esp: knowledge-based expert strategic planner

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Expert Systems With Applications, Vol. 4, pp. 99-115, 1992 0957--4174/92 $5.00 + .00 Printed in the USA. © 1991 Pergamon Press pie ESP: Knowledge-Based Expert Strategic Planner S. ARUNKUMAR AND N. JANAKIRAM* Indian Institute of Technology, Bombay, Powai, Bombay-400 076, India and *Century Rayon, P B No. 22, Shahad-421 103, Maharashtra, India Abstract--This article deals with the process of setting strategic objectives and related policies for a business organization. Knowledge of environmental and organizational strengths and weaknesses is of vital importance in a strategic planning system. Expert Strategic Planner (ESP) addresses these issues during the strategic planning process. The detailed function and role of ESP are explained along with its proposed architecture. A chunk-based organization for knowledge and a message- passing locomotion for navigation are proposed, lnferencing is done through generation offigures of merit from knowledge and learning. A few illustrations are included. An application for ESP is also discussed. ESP, a stand-alone knowledge-based, strategic planning system, is ideally suitedfor determining the strategic objectives in the Intelligent System for Productivity Management (ISPM) proposed by the authors. 1. INTRODUCTION AN ORGANIZATION interacts with the external envi- ronment comprising government and competing and collaborating organizations such as vendors, markets, and society. Its constituent subsystems such as pro- duction, marketing, management, and finance con- tribute ideally in a synergistic manner. The efficacy of the organization depends on how well it overcomes weaknesses and harnesses strengths in counteracting threats and exploiting opportunities provided by the environment. The design of a strategic planning system includes the function of environmental scanning and situational analysis (Lorange & Vancil, 1981). Man- agement needs to set objectives and evolve a strategic master plan with adequate cognizance of internal and external conditions. Objectives and plans set without adequate exposure to, and analysis of, internal and ex- ternal conditions are difficult to achieve as contrasted with clear and well-thought out objectives that can greatly increase the effectiveness and efficiency of the business (Granger, 1964). An organization may set for itself a number of ob- jectives such as Return on Investment (ROI), Return on Net Worth (RONW), Quality Image, Market Lead- This work was done as part of the Intelligent Systems Project at the Indian Institute of Technology, Bombay, India. Requests for reprints should be sent to S. Arunkumar, Department of Computer Science & Engineering and the Interdisciplinary Pro- grammes in Industrial Management and Biomedical Engineering, Indian Institute of Technology, Bombay, Powai, Bombay-400 076, India. 99 ership, Profitability, and Offering of goods and services to customers at affordable prices. Other subobjectives as well as social objectives may also be included. The chief executive of an organization along with concerned divisional managers sets the corporate (strategic) ob- jectives (such as ROI, sales, growth rate) and this in turn is transmitted to the divisions. The targets set would have to be verified as to their implementation with respect to operational aspects. This article describes our work on the knowledge- based system ESP for strategic planning that helps evolve implementable plans based on selected corpo- rate objectives. The paper is organized as follows: Section 2 deals with the role of the strategic planner and the need for knowledge in strategic planning. Section 3 presents an overview of ESP, its modules, architecture, and func- tions. Section 4 deals with knowledge and its organi- zational, navigational, and inferencing aspects. Sections 5, 6, and 7 deal with the remaining modules of ESP, that is, data manager, model base, and goal setter, re- spectively. Section 8 discusses an application for ESP in a continuous processing chemical industry manu- facturing rayon yarn. Section 9 discusses the feedback and the top-down-bottom-up balance between tactical and operational planning and strategic planning. This section also discusses how ESP resets strategic objec- tives midstream, based on the feedback. Section l0 brings out the advantages of the knowledge-based sys- tem for strategic planning. Section l 1 defines the role of ESP in productivity management, especially in the intelligent system proposed by the authors. Section 12 concludes the article.

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Page 1: ESP: Knowledge-based expert strategic planner

Expert Systems With Applications, Vol. 4, pp. 99-115, 1992 0957--4174/92 $5.00 + .00 Printed in the USA. © 1991 Pergamon Press pie

ESP: Knowledge-Based Expert Strategic Planner

S. ARUNKUMAR AND N. JANAKIRAM*

Indian Institute of Technology, Bombay, Powai, Bombay-400 076, India and *Century Rayon, P B No. 22, Shahad-421 103, Maharashtra, India

Abstract--This article deals with the process of setting strategic objectives and related policies for a business organization. Knowledge of environmental and organizational strengths and weaknesses is of vital importance in a strategic planning system. Expert Strategic Planner (ESP) addresses these issues during the strategic planning process. The detailed function and role of ESP are explained along with its proposed architecture. A chunk-based organization for knowledge and a message- passing locomotion for navigation are proposed, lnferencing is done through generation of figures of merit from knowledge and learning. A few illustrations are included.

An application for ESP is also discussed. ESP, a stand-alone knowledge-based, strategic planning system, is ideally suited for determining the strategic objectives in the Intelligent System for Productivity Management (ISPM) proposed by the authors.

