KEY WORDS: program plan; interpretive structural modeling (ISM); hierarchy; ..... WASTE. MANAGEMENT. 5. TO iMPROVE QUALITY. OF. COAL. 6. TO.
Systems Practice, Vol. 5, No. 6, 1992
Hierarchy and Classification of Program Plan Elements Using Interpretive Structural Modeling: A Case Study of Energy Conservation in the Indian Cement Industry J. P. S a x e n a , 1 Sushil, 2 a n d P r e m Vrat 3
A program can be analyzed and studied by dividing it into elements and subelements. Graphical presentation of the driver power and dependence of the subelements of the identified elements assists in their classification in categories representing the nature of the role each subelement plays as a variable in the program. This paper presents methodology for the hierarchy building of subelements, graphical presentation of their driver power and dependence, and classification in categories such as autonomous, dependent, linkages, and independent variables. The paper further presents a case study of energy conservation in the Indian cement industry to determine the hierarchy of program plan elements and to classify them in categories. Key variables of the program are also highlighted.
KEY WORDS: program plan; interpretive structural modeling (ISM); hierarchy; classification; key variables.
1. I N T R O D U C T I O N T h e s y s t e m a t i c a n a l y s i s o f a p r o g r a m as a w h o l e is o f g r e a t v a l u e for its effective i m p l e m e n t a t i o n a n d u s e f u l n e s s to society in m e e t i n g p r e s e n t as w e l l as future n e e d s . F o r this p u r p o s e , t h e p r o g r a m c a n b e d i v i d e d into n i n e e l e m e n t s as d e l i n e a t e d b y Hill a n d W a r f i e l d (1972): (i)
t h e s o c i e t a l sectors affected,
t National Council for Cement and Building Materials, M-10, South Extension Part II, New Delhi 1 I0 049, India. 2Centre for Management Studies, Indian Institute of Technology, Hauz Khas, New Delhi 110 016, India. 3Department of Mechanical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi 110 016, India.
651 0894-9859/92/12004365.1506.50/09 1992PlenumPublishingCorporation
652
Saxena, Sushil, and Vrat
the needs of the program, major constraints, the alterables which could be altered, (v) the objectives of the program, (vi) the objective measures to evaluate each objective, (vii) the activities needed for the action plan, (viii) the activity measures to evaluate the results achieved from each activity, and (ix) the agencies involved in execution of the program. (ii) (iii) (iv)
Each element is further subdivided into a number of subelements as considered appropriate. The study of program planning linkages provides a thorough understanding of the issues involved relating to various elements considered and the role of agencies and an appreciation of problems of others for evolving an integrated approach leading to a better and acceptable solution. Yet the methodology is lacking in effectiveness, due to the absence of any hierarchical relationship between various subelements of an element. An understanding of the hierarchy of the subelements, and their driver power and dependence, and classification into various categories of variables representing the characteristics of the program elements are essential for an indepth appreciation of the issues at hand. Interpretive structural modeling (ISM) provides a base for such an analysis. The information available from the analysis is very useful in policy formulation and strategy planning.
