CIM Implementation Model

Copyright © IFAC Large Scale Systems. London. UK. 1995

CIE/CIM IMPLEMENT ATION MODEL Ljubisa Vlacic*, Takeaki Nakamura** and Yotaro Ogiwara**

* Faculty of Science and Technology. Griffith University. Brisbane. Qld. 4111. Australia ** The University of Electro-Communications. Tokyo 182. Japan

Abstract: This paper deals with multicriteria-based decision making model to be used to evaluate computer integrated enterprise/manufacturing implementation effects. Keywords: Computer-integrated enterprises manufacturing, multiple-criterion optimisation, economic implementation models.

The definition of the term Computer Integrated Enterprise (CIE) comprises the integration of all aspects of company operations including management related aspects by the computerised automation system. Such computerised automation systems operate as a decision support tool for the individuals carrying out the functions assigned to them (in the framework of corporate management, engineering design , marketing and sales, etc.)

1. INTRODUCTION

This paper is an interim report on our latest developments, and is organised as follows . Section 2 provides a discussion on a multicriteria-based model, developed to be used in CIE/CIM implementation-stage related decision making while Section 3 provides a discussion about the model sol ving methodology . Finally , in Section 4, conclusions are drawn. Before approaching the next section. it is important to point out that there are certain differences in the meaning of the terms CIM (Computer Integrated Manufacturing) and CIE (Computer Integrated Enterprise), especially from the viewpoint of industrial automation specialists . Though the analysis of these differences is not the concern of this research , a distinction will be established following those of the Purdue CIE Reference Architecture (for more details see Williams. 1989) as follows :

2. CIElCIM IMPLEMENTAnON DECISION MODEL . After the CIE/CIM system has been installed it enters into the operation stage of its life cycle. There are a number of published reports on analysis of effects of computerisation of industrial plants, prepared on the basis of experience acquired during early implementation of CIE/CIM in the workplace. However, all reports are a class of 'questionnaire-based' analysis.

The definition of the term Computer Integrated Manufacturing (CIM) comprises automation of the whole of the manufacturing functions of the enterprise. The computerised automation system (i.e., computer-based control system) in the framework of those functions. acts (process control, production scheduling, sequencing, etc.) directly on the plant equipment to accomplish the needed task.

Here, we present the multi criteria-based decision model which consists of 20 criteria structured across two hierarchical levels with a decision model solving methodology, founded on an aggregation procedure for hierarchically grouped decision criteria. This model has already been successfully used in conjunction with a model solving methodology for the screening of survey results of CIE implementation across six Japanese

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Training Requirement. This property refers to the time span required to train all persons affected by automated manufacturing technology to permit the efficient function of the CIE system.

variation in size. weight and finish for products or services as well as to maintaining the resulting goods or services within these limits.

Motivation. This term refers to the willingness to achieve a goal in CIElCIM environment. Namely. it is often the case that workers lose their job willingness due to automated manufacturing technology. Therefore this criterion has been taken to express the effectiveness of workers as their workplace becomes more computerised.

OrganisationaL Impact Decision Making . Organisational decision making is the response to a need or stimulus by means of acquiring. organising and processing information in order to recognise alternative courses of action. and then selecting one among them. Efficiency of Integration. This property refers to an efficiency in communications across all departments of the enterprise. Accuracy of information is another important component of this property.

OperationaL Impact Delivery ScheduLe Performance. This is the required or agreed delivery time. i.e. the rate of delivery of goods or services purchased for a future period.

OrganisationaL Structure. Organisational structure refers to the framework within which the totality of human interactions is subdivided and the job duties. personal relations and lines of authority are specified.

Productivity. Increasing productivity is clearly an important outcome of CIE and the term productivity refers to quality or state of being productive. When using the term in a manufacturing context it means the measured output of goods from a productive facility relative to some standard. norm. or potential maximum. When introducing CIE. productivity is increased because of the time that can be saved and the errors that can be reduced . Production is also increased when the principles of human-computer interaction are effectively implemented.

3. A DECISION MODEL SOLVING METIlODOLOOY It is a known fact in decision theory that models often have hierarchical structure and that the criteria of a lower level must be grouped and aggregated into a criterion of the upper level. The theoretical foundations of such aggregation require rather complicated testing of various independent assumptions. hence most applied examples of such aggregation are based on heuristics. We apply here an aggregation procedure for hierarchically grouped decision making criteria developed by Vlacic et al.. (1986).

Inventory. Inventory often refers to items which are in stock or work in progress and which also serve to decouple successive operations in the process of manufacturing a product and distributing it to the final consumer. Inventories may consist of finished goods ready for sale; or they may be work in progress; or they may even be raw materials. Maintainability . Maintainability refers to all actions necessary for retaining an item in or restoring it to a specified condition and the term maintainability must be considered in overall system design and plant proportions. The operator who supplies or reprograms an automated piece of equipment should be able to access all necessary parts for regular operation in the space which should allow him to bring service equipment and supplies on site for routine and emergency maintenance.

In our case. all performance measures are measurable. and consequently. can be accepted as decision making criteria. As can be seen from Table 1. the upper level criteria express composite evaluations of lower level criteria. For example. the criterion 'Economic impact' expresses the composite evaluation of lower level criteria. namely profitability. operational risk and net present value. The important part of our model is that most of the criteria regardless of the hierarchical level they belong to. can only be subjectively assessed. while very few of them can be analytically modelled and thus expressed quantitatively.