1. INTRODUCTION

AN ORGANIZATION interacts with the external envi- ronment comprising government and competing and collaborating organizations such as vendors, markets, and society. Its constituent subsystems such as pro- duction, marketing, management, and finance con- tribute ideally in a synergistic manner. The efficacy of the organization depends on how well it overcomes weaknesses and harnesses strengths in counteracting threats and exploiting opportunities provided by the environment. The design of a strategic planning system includes the function of environmental scanning and situational analysis (Lorange & Vancil, 1981). Man- agement needs to set objectives and evolve a strategic master plan with adequate cognizance of internal and external conditions. Objectives and plans set without adequate exposure to, and analysis of, internal and ex- ternal conditions are difficult to achieve as contrasted with clear and well-thought out objectives that can greatly increase the effectiveness and efficiency of the business (Granger, 1964).

An organization may set for itself a number of ob- jectives such as Return on Investment (ROI), Return on Net Worth (RONW), Quality Image, Market Lead-

This work was done as part of the Intelligent Systems Project at the Indian Institute of Technology, Bombay, India.

Requests for reprints should be sent to S. Arunkumar, Department of Computer Science & Engineering and the Interdisciplinary Pro- grammes in Industrial Management and Biomedical Engineering, Indian Institute of Technology, Bombay, Powai, Bombay-400 076, India.

99

ership, Profitability, and Offering of goods and services to customers at affordable prices. Other subobjectives as well as social objectives may also be included. The chief executive of an organization along with concerned divisional managers sets the corporate (strategic) ob- jectives (such as ROI, sales, growth rate) and this in turn is transmitted to the divisions. The targets set would have to be verified as to their implementation with respect to operational aspects.

This article describes our work on the knowledge- based system ESP for strategic planning that helps evolve implementable plans based on selected corpo- rate objectives.

The paper is organized as follows: Section 2 deals with the role of the strategic planner and the need for knowledge in strategic planning. Section 3 presents an overview of ESP, its modules, architecture, and func- tions. Section 4 deals with knowledge and its organi- zational, navigational, and inferencing aspects. Sections 5, 6, and 7 deal with the remaining modules of ESP, that is, data manager, model base, and goal setter, re- spectively. Section 8 discusses an application for ESP in a continuous processing chemical industry manu- facturing rayon yarn. Section 9 discusses the feedback and the top-down-bottom-up balance between tactical and operational planning and strategic planning. This section also discusses how ESP resets strategic objec- tives midstream, based on the feedback. Section l0 brings out the advantages of the knowledge-based sys- tem for strategic planning. Section l 1 defines the role of ESP in productivity management, especially in the intelligent system proposed by the authors. Section 12 concludes the article.

Page 2: ESP: Knowledge-based expert strategic planner

100 S. Arunkumar and N. Janakiram

2. PLANNING AND KNOWLEDGE

Strategic management is the formulation and imple- mentation of plans relating to matters of vital, pervasive and continuous importance to the total organization (Anthony, 1965; Hufnagel, 1987; Thietart, 1988). Gallo (1988) presents a top-down approach for strategic planning consisting of five steps: defining goals and objectives, identifying constraints, generating alterna- tives, selecting an ideal alternative, and implementing the solution. It starts from a high level and provides an organized way for evaluating information and mak- ing decisions. Sharplin (1985) proposes a strategic management process model consisting of two phases: (l) strategy formulation (determination of organiza- tional mission, assessment of organization and its en- vironment, setting of specific objectives or direction, and determination of strategies for accomplishing these objectives) and (2) strategy implementation (activation of strategies, ex-post evaluation, and control). He mentions that phase 1 is often called strategic planning. Lorange and Vancil (1977) mention five pillars for success of a formal planning process: use of such pro- cess as support to formulating strategic choices; un- derstanding of the process at all organizational levels; minimum level of standardization of contents, meth- ods, formats, and dead lines of the system; its integra- tion with other systems such as control or information systems; and involvement of line managers in the pro- cess. Mockler (1989) looks at the strategy development process as consisting of seven segments: (l) industry

analysis, (2) identifying opportunities and keys to suc- cess, (3) comparative competitive position evaluation, and (4) to (7) developing various levels of strategies and strategic plans. He suggests structured situation analysis and reformulation or reconceptualization of the situation as essential steps in developing a knowl- edge-based strategic planning system. He advocates use of situation diagrams, decision charts, and a scenario- development technique for identification of knowledge requirements to facilitate decision making.

Planning systems, as they become more sophisti- cated, look further into the future, laying greater em- phasis on qualitative objectives, the firm's internal ca- pabilities and on the effects of social, political, and technological trends (Rhyne, 1985). Knowledge about external environment as well as of the organizational strengths and weaknesses is of essence in strategic planning. This knowledge is massive, complex, and dynamic in nature. Strategic planning must be inte- grated with tactical and operational aspects linking the objectives across these three levels. The suitability of the capital productivity measure ROI as a strategic ob- jective has been dealt with in (Arunkumar & Jana- kiram, 1989). ROI can be exploded down to the shop floor operational targets (productivity indices) (see Figure 1), establishing linkages between productivity indices and the (corporate) strategic objective (refer to Eilon, Gold, & Soesan, 1976). Productivity is inter- twined with profitability; the necessity for knowledge of the enterprise and environment for proper manage- ment of productivity is brought out, for example, by

I R & D COSTS SALES -t-

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. . . I O P E R A T I O N A L U N I T S / D E P A R T M E N T S

Note

PI. P2. P3. F~ AND P5 ARE PARTIAL PRODUCTIVITY INDICES WITH RESPECT TO MATERIAL,LABOUR.FACTORY OVERHEADS, OTHER COSTS AND WORKS SUBCONTRACT EXPENSES RESPECTIVELY.