2. INTERPRETIVE STRUCTURAL MODELING Identification of the structure within a system is of great value in dealing effectively with the system and for better decision making. Structural models may include interaction matrices and graphs (Warfield, 1973a), intent structures (Warfield, 1972), delta charts (Warfield, 1971), signal flow graphs, etc. However, these structural models lack interpretation of the embedded object or representation system. An interpretive structural model deals with the interpretation of an embedded object or representation system by systematic iterative application of graph theory, resulting in a directed graph for the complex system in a given contextual relationship among a set of elements. Sage (1977) defines interpretive structural modeling as a process that transforms unclear, poorly articulated mental models of systems into visible, well-defined models useful for many purposes. The mathematical foundations of the methodology of ISM are given in various works (Harary et al., 1965; Waller, 1980; Ochuchi et al., 1986). The philosophical basis for development of the ISM approach has been presented by Warfield (1973b), who also dealt with the conceptual and analytical details of
Classification of Program Plan Elements Using ISM
653
the ISM process (Warfield, 1974). Malone (1975) discussed the application of ISM in structuring personal values and focusing on barriers to investment in a central city. Hawthrone and Sage (1975) used ISM for higher-education program planning. Jedlicka and Meyer (1980) used ISM for cross-cultural purposes. Using the ISM methodology, Sage (1977) has presented a methodology for development of a hierarchy among elements for a given contextual relationship. However, the methodology does not provide information on the key variables and clear ranks of various subelements. Keeping the methodology suggested by Sage as a base for development of a hierarchy among the elements, a further step is suggested in this paper for development of a hierarchy among the subelements to overcome this problem. 3. M E T H O D O L O G Y FOR HIERARCHY D E V E L O P M E N T AND CLASSIFICATION OF SUBELEMENTS The proposed methodology (Fig. 1) has two parts: (i) hierarchy development and (ii) classification of the subelements. 3.1. Hierarchy Development
The program is divided into elements and each element is further subdivided into a number of subelements. The contextual relations of the subelements in each element are than determined. Based on the contextual relationship under consideration, the structural self-interaction matrix (SSIM) and reachability matrix are prepared. The reachability matrix as obtained from SSIM is checked for the transitivity role. If the transitivity role is found not to be satisfied, the SSIM is reviewed and modified by giving specific feedback about the transitive relationship to the expert. From the revised SSIM, the teachability matrix is again worked out and tested for the transitivity rule. The process is repeated till the teachability matrix meets the requirements of the transitivity role (Fig. 1). The reachability matrix is transformed into a lower triangular teachability matrix format for developing a digraph and interpretive structural model for the element. The reachability matrix is also subjected to level partition to determine the levels. The subelements are accordingly arranged at different levels and interconnected by eliminating the transitivity relationships. The ISM thus developed may have cycles at a particular level and feedbacks across the levels between subdements. In normal circumstances, the feedbacks and cycles should be eliminated to arrive at the ISM, based on the minimum edge digraph; but the same should be retained in the matrix if the intention is to study further the influence of the indirect relationship between subelements. Therefore, the above methodology is adopted deliberately with the intention to study the indirect relationship (Sax-
654
Saxena, Sushii, and Vrat The Program ]
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Fig. I. Methodologyfor preparing the interpretivestructural model and determiningthe hierarchy of prograraplan subeleraentsand their classifications.
ena et al., 1990c) as an extension of the study of direct relationships through ISM. The intention of this exercise is to obtain the most representative problem structure in view of the participants' understanding in terms of actual subelements rather than the inclusion of dummy subelements for generating a skeleton hierarchical digraph. The driver power of the subelements is derived from the arithmetical sum of the number of interactions in the row, and the dependence of the elements
Classification of Program Plan Elements Using ISM
655
is derived from the arithmetical sum of the number of interactions in the column of the reachability matrix. Based on the driver power and dependence, the rank of each subelement is determined. The driver-power ranks represent the hierarchy among the subelements. The subelement of the first rank is the key subelement and deserves maximum attention. 3.2. Classification of Subelements
The various subelements in an element are then depicted in the driver power-dependence matrix (Godet, 1985). For the purpose of classification of subelements, the driver power-dependence matrix is divided into four sectors. Sector I:
Weak driver and weak dependent variables (points near the origin); a group of so-called autonomous variables. These variables are the factors relatively disconnected from the system, with which they only have few links, though these links could be very strong. Sector II: Weak driver and strongly dependent variables. The variables are mainly dependent. Sector III: Strong driver and strongly dependent variables. These variables should be studied more carefully. These linkage variables are unstable. Any action on these variables will have an impact on others and a feedback effect on themselves to amplify or support the initial pulse. Sector IV: Strong driver and weak dependent variables. They condition the rest of the system and are called independent variables.
4. T H E CASE OF ENERGY CONSERVATION IN T H E INDIAN C E M E N T INDUSTRY Energy conservation in the Indian cement industry is an important program and has been divided into nine elements for the purpose of analysis. The nine elements are further subdivided into subelements; the number of subelements in each element has been kept as exhaustive as possible. The present study has considered 12 societal sectors, 9 needs, 12 constraints, 10 alterables, 12 objectives, 10 objective measures, 22 activities, 15 activity measures, and 10 agencies, thus totaling 112 subelements in the system (Saxena et al., 1989, 1990a, b). 4.1. The Contextual Relationships
For the purpose of interpretive structural modeiing, the contextual relations for each element considered are detailed in Table I.
