technology Separates Exporters from non-exporters." Clearinghouse on State International Policies, Sept/Oct. 1998, P.3.
g.   "Computers boost U.S. productivity," San Jose Mercury News, Feb.6, 1998.
h.   "Manufacturing industy urged to emulate IT industry," Deccan Herald, Bangalore, India, Nov. 18, 1999.