FIGURE 1. Productivity tree.

Page 3: ESP: Knowledge-based expert strategic planner

ESP: Knowledge-Based Expert Strategic Planner 101

Bailey (1978), Ganguly (1987), Government of Japan (1966), Nishikawa, Sazanami, and Yamada (1969), and Sahu (1986). The ROI objective has been chosen in this article, although any other measure may also be used in ESP.

3. OVERVIEW

ESP fixes medium- and long-term corporate objectives such as:

1. Return on Investment (ROI), or Return on Capital Employed, or Return on Net Worth

2. Gross Profit, or Net Profit, or Profit After Tax, or Earnings Before Interest and Taxes (EBIT). Other targets such as market share, growth, and sales

turnover are also determined along with these objec- tives, some of which are used at the tactical level. ESP

takes stock of the external environment and internal conditions on which productivity depends before set- ting appropriate strategic, tactical, and operational productivity targets. ESP confiders the following as- pects before arriving at a figure for the ROI objective: past performance of the organization, trends, compet- itors' performance, current activities, and industry trends by using the strategic model base; organizational strengths/weaknesses, environmental threats/oppor- tunities, emerging trends in technology/management, and the decision maker's experience through the knowledge base (KB).

Therefore, the structure of ESP is as follows. It has two submodules, Goal Setter (GS) and Data Manager (SDM), supported by a Model Base (SMB) and a Knowledge Base (SKB) (see Figure 2 for architecture). SKB comprises the expertise of recognized manage- ment professionals as well as knowledge acquired from

EXTERNAL DATA

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FIGURE 2. ESP architecture.

Page 4: ESP: Knowledge-based expert strategic planner

102 S. Arunkumar and N. Janakiram

good corporate examples. The KB is divided into mul- tifarious aspects relating to functional areas and exter- nal environment. SMB includes forecasting models, financial models, and business games. SDM is an in- terface to the Master Data Base (MDB) of the orga- nization. The strategic planning process in the context of ESP is presented in Figure 3.

The inferencing in ESP is made via GS. The pro- ductivity DB contains relevant data of productivity measures and indices of the organization, competition, and the industry. Suitable models are present in the MB, such as those based on forecasting. The KB con-

tains knowledge pertinent to internal and external en- vironments and incorporates the expertise of top and divisional management.

GS starts with a trigger value (say, the previous year's industry average) for the organizational objective ROI. Navigation through the strategic KB refines this value for the chosen objective by adding or deleting certain Figures of Merit (FOMs) to or from the trigger value depending on the combination of factors in the KB. In a multidivisional company, the objective is fixed for each division and the ROI objective for the organiza- tion inferred subsequently. Objectives under various

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I PRODUCTIVITY DATA BASE I

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FURTHER DATA/KNOWLEDGE I ~)ATA/KNOWLEDGE PROCESSIN~ PROCESSING REQUIRED ] I ADEQUATE /

I DETERMINE ROI OBJECT]VE FOR DIVISION

FEED BACK FROM TOPP

FIGURE 3. Flowchart for strategic planning.

Page 5: ESP: Knowledge-based expert strategic planner

ESP: Knowledge-Based Expert Strategic Planner 103

alternative scenarios (of assumptions and data) are ex- amined before fixing their values.

4. KNOWLEDGE ORGANIZATION

Aspects concerning functions such as Marketing and Organization have a dominant effect on the operations of a firm (see Dean in Turban & Watkins, 1988) and therefore the performance objectives need to be deter- mined in light of these factors, since the result of rea- soning and inferencing is context dependent (Bobrow & Winograd, 1977).

ESP requires knowledge that does not necessarily fall in the framework of rule-based systems. Much of strategic planning is beuristic and judgmental in nature with knowledge not being as structured as in the form of production rules. This calls for nonformal represen- tations such as images and memories based on expe- rience, without making explicit the strict rules and their conditions (Dreyfus, 1981). The concept of influence diagrams to construct knowledge maps capturing di- verse information has been dealt with recently by Howard (1989) and may be used in the process of knowledge engineering.

The KB must be able to express completely, pre- cisely and with clarity, even in the "imperfect domain" of concern to ESP. Complex knowledge, uncertainty, and change have to be expressed as, for example, tech- nology evolves over time. Concepts, inheritance, and assertions must be expressible. The KB must have a capacity for breadth, density, and uniformity (Hayes, 1985) so that inferential connection can be seen be- tween different parts. It is desirable to prove consis- tency. It may be difficult to prove statements as false and we need to have a mechanism for handling of par- adox (Minsky, 1981). The KB is layered, from overall domain to specific agents and states. It is unrealistic to consider unguided invocation and selective application of knowledge sources is important for effective infer- ence. For a comprehensive review in knowledge rep- resentation, see (Brachman & Levesque, 1985). The following points need to be borne for proper knowledge organization: I. The KB organization needs to allow for significant

imprecision especially at the level of ESP. 2. It should allow for relative information to compare

across industries and companies. 3. Uncertainty is a major component and this must

be allowed. 4. Temporal relations, for example, global and gov-

ernmental relations, although not exactly known, may be allowed through constraints.