Saxena, Sushil, and Vrat
656
Table I. Contextual Relations for Each Element Element Societal sectors Needs Constraints Alterables Objectives Objective measures Activity Activity measures Agencies
Contextual relationships One societal sector influences other societal sectors in energy conservation. One need assists other needs in energy conservation. One constraint contributes other constraints in high energy consumption. One alterable assists other alterables in energy conservation. One objective assists other objectives in energy conservation. One objective measure leads to other objective measures of energy conservation. One activity assists other activities in energy conservation. One activity measure leads to other activity measures inenergy conservation. One agency assists other agencies in energy conservation. .i
4.2. Development of the Hierarchy of Subelements Keeping the contextual relationships in view, the SSIM for each element has been developed using the symbols V, A, X, and O. Nine SSIMs were thus obtained. Reachability matrices are then developed for each element from the respective SSIM by substituting the values o f V, A, X, and O with 1 and 0 as the case may be. As an illustration, the SSIM for the objectives is presented in Fig. 2, and the reachability matrix in Fig. 3. On checking the rcachability matrix for the objectives, it is observed that the matrix is not transitively closed, e.g., e41 and e15 are present and e4s is not. Similarly, there are many other examples in the matrix which do not satisfy the transitivity rule. The reachability matrix is corrected for transitivity, and accordingly the SSIM is modified and sent to the expert, drawing his attention to specific modifications for his views. Once the expert is satisfied, the SSIM and teachability matrix are ready for further processing. The revised SSIM and reachability matrix for the objectives are presented in Figs. 4 and 5, respectively. The reachability matrix presented in Fig. 5 satisfies the transitivity rule and is ready for further use. The levels for subelements are determined by level partition on the reachability matrix. The levels for the subelements of the element "objectives" are determined in Tables IIa to g, indicating that there are seven levels in the ISM for objectives. The ISM and the driver power-dependence matrix for objectives are shown in Figs. 6 and 7, respectively. Ranks o f the driver power of the subelements of objectives (Fig. 5) indicate that Instrumentation and Computer Controls (11) has the first rank and is the key objective for energy conservation. The key subelements o f all the elements are presented in Table III. Note that while the relative picture of the hierarchy among the subelements o f objectives is obtained from the levels in the ISM (Fig. 6), the cycles and
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(b) Reachability and antecedent sets for P-L0-LI
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Table III. Key Subelements for Energy Conservation S1No.
Element Societal sector Needs Constraints Alterables
Objectives Objective measures Activities Activity measures Agencies
Key subetement(s) National Council for Cement and Building Materials (NCB) Instrumentation and Computer Controls i. Shortage of Funds ii. Absence of Suitable EC Laws i. Availability of Funds ii. Transport Bottlenecks iii. Government Regulations and Laws iv. Change in Technology v, Instrumentation and Automation i. To Develop Sound Energy Policy ii. To Train Manpower in Energy Conservation Nos. of Engineers and Managers in Energy Conservation Organize Workshops/Seminars Number of Workshops/Seminars Organized NCB
Classification of Program Plan Elements Using ISM
663
feedbacks in the ISM present a problem in identifying the key element and a clear hierarchy among the subelements. This difficulty has been overcome by determining the driver power, dependence, and ranks of the subelements.