FLexibility. Production line flexibility refers to its capability: • •

to produce different products from those which are currently being produced. and to be rescheduled and adapted to suit new commercial conditions (e.g. changes in quantities of products ordered).

However. applied decision model solving methodology can assist us in aggregating the lower level criteria (which are of either a qualitative or quantitative nature) into the composite upper level criteria which can further assist in aggregating all subjective assessments of upper level criteria into an overall assessment.

Quality ControL. Quality control refers to the procedure of establishing acceptable limits of 33

The core of the multi criteria decision making algorithm is a choice of a utility or value function of a decision maker or of a team of them. There are many known forms of utility or value functions, see for details Keeney, et al. (1982). A utility or value function which is consistent with engineering applications was proposed by Wierzbicki (1982) as a so called order-consistent achievement function that is used to scalarize achievements on multiple objectives and depends parametric ally on contextual information on the attainable ranges of values for these objectives as well as on the placement of so called reference levels - which might be interpreted as aspiration or reservation levels, or both - in these ranges. This contextual information is usually available and important in engineering applications. Let

Y = (Yp ... Yi' ... Yp)

Wierzbicki (1982), but it is noted there that many different definitions of single-dimensional achievement components can be used to define variants of an order-consistent achievement function, as long as these components are continuous, strictly monotone and have value zero at

Thus, the achievement function u(y,y) is the minimum of single-dimensional achievement components corrected by their average, which represents a trade-off between the worst underachievement and the average achievements; the scaling by 1/(1 + c) is included only to make

u(y,y) = Ui(YPYi)

{y: y(y,y) ~ O} = Y + D, where D is the positive cone (orthant) in the space of

vectors of

Ui(Yi'Yi)' only on its monotonicity, its zero-value at Yi and on the general form of u(y, y). If c > 0, then u(y, y) is strictly order-preserving (strictly monotone) and {y: y(y,y) ~ O} = Y + Dc where

that achievement function as suggested by Wierzbicki in (Wierzbicki, 1982) might have the form:

Dc is a cone containing D but "slightly broader", Thus, the achievement function is orderapproximating, see Wierzbicki (1982). In the same paper, the theoretical implications of these properties are specified as follows:

U(y,y) (1)

•

where c is a weighting coefficient,

0$

C

< p, •

components which can be defined by

Ui(Yi'Yi) =

u(y,y)

with respect to

Y

If Y is in Yo and is a weakly Pareto-optimal point of this set, then the maximum of u(y,y) with c = 0 is attained at Y = Y and

is equal to zero; the same applies if y is a properly Pareto-optimal point with a given bound on trade-off coefficients and we use u(y,y) with c> 0 selected to represent this bound.

(Yi - Yi)/(Yi,mar - Yi) 'f Yi - -< Yi -< Yi.mar

each maximum of

in a set Yo of attainable objective vectors is a Pareto-optimal point of this set (weakly Pareto-optimal, if C = 0, properly Paretooptimal if c > 0),

and ui (y i Si) are single-dimensional achievement

1

p components. This property obviously

does not depend on a particular form of

Y = (Y/, ... yp ... Yp) such Yi.min < Yi < Yi.mar. Then an order-consistent

Ui(Yi'Yi)+

Ui(Yi,yi) are equal,

continuous and

or aspiration levels

/SiSp

if all

Note that if c = 0 then the achievement function u(y, y) is order-representing, that is, it is

denote the vector of

values corresponding to chosen p objectives or criteria that all should be maximised (if some or all should be minimised, we then can change the signs accordingly). Suppose the ranges Yi.min < Yi,lnax of attainable or relevant objective values are given for each i. Suppose, moreover, that a decision maker specifies a vector of reference

={min

Yi = Yi'

(2)

The above implications can be used, for example, for checking the attainability and Pareto-optimality of a given y (we give here the alternative cases Note that this function is continuous even at

Yi

=Yi

when using

=

where its value is ui (Yi' Yi ) O. In fact, this definition is slightly different than used in 34

C

= 0)

Vlacic, Lj . (1989). Decision Support Systems in the Design of Process Control Systems. Information and Decision Technologies, 15, pp. 179-191. Wierzbicki, A.P. (1982). A Mathematical Basis for Satisficing Decision Making. Mathematical Modelling, Vol. 3, pp. 391-405. Wierzbicki, A.P. (1992). Multiple Criteria Games; Theories and Applications. IIASA Laxenburg WP-92-079. Williams, TJ. (ed.) (1989). A Reference Model for Computer Integrated Manufacturing (c/M): A Description from the Viewpoint of Industrial Automation, Instrument Society of America Williams, T.J. (1991). The Purdue Enterprise Reference Architecture. Research Report J54, Purdue University.

Vlacic, Lj and Matic, B. (1986). Evaluation of Perfonnances of Process Control Systems and the Choice of Use-Oriented Process Control System Based on the Concept of Quasisatisficing Decision Making . In Simulation on control systems (Troch, I, Kopacek, P and Breitenecker, F (eds», Pergamon Press, pp 251-256. Vlacic, Lj et al. (1986). Aggregation Procedures for Hierarchically Grouped Attributes with Application to Control Systems Perfonnance Evaluation. In Lecture Notes in Economics and Mathematical Systems Recent Advances and Historical Development of Vector Optimization (Jahn, J and Krabs J, (eds» Springer-Verlag, Heidleberg, pp 285