5. The grains of knowledge for reasoning may be ap- plicable from years to seconds and these need to be modified and updated (Alien, 1983).

6. For efficiency, default reasoning needs to be allowed.

7. Time requirement may be at a particular point or over an interval.

8. Planning begins with suggestive and imperfect im- ages and progressively improves, although still im- perfect.

9. The relations have "strength" connotation and these need to reflect in inferencing, which would be bi- directional. The declarative nature of the KB has called for a

hierarchical chunked organization (Bobrow & Wino- grad, 1977; Sowa, 1984). Chunk is a collection or clus- ter of knowledge units in which typical properties are assumed true of an object unless more specific infor- mation is immediately available and identification of the object is based on recognizing some set of salient properties; for example, each of the function groups such as Marketing, Organization, and so on, mentioned earlier, will constitute a chunk. The Chunks are or- ganized into Macros, which in turn are decomposed into Micros, which are further decomposed into Mol- ecules and Atoms (see Figure 4).

4.1. Knowledge Base

The typical knowledge chunks deal with Marketing, Organization, Technical, Research & Development, Environment, Finance, Industrial Policy, and Import & Export Policy. For example, the Industry chunk has macros on raw material, scope for modernization, market potential, yarn processing sector, etc. The Mar- keting chunk (Figure 4) has Product, Competition, Publicity as Macros, containing knowledge under the respective heads. In a typical industrial setting, there is interaction between the chunks to produce a syn- ergistic effect, rather than each chunk being exercised in isolation. Keeping this in view, certain macros and micros have been provided with inter- and intrachunk communication capability. Macros/Micros at level 3 have inter- and intrachunk, at level 2 only inter- chunk, at level 1 only intrachunk, and at level 0 no communication capabilities (see Figure 4). Atoms and molecules have neither inter- nor intrachunk com- munication capability. Their communication is only hierarchical, which produces synergistic effect at the higher-level node. Macros/micros can be a subset of other macros/micros of same level type. A micro, for example, can be represented as a frame and, if it pos- sesses subsets, would be a frame of frames. Interchunk communication is always at peer level. Lower-level macros/micros do not have a communication facility (access) that their ancestors do not possess. In this hi- erarchical organization, the highest level is the Chunk and at the lowest or the leaf level is the Atom. This organization yields a network of nodes and semantic links. Macros and micros are generally referred to as nodes. A supernode is one to which all chunks are con- nected.

Page 6: ESP: Knowledge-based expert strategic planner

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Page 7: ESP: Knowledge-based expert strategic planner

ESP: Knowledge-Based Expert Strategic Planner 105

' M A R K E T I N

FIGURE 5. Marketing chunk.

Other salient aspects of chunk organization are: 1. Atom and chunk are absolute; in between, there are

several levels. 2. Macro/micro can be a subset of another macro/

micro of same or higher level. 3. Lower-level macro/micro does not have a facility

that the higher level does not have. Higher-level macro/micro dictates inter and intra flows.

4. Atoms and molecules are not allowed to interact with atoms and molecules of other macros either within the same chunk or ofanother chunk in order to reduce the problem complexity.

5. All communication except hierarchical is peer level; inter and intra peer level communication is selec- tive.

6. Atoms and molecules within a micro produce a synergistic effect at the next higher-level hierarchical knowledge unit.

7. A micro, for example, can be represented as a frame and, if it possesses subsets, would be a frame of frames. The knowledge organization for two representative

chunks, Marketing and Organization are given in Fig- ures 5 and 6, respectively. The knowledge (partly in- dustry dependent and partly industry independent) has a significant impact on the working and performance of the enterprise.

4.2. Navigation through the KB

A tagged data-flow approach is taken for inferencing with reasoning dominated by matching. Units of mes- sage contain FOM and features. The consultation is divided into the following steps: 1. Node and link activation 2. Multipass locomotion, which includes inter- and

intrachunk communication that is carried out until the FOMs generated at successive passes are sufli-

ciently close or terminated by the discretion of the decision maker A message has an identity. It carries related orga-

nizational features and their values obtained by (partial) inference and nodes visited. Locomotion is initiated at the base micronodes of the knowledge graph; the mes- sages originate at the base and travel upward hierar- chically and laterally toward a peer node in the same or another chunk. At the bottom of the graph, the intra relationships are more predominant and as messages travel upward, more interchunk relationships come into play.