4.3. Classification of Subelements
Considering the driver power and dependence of each subelement, driver power-dependence matrices are prepared and the subelements classified into four sectors for the nine elements. It is observed that some of the subelements fall on the line of separation of these sectors. The analyst has to use has judgment to classify these subelements in either of the sectors by considering various other factors. In Fig. 7, presenting the driver power-dependence matrix for objectives, it can be seen that objectives such as To Convert Wet to Dry Process (3), To Conserve Energy by Waste Management (5), To Have Adequate Supply of Coal (7), To Have Adequate Electricity (8), and To Install Captive Power Plants are the linkage variables in the system with strong driver power and strong dependence. Any action on these objectives will result in saving energy, and a lack of action on these objectives will lead to a continued waste of energy. Analysis further shows that the objectives To Develop Energy-Efficient System/Equipment (1), To Apply Computers/Instrumentation (4), To Develop Sound Energy Policy (10), and To Train Manpower in Energy Conservation (11) are independent variables with strong driver power and weak dependence. These are important for reducing the energy consumption, as they influence the system. Further, the variables To Utilize Energy Efficiently (2), To Improve Quality of Coal (6), and To Increase Capacity Utilization (12) are mainly dependent. Table IV shows the classification of various subelements in a given element into different categories related to each sector.
4.4. Findings
(i) The National Council for Cement and Building Materials (NCB), an R&D organization devoted to the cement industry, has been found to be the key societal sector. Classification of societal sectors indicates that NCB is an independent variable. Study shows, further, that the NCB influences other societal sectors such as cement units, electricity boards, coal companies, railways, the government, the Bureau of Indian Standards (BIS), etc. (ii) Instrumentation and Computer Controls has emerged as the key need. In fact, it assists other needs in energy conservation. The classification of subelements shows that Instrumentation and Computer Control is an independent variable.
2. Needs
1. Societal sectors
Element
--
Autonomous
-
-
--
Dependent
Linkage
(1) Adequate Supply of Coal (2) Policy on Import or Allocation of High Grade Coal (3) Adequate Supply of Electricity (4) Policy on Distribution and Pricing of Natural Gas (5) Conversion of Wet to Dry Process
Cement Plants Coal Companies Railways Electricity Boards Government Bureau of Indian Standards (9) Cement Machinery Manufacturers (CMM) (11) Cement Machinery Manufacturers' Association (12) Gas Authority of India
(1) (3) (4) (5) (6) (8)
Subelement
Table IV. Classification of Subelements
(7) Instrument and Computer Control
(2) National Council for Cement and Building Materials (7) Industrial Development Bank of India (10) Cement Manufacturers' Association
Independent
5. Objectives
4. Alterables
3. Constraints
Type of Energy Use Quality of Power Quality of Coal Introduction of EnergyEfficient Equipment
(2) To Utilize Energy Efficiently and Conservatively
(1) (2) (3) (4)
(9) Government Regulations and Policies (10) Poor Quality of Power (11) Poor Quality of Coal (12) Energy-Efficient Equipment
(3) To Convert Wet to Dry Process Plant (5) To Conserve Energy
(5) Availability of Funds (6) Availability of Trained Manpower (7) Transport Bottlenecks (8) Government Regulations and Laws (9) Change in Technology (10) Instrumentation and Automation
(5) Technological Constraint (Wet to Dry) (8) Nonexistence of Energy Monitoring System
(6) Development of Energy System/Equipment (8) Management of Energy Waste (9) Efficient Operation and Maintenance of Plants
(1) To Develop Energy Efficient System/ Equipment
(1) Limited Reserves of Coal/Natural Gas (2) Transport Bottlenecks (3) Lack of Instrumentation and Automation (4) Lack of Trained Manpower (6) Shortage of Funds (7) Absence of Suitable Energy Conservation Law
(1) Convert Wet to Dry Process
(4) Identify New Coal and Gas Reserves
7. Activity
(6) To Improve Quality Of Coal (12) To Increase Capacity Utilization
Dependent
(1) Reduction in Energy Conservation (2) Increase in Capacity Utilization (6) Quantity of Electricity Made Available (9) Capacity of Captive power
Autonomous
6. Objective measures
Element through Waste Management To Improve Quality of Coal To Have Adequate Electricity To Install Captive Power Plants Improvement in Quality of Coal
Linkage
(3) Install Instruments and Computers
(5)
(9)
(8)
(7)
Suhelement
Table IV. Continued
Independent (4) To Increase Computer Application and Instrumentation (10) To Develop Sound Energy Policy (l 1) To Create Trained Manpower in Energy Conservation (3) Number of EnergyEfficient Equipment/ System Developed (4) Increase in Quantity of Coal Received by the Industry (7) Extent of Computerization of Instrumentation (8) Extent of Waste Heat Utilization (10) No. of Engineers, Supervisors, Managers Trained in Energy Conservation (14) Create Soft Loan Facilities for Installing
