Summary by James R. Martin, Ph.D., CMA
Professor Emeritus, University of South Florida
JIT Main Page | Control/Controllership
Main Page | Structure/Restructure Main Page
The purpose of this paper is to examine the relationship between an organization's control structure and the adoption of just-in-time (JIT) manufacturing technology. More specifically, the authors develop a framework to support and test the contention that mass production firms that adopt JIT need an organic control model rather than a mechanistic model. The empirical sections of the paper include three case studies and survey results from 155 questionnaires.
Technology and Management Control System Design: Theory Development
The authors define and discuss mechanistic and organic control models in this section. Their discussion is summarized in the table below.
| Characteristic | Mechanistic Control | Organic Control |
| Structure | Centralized, bureaucratic & rigid |
Decentralized,
participative, adaptive & flexible |
| Communication | Vertical | Network, horizontal, diagonal and vertical |
| Individual Specialization | High | Multi-function, cross-trained |
| Cooperation & teamwork | Low | High |
This is followed by a discussion of Woodard's three types of technology. The main characteristics and applicable control structure for each type of technology are summarized in the following table.
| Characteristic | Unit or small batch |
Large batch or mass production of standardized products |
Continuous process production of a single bulk product |
| The Work | Craft-like | Highly specialized & standardized | Skilled operators solve problems & maintain equipment |
| Production | Flexible | Semi-automatic & standardized | Automated & standardized |
| Structure needed | Organic | Mechanistic or machine bureaucracy |
Organic |
Kalagnanam and Lindsay continue with a definition and discussion of the JIT philosophy, mentioning a variety of characteristics such as:
dedicated to the elimination of waste,
holistic concept, i.e., manage the whole system not the parts,
customer orientation,
demand-pull system,
quality at the source,
do it right the first time,
flexibility,
flat organization,
employee involvement,
decentralized decisions,
elimination of inventory buffers,
line balancing,
tightly coupled organization processes,
continuous improvement,
high degree of communication and coordination, and
need for the organic model.
This section is supported with Table 1, p. 8. An adaptation of the table appears below.
| Theme or characteristic | Unit production | Traditional mass production | Process production | Just-In-Time production |
| Dominant theme | Product flexibility | Efficiency | Throughput | Production mix, flexibility, efficiency, and throughput |
| Product flexibility | High | Low | Low | Low to medium (unless automated) |
| Production mix flexibility | Low to high | Low | Low | Medium to high |
| Standardization | Low to medium | High | High | High |
| Degree of coupling | Low to medium | Low | High | High |
| Continuous improvement | Low to medium | Low to medium | Low to medium | Medium to high |
Case Studies
This section includes a fairly long discussion of three cases used to support the theoretical framework (pp. 7-18). Each case study is based on interviews conducted during a four day visit. The cases describe each plant's approach to structure and control including a considerable number of quotes. Generally, the cases support the authors' framework by showing that these companies are moving toward less vertical hierarchy, more consensual decision making, more teamwork, more cross functional communication, more emphasis on customer satisfaction, lower inventory levels, and more of a people orientation.
Hypotheses
Two hypotheses were tested.
H1: JIT mass production firms will utilize an organic model of control to a greater extent than traditional mass production firms.
H2: With respect to JIT mass production firms, the utilization of an organic model of control will be associated with higher rates of improvement in key manufacturing areas.
H2 was tested by examining seven key performance areas: quality, inventory reduction, production cycle time reduction, on-time delivery, setup time reduction, production lot-size reduction and overall cost reduction.
Survey
A survey of 1580 plant managers produced 155 usable responses. The low response rate is discussed and explained. Firms were classified as JIT or non-JIT on the basis of whether they were using a demand-pull system. This analysis placed 106 firms in the JIT category. Firms were placed in the mass production category if 40% or more of their capacity was used for large batch production. Twelve plants were placed in the mass production category and 40 firms were classified as JIT mass production. Various test were performed to test the validity of these classifications. The results of these tests are reported in Table 3 (not included) and supported their classifications.
The validity of the mechanistic/organic measure was also examined from various perspectives. According to the authors, both the case studies and the literature review based theory supported this logic.
Hypothesis 1 was tested at two levels: JIT firms regardless of the level of JIT adoption, and firms with 60% or more production devoted to JIT. Regression analysis was based on the following equation:
Y = b0 + b1X1+ b2X2
where: Y = the form of management control.
Low values = mechanistic. High values = organic.
X1 =
size based on total employees.
X2 = a
dummy variable where Mass = 0 and JIT = 1.
The results are consistent with hypothesis 1. The adjusted R2 = .23 for the model. When only high JIT firms were examined the adjusted R2 increased to .38.
Spearman rank correlations between performance improvement variables and control method for JIT mass production firms were used to identify the relationships related to hypothesis 2. These results supported the hypothesis in all seven performance areas, but were considered tentative by the authors.
Conclusion
The findings can be generalized as follows. The size of the relationships predicted by the model increased as the level of capacity devoted to JIT increased. The results are consistent with other studies and seem to be consistent across studies regardless of the methodology used. In addition, this study makes a contribution by providing a method for classifying JIT firms and measuring the mechanistic/organic construct.
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