Consider, for example, an intrachunk hierarchical navigation (Figure 7). Message Mi from lit node i moves upward to its ancestor ai. If there are other descendent lit nodes di,], di,2, di,3 . . . . . di,,,ti) as well as lateral con- nections (node li), the i th nodal activation is a function of the intensities at all, i, d~,2, di,3 . . . . . di,nti), and li for consideration of features and nodal intensity. Note that SKB is validated for nonexistence of cycles. If for ex- ample, node di,6 is not lit (inactive), there is no con- tribution to activation from d,,6. The navigation illus- trated is of hierarchical type, which also holds well for a lateral type. Each node maintains a list of its active neighbours. Once a message unit leaves a node, then the node is crossed out, placed in the inactive list, and is not eligible for transceiving additional messages in this pass.

Activation is done by the relevancy of the nodes and links with respect to the consultation, which is described by a set of generic features (e.g., character- istics of the organization). The nodes and links related to these generic features are activated by generation of a __Nodal Intensity Factor (N-IF). If the NIF is above a threshold value, the node is lit up. Lighting up at a node is the process of generation of an NIF based on the aggregate collection of features currently available at the node and the descendent and related peer NIFs. The extent of lighting up is an increasing function of

Page 8: ESP: Knowledge-based expert strategic planner

106 S. Arunkumar and N. Janakiram

( P R O D U C T

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FIGURE 5a. Product macro.

the intensity factor and measured over a grey scale, using the notion of a distance function, for example, the Levinshteinian distance or the notion of semantic distance depending on the number of links to be tra- versed between two nodes. The extent of lighting up depends on the NIF, which is a function of the distance between the actual value of the nodal feature and its

template value, which may be considered as the ideal or target.

d {train (value), template} --*

Nodal Intensity Factor (NIF) (lighting up)

Min d (i.e., d = 0) --* maximum lighting up.

Page 9: ESP: Knowledge-based expert strategic planner

ESP: Knowledge-Based Expert Strategic Planner 107

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FIGURE 5b. Marketing skills macro.

Links have certain weights, and the strength of a link is taken into consideration in the lighting-up pro- cess. A node with NIF below a threshold is shut out and rendered inactive.

The locomotion of the message is in the nature of simultaneously spreading activation (Quillian, 1967) by way of replicated messages to eligible adjacent nodes. The output features are given qualitative or quantitative points as the messages move through the nodes. Note that additional features with values are typically added

in the course of consultation. The locomotion is co- ordinated so that the messages along different paths converging at a node are amalgamated prior to sub- sequent locomotion. A multilevel locomotion is pro- posed for intra- and interchunk inferencing in each pass.

The initial FOM chosen for ROI may be the industry average, which is refined during successive inference cycles by generating adjustment factors. The NIF of a node is determined based on those of the dependent

Page 10: ESP: Knowledge-based expert strategic planner

108 S. Arunkumar and N. Janakiram

O R G A N I Z A T I O N D

FIGURE 6. Organization chunk.

nodes and the adjustment factors on the messages re- ceived; additional features also may be loaded on the message units. The messages are sent upward to the node's own ancestor as well as laterally to peers, thus replicating identical copies of messages to all connected nodes.

The FOM generated at the supernode is a function of the chunk-intensity factors. The iteration ends when all the messages terminate at the supernode, none being in transit in the net; the supernode amalgamates the information including the characteristics that yield such measures for FOM as growth factor. Fresh messages are then generated and the procedure is continued or else terminated if no appreciable change in the FOM is noticed, ending the consultation process.

4.3. Refinements

We outline below a few refinements: I. The messages can be specialized to chunks and need

not be identical.

2. Preprocessing, leading to KB compression, may be carried out before the beginning of consultation to activate only a subset of nodes and links for the entire consultation based on consultation and nodal features.

3. The process of generation of NIFs may use learning based on inputs from experts, as well as from pre- vious consultations.

5. STRATEGIC DATA MANAGER (SDM)

SDM accesses MDB and obtains the data necessary for setting corporate objectives, typical data required being Sales, EBIT, Total Assets Employed, Output, Capacity, Fixed Investment, and the like. From this data, infor- mation such as profit percentage, assets turnover, ROI, and capacity utilization is compiled, which can be viewed in various formats convenient to management. Comparison can be made with similar information pertaining to industry, competition as well as in respect of environmental, technological, and other factors.

i . . . . N o d e u n d e r c o n s i d e r a t i o n

. . . . A n c e s t o r N o d e to N o d e ' i '

li . . . . . N o d e l a t e r a l to N o d e ' i '

di. I , d i . 2 , di03 . . . . . di, n ( i ) ~ D e s c e n d e n t N o d e s of N o d e ' i '

FIGURE 7. Chunk schematic.

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ESP: Knowledge-Based Expert Strategic Planner 109

MDB contains such data as sales, operating ratios, costs, and other details of the firm as well as compe- tition. Any external data required and not available in MDB will be input to SDM directly.

6. STRATEGIC MODEL BASE (SMB)

SMB evolves relatively long- and medium-term strat- egies for the organization to maximize appropriately chosen financial measures. SMB design includes in- terface for forecasting, macromodeling of the organi- zation, its constraints as well as objectives and the op- timization thereof, risk analysis, decision analysis, and pertinent financial modeling systems (Brooke & Duffy, 1986; Mclnnes & Carleton, 1982), corporate planning models (Grinyer & Wooller, 1975; Hayes & Nolan, 1974; Naylor & Schauland, 1976) and other related models.