i
(6) Create Efficient Transport Facilities for Coal and Gas (7) Encourage R&D for Improving Quality of Coal (22) Standardize Equipment for Energy-Efficient Performance
(2) Install Energy-Efficient System/Equipment (5) Monitor Quantity and Quality of Coal Received by Cement Industry (8) Encourage R&D for Development of Energy-Efficient Equipment/System (9) Monitor Quantity and Quality of Electricity to Cement Industry (10) Install Captive Power Plants (11) Introduce Improved Maintenance Techniques (12) Introduce Energy Monitoring System (13) Regular Energy Audit Study
(21)
(20)
(19)
(18)
(17)
(16)
(15)
Energy-Efficient Equipment Review Taxes and Duties on EnergyEfficient System/ Equipment Liberalize Import of Energy-Efficient System/Equipment Organize Training Programs for Energy Audit, Energy Conservation, and Energy Monitoring Organize Workshops/ Seminars on Energy Give Publicity Through Media for Energy Conservation Success Stories Create Motivation for Energy Conservation Efforts by Instituting Energy Awards Modify Licensing Policy for New Plants with Minimum EnergyEfficiency Standards
9. Agencies
Activity measures
Element
(1) Improvement in Coal Quality as Received by the Cement Industry (3) Improvement in Availability of Power (6) No. of Energy-Efficient Equipment Installed (9) R&D Projects Undertaken for Energy Conservation (13) Degree of Standardization of Energy Performance Achieved (14) No. of Energy Awards Given Away
Autonomous
(1) Cement Plants (8) Industrial Development Bank of India
(5) Specific Energy Consumption (Thermal, Electrical, Total) (15) No. of Plants Improving Energy Performance
Dependent --
Linkage
Electricity Boards Coal Companies Government Cement Machinery Manufacturers (7) Bureau of Indian Standards (9) Railways (10) Consultants
(2) (3) (4) (5)
Subelement
Table IV. Continued
(2) Improvement in Availability of Coal (4) Quality of Power Received by Industry (7) Loans Granted for Energy-Efficient Equipment (8) No. of Plants Covered by Energy Audit (10) No. of Persons Trained in Energy Audit (ll) Capacity of Captive Power Added (12) No. of Workshops and Seminars Organized
Independent
Classification of Program Plan Elements Using ISM
669
(iii) Shortage of Funds and Absence of Suitable Energy Conservation Laws have emerged as the key constraints and contribute to other constraints in higher energy consumption. The analysis shows that lack of funds and Absence of Suitable Energy Conservation Laws are independent variables among the constraints. (iv) Availability of Funds, Transport Bottlenecks, Government Regulations and Laws, Change in Technology, and Instrumentation and Automation have been found to be the key alterables. Increasing or decreasing the allocation of funds has an impact on other alterables in energy conservation. Similarly Government Regulations and Laws, Change in Technology, Instrumentation and Automation, Transport Bottlenecks, etc., have considerable influence on the expected specific energy consumption level. Classification shows that these variables are linkage variables. This finding is in consonance with the earlier finding that Shortage of Funds is the key constraint, and if tackled properly, the energy conservation could be achieved easily. (v) Study shows that To Develop Sound Energy Policy and Training of Manpower in Energy Conservation are the key objectives. These objectives serve all other objectives for energy conservation. Classification of subelements shows that To Develop Sound Energy Policy and Training of Manpower in Energy Conservation are independent variables. (vi) Study has brought out that the most important objective measure is Numbers of Engineers and Managers in Energy Conservation. This finding is in consonance with the key objectives identified, i.e., To Develop Sound Energy Policy and Training of Manpower in Energy Conservation. Classification of subelements shows that this subelement is an independent variable. (vii) Study highlights that there is a great need for creation of awareness of energy conservation, which is reflected by the fact that Organizing Workshops/Seminars has emerged as the key activity. This activity assists a large number of other activities in energy conservation. Classification of subelements shows that the activity Organizing Workshops/Seminars is an independent variable. The activity is closely related to the objective Training of Manpower in Energy Conservation. (viii) Number of Seminars Organized is the key activity measure and is in consonance with the finding that Organizing Workshops and Seminars has emerged as the key activity. Classification of subelements also shows that Number of Seminars Organized is an independent variable in the element. (ix) NCB has been found to be the key agency and assists all other agencies in energy conservation. Classification of subelements shows that NCB is an independent variable in the element. (x) Study shows that, of the 15 key subelements, 5 key subelements in Alterables are linkage variables and the remaining 10 key subelements are independent variables.