7. GOAL SETFER (GS)

This is the control driver of ESP as well as the inference engine for SKB. GS obtains the projected target for the firm using SKB based on industry, firm and compe- tition averages for ROI, prevailing bank rate, and the best estimate of corporate team and divisional man- agers, from SDM as well as forecasts for industry, firm, and competition from SMB.

8. APPLICATION OF ESP

The application (Arunkumar & Janakiram, 1991) deals with a multistage, continuous-processing industry pro- ducing rayon filament yarn from wood pulp. It is a multimillion rupee industry, and a typical 30 tons/day semiautomated plant employs several thousand people in India. The impact of gains in productivity on such a scale of operations would be considerable. The ma- terial and energy (power and steam) costs being sub- stantial, any gains in productivity will have a favorable impact on competitiveness. A major quantity of key raw materials, like wood pulp and sulphur, are im- ported. Import regulations change from time to time, leading to uncertainties in raw material availability. The process being complex, deep knowledge of oper- ating and process conditions is essential to maintain good quality. Few experts are available in this industry. Being a continuous process industry using a variety of chemicals, plant and machinery are subject to exten- sive corrosion, resulting in high maintenance costs.

The final product, rayon yarn, is classified into dif- ferent grades before it is marketed. Although cost for each product is the same, realization depends on the grade. Rayon market is volatile where prices keep fluc- tuating and competition is keen.

8.1. Rayon Process

The viscose rayon process (Moncrieff, 1975) consists of steeping pulp sheets in caustic soda, shredding the soaked sheets into small pieces; churning the small pieces in carbon disulphide; dissolving the xanthate so formed in dilute caustic soda; blending the viscose formed from various dissolvers; ripening, filtering, and deaerating the viscose; spinning the viscose through a coagulating bath (spinbath) through a spinnerette and collecting the yarn in the form of cakes and washing them in the after-treatment department; and drying and coning the yarn into cones, which are packed in corrugated boxes for dispatch to markets. Rayon yarn is produced in about 10 deniers from 40 D to 600 D. Denier (D) is the weight in grams of 9000 m of yarn; the higher the denier, the coarser the yarn (low deniers mean fine yarns). In each denier, there are a minimum of five qualities. The yarn is also produced in different colors and shades. Taking combinations of color, de- nier, and quality into consideration, nearly 300 prod- ucts are produced.

8.2. Role of ESP

The planning function is based on a knowledge of the environmental factors as well as organizational strengths and weaknesses. It fixes up both the long- range and short-range organizational objectives, duly making logical assumptions where necessary. For in- stance, one long-term objective may be ROI along with, say, yearly sales and growth targets (Arunkumar & Janakiram, 1989). These can be further disaggregated into periodic tactical targets (such as monthly sales for each product, territory) and further into operational targets in terms of productivity indices at unit/depart- ment level, which constitute the short-term objectives.

ESP performs planning operations by setting initial goals and attempts to bring out the detailed imple- mentation, which is evaluated for its resultant perfor- mance. Replanning may be necessary until acceptable levels are achieved. This procedure also yields control parameters.

8.3. Illustrative Consultations

A prototypical implementation of ESP was made in Common Lisp environment on a CDC Cyber 180/840 mainframe computer running NOS/VE operating sys- tem. A few typical consultations in the strategic plan- ning process are illustrated briefly. For the sake of brevity and illustration, in each case, details are given for navigation through a subset of SKB. Let us consider the Marketing chunk shown in Figure 5. It may be seen that the Marketing chunk has nine macros: Prod- uct, Marketing Skills, Customer Profile, Competition,

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110

Service, Exports, Demand, Publicity, and Government. The Product macro, for instance, has six micros: Fea- tures, Reputation, Quality, Service, Price Sensitivity, and Product Differentiation. During the KB compres- sion, the preprocessor may light up three micros: Fea- tures, Reputation, and Quality, loving the other three micros inactive. In this Product macro (Figure 5a) therefore, out of possible six messages, only three be- longing to the lit-up micros become active.

For this consultation, the data matching of the Fea- tures, Reputation, and Quality micros at the atom level is as follows:

Features Micro. (i) Availability with competitors : Not Available

(ii) Multiutility : Yes (iii) Technology: modern/obsolete : Modern (iv) Combination : Unique (v) Future oriented : Yes

Reputation Micro. (i) Market share--high/low : High

(ii) Trademark--national/world famous : World fa- mous

(iii) Product image--poor/good : Good

Quality Micro. (i) First customer choice : Yes

(ii) Enjoys price premium : Yes (iii) Threat to competition : Yes (iv) ISI mark : Yes (v) Quality of design : Good

(vi) Quality of performance : Good (vii) Mean Time Between Failures (MTBF) : Long

The Product macro has, therefore, the following schema (Table l) (for sake of brevity and clarity, the schema and currency are given together):

TABLE 1 Schema of "Product" Macro

{ {Macro: Product Member of Chunk: Marketing

Has: Micros Currency Connectivity

Product features Unique Intra Monopoly,

Reputation Excellent

Quality Leader

Service

Price sensitivity

Product differentiation

oligopoly Inter Intra Keen Inter Intra Keen Inter Intra Service backup,

policy Inter Intra Protection,

consumer Inter Intra Monopoly,

unlimited Inter

S. Arunkumar and N. Janakiram

Likewise, schema of Marketing Skills macro (Table 2) (Figure 5b) appears as follows:

TABLE 2 Schema of "Marketing Skills" Macro

{ { Macro: Marketing Skills Member of Chunk: Marketing

Has: Micros Currency Connectivity

Professional Highly Intra Infrastructure Inter

Type Aggressive Intra Limited, growing Inter

Market Thorough Intra Limited research Inter

Outlets Established Intra Infrastructure Inter

Proceeding thus, the schema of the Marketing chunk (Figure 5) appears as shown in Table 3, after the rel- evant activations, locomotion, and inference. The In- dustry chunk has the schema shown in Table 4 for the specific application (rayon industry) taken up for ESP (the mechanism of obtaining the values, having been previously illustrated in case of the Marketing chunk, is not shown). The Organization chunk (Figure 6) has the currency of the macros, shown in Table 5.

The initial ROI is selected from past industry av- erage for this particular application. In this case, the consultation was initiated with an ROI value of 12%. A high FOM was generated, with the industry profile being "good," product and market "excellent," and management "highly competent," in spite of "poor" industrial relations.

The consultation terminated at an ROI value of 16%. Translating ROI to market share yields a sales target of 18,500 tons of rayon yarn per annum (all deniers and colors included). The detailed case study is given in Arunkumar and Janakiram ( 1991).

9. FEEDBACK FROM TACTICAL AND OPERATIONAL PLANNING

The implementability of the strategic objectives de- pends on the feasibility of tactical and operational tar- gets to be set in consonance. The corporate objectives like sales and growth rate set by ESP, are broken down into productwise, modelwise tactical targets and further down into shop floor or operational unit-level produc- tivity targets within the overall framework of produc- tion capacity and other constraints. After the tactical planning exercise is completed, the expected ROI for the division is then computed to check if it agrees with the strategic target set for ROI. If there is no agreement, the tactical planning exercise is repeated, with varying parameters and assumptions, until the tactical targets set enable achievement of strategic objectives.

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ESP." Knowledge-Based Expert Strategic Planner

TABLE 3 Schema of "Marketing" Chunk

111

{ {Chunk: Marketing

Has: Macros Currency Connectivity

Product Excellent Intra Inter

Marketing skills Highly competent Intra Inter

Service backup Excellent Intra Inter

Demand Good Intra Inter

Publicity Well covered Intra Inter

Export possibility Good potential Intra Inter

Competition Oligopolistic Intra Inter

Government Helpful Intra Inter

Customer profile Consumer product Intra Inter

Competition, demand Image of organization chunk Product, competition Management, people of organization chunk Product, demand Image, people of organization chunk Product, competition Image, industrial relations of organization chunk Customer profile, demand Image of organization chunk Government Image of organization chunk Marketing skills, product, demand Image, industrial relations of organization chunk Export possibility Management of organization chunk Price sensitivity Management of organization chunk} }

This top-down/bottom-up balance is necessary since the former lays down what is required to be achieved and the latter brings out what is achievable; manage- ment at both levels must agree on divisional goals for them to be realistic and achievable (Bernier, King, Maalouf, & Long, 1986; Lorange & Vancil, 1981). Several iterations are done until the expected ROI matches the target. If a conflict still exists and the stra-

tegic objective is found to be too high or low and not practical, then the strategic planning exercise is repeated by the corporate team in light of feedback received from tactical planning. This process would culminate either in appropriate revision of the strategic objectives or in the corporate team convincing divisional man- agement that the strategic objectives originally set for the division are reasonable and achievable.

TABLE 4 Schema of "Industry" (Rayon) Chunk

{ {Chunk: Industry (Rayon)

Has: Macros Currency Connectivity

Raw material availability Good Intra Market potential Inter Demand, competition of marketing chunk

Scope for modemization Excellent Intra Market potential, export scenario, processing Inter Technology of technical chunk

Market potential Very good Intra Raw material availability, scope for modemization,

Inter Export scenario Bright Intra

Inter Quality/price interface Encouraging Intra

Inter Govemment policy Complementary Intra

Inter Capacity utilization Broad banded Intra

Inter Spinning sector Optimum utilization Intra

Inter Processing sector Keen competition Intra

Inter Weaving sector Growth oriented Intra

Inter

quality-price interface, capacity utilization Demand, competition of marketing chunk Scope for modemization, quality-price interface,

government policy Export policy of marketing chunk Market potential, export scenario Product of marketing chunk Export scenario, spinning sector, weaving sector Government, demand of marketing chunk Market potential Demand of marketing chunk Government policy Demand, export possibility of marketing chunk Scope for modernization Demand of marketing chunk Government policy Demand of marketing chunk

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112 S. Arunkumar and N. Janakiram

TABLE 5 Schema of "Organization" Chunk

{ {Chunk: Organization

Has: Macros Currency Connectivity

Management Average Intra Inter

People Average Intra Inter

Industrial relations Poor Intra Inter

Image Poor Intra Inter

Government Intra Inter

Image, government Marketing skills, customer profile of marketing chunk Image Marketing skills, service backup of marketing chunk People, government Demand, competition of marketing chunk Management, people Demand, product, service backup, publicity, competition, export

possibility of marketing chunk Industrial relations, management

In this application, at the end of the first round of tactical planning exercise, the expected ROI was com- puted at 13% and overall sales target at 11,000 tons. Since these are lower than the strategic objectives laid in Section 8.3, the tactical planning exercise is repeated, by interactively adjusting sales quantities and prices for the denier mix at four marketing centers and by increasing sales targets at two other centers. After three such iterations, the expected ROI improved to 16% and overall sales to 18,500 tons, thus meeting the stra- tegic targets. The overall sales figures are then seg- mented into figures for individual deniers by periods.