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5. DISCUSSION AND CONCLUSIONS Interpretive structural modeling for the elements identified and studied in program planning linkages is a step forward in system analysis. The methodology of deriving the driver-power ranks of the suhelements from the reachability matrix is an improvement in studying the hierarchy of subelements, yet still retaining aU the merits of theory of binary matrices and digraphs. The methodology of classification of the variables is a simple exercise for identifying the type of role each variable is playing in the program. The whole exercise presents a very clear picture of the structure and the relation of subelements in the program, thus enabling appreciation of the complexities of the program and evolution of correct policies and strategies. REFERENCES Godet, M. (I985). Scenarios and Strategic Management, Economic Press, Paris, pp. 44-45. Harary, F., Norman, R. Z., and Cartwright, D. (1965). Structural Model: An Introduction to the Theory o f Directed Graphs, Wiley, New York. Hawthrone, R. W., and Sage, A. P. (1975). On applications of interpretive structural modeling to higher education program planning. Socio. Econ. Plan. Sci. 9, 31-43. Hilt, J. D., and Warfield, J. N. (1972). Unified program planning. IEEE Trans. Syst. Man. Cybern. SMC-2(5), 610-621. Jedlicka, A., and Mayer, R. (1980). Interpretive structural modeling cross cultural uses. 1EEE Trans. Syst. Man. Cybern: SMC-10(1), 49-51. Malone, D. W. (1975). An introduction to the application of interpretive structural modeling. Proc. IEEE 63(3), 397-404. Ochuchi, A., Kurihara, M., and Kaji, I. (1986). Implication theory and algorithm for Teachability matrix model. IEEE Trans. Syst; Man. Cybern. SMC-16(4), 610-616. Sage, A. P. (1977). Interpretive structural modeling. In Methodology for Large Scale System, McGraw-Hill, New York, pp. 91-164. Saxena, J. P , Vrat, P., and Sushil (1989). Energy conservation in Indian cement industry--An application of program planning linkage approach. 9th MIAMI International Congress on Energy and Environment, 11-13 Dec. Saxena, J. P., Vrat, P., and Sushil (1990a). Linkages of key elements in fuzzy program planning. SysL Res. 7(3) 147-158. Saxena, J. P., Sushil, and Vrat, P. (I990b). Fuzzy interpretive structural modeling applied to energy conservation (submitted for publication). Saxena, J. P., Sushil, and Vrat, P. (1990c). Impact of indirect relationships in classification of variables--a MICMAC analysis for energy conservation. Syst. Res. 7(4), 245-253. Waller, R. 1. (1980). Contextual relations and mathematical relations in interpretive structural modeling. IEEE Trans. Syst. Man. Cybern. SMC-10(3), 143-145. Warfield, I. N. (t971). The DELTA ehart--a method for R&D project portrayal. IEEE Trans. Eng. Manage., 132-139 (Correction, May 1972, p. 74). Warfield, J. N. (1972). Intent structures. IEEE Trans. Syst. Man. Cybern. SMC-3(2), 133-140. Warfield, J. N. (t973a). Binary matrices in system modeling. IEEE Trans. Syst. Man. Cybern. SMC-3, 441-449. Warfield, J. N. (1973b). An assault on complexity. Battelle Monograph, 3, Battelle Memorial Institute, Columbus, Ohio. Warfield, J. N. (1974). Structuring complex systems, Battelle Monograph, 4, Battelle Memorial Institute, Columbus, Ohio.