The result of this exercise will be setting of the ROI corporate objective and related tactical targets such as sales, production, machine acquisition, gross raw ma- terial input, broad policies on subcontracting, budgets for such expenses as marketing, advertising, and prod- uct promotion, obtained by exercising of the tactical models, by iterations between strategic planning and tactical and operational planning.

Once the environmental analysis (industry com- parison, market requirements, technical and compet- itive trends) and analysis of internal resources are un- derstood, a continual process of reiteration (adjustment of one objective in light of another and in light of new developments in resources and environmental condi- tions) takes place (Granger, 1964).

9.1. Resetting Strategic Objectives in Midstream

Constant monitoring and evaluation brings out aber- rations in performance vis-fi-vis targets, when system- atic analysis is made to check if there are drastic changes in the environment and/or in planning premises, in which case the strategic planning process is repeated in order to reset the strategic objectives.

10. ADVANTAGES

The knowledge-based corporate goal-setting process has the following important characteristics:

1. It is participative and interactive, practical, and re- alistic.

2. KB is dynamic, making midstream corrections pos- sible, thus upholding the navigational principle of planning.

3. It is fact based and scientific. Because of these characteristics, the confidence in

the system increases and people would put forward their best to achieve the objectives since they feel the objectives are realistic and achievable.

11. ROLE OF ESP IN PRODUCTIVITY MANAGEMENT

The profitability of an enterprise depends on the pro- ductivity levels achieved. Productivity is best promoted by linking productivity indices with organizational ob- jectives set after proper environmental scanning and industry structure analysis. In line with this approach, the authors proposed an Intelligent System for Pro- ductivity Management (ISPM) comprising two sub- systems--one for planning and the other for diagnosis and control of productivity performance (see Figure 8). The planning subsystem ESTOPP deals with stra- tegic, tactical, and operational planning. ESP can be used with advantage for undertaking the strategic planning task in the ISPM framework (see Figure 9). The tactical and operational planning tasks will be car- ried out by another module Tactical and Operational Planner for Productivity (TOPP). The strategic, tactical and operational targets set are stored in a Productivity Database (PDB) for further use in monitoring, evalu- ation, and improvement of productivity. These targets are updated over time as necessary. PDB also includes available productivity information of competition and industry. Although ESP in the ISPM framework, has interrelationships with other modules of the system, the design of ESP facilitates its use as a stand-alone system.

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ESP." Knowledge-Based Expert Strategic Planner 113

PROOUCTIVITY EVALUATOR

\

/I

~TT PERT RATEGIC PLANNER

&

~ m t i o l ~ l ~'¢~ ~ PLANNER FOR

~. .'~ ~ . ~ ..

,/. .;

,~ONTROLLER of~ " ~ UPERATIONAL PROOUCTIVITY

"--- & • ~. m / w

• ~ ~ - /

.\ // \ ~ c . ~ /

MONITOR "

\

\ . /

FIGURE 8. Productivity management process-enWy relationship diagram.

12. CONCLUSIONS

ESP has the capability for scanning the environment and analyzing internal strengths and weaknesses of the enterprise so vital for strategic planning. Because ESP is knowledge based, it brings refinement to the strategic planning process and is a powerful tool in the hands of the corporate planner• ESP can help in productivity planning, that is, fixing organizational objectives in the ISPM framework. Thus, although it is a stand-alone

system on its own, ESP can effectively function as a module of the productivity management system, ISPM.

ESP was used to determine strategic objectives for Rayon plant, a continuous processing chemical indus- try producing rayon yarn. Its performance is expected to improve substantially when the built-in learning mechanism becomes operational. In particular, the conditions leading to favorable performance can be captured by machine-learning techniques (Arunkumar & Yegneshwar, 1990) and incorporated in the KB.

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114 S. Arunkumar and N. Janakiram

r---GJg T

I PRODUCTIVITY ENGlNE 1 1

ESTOPP r ______ _ ______ ____ ____ ____ J 7

i I El2 COP I I

FIGURE 9. ESP in ISPM framework.

Further work is planned on this with more extensive testing of ESP individually and as a subsystem of ISPM.

Acknowledgements-The authors wish to thank Mr. Durgeshchandra, Joint Resident, Century Rayon for his encouragement. This research is part ofthe Intelligent Systems Project from the Ministry of Human Resources Development, Government of India, at IIT, Bombay. The authors wish to thank the editor-in-chief of ESWA and the reviewers for their constructive suggestions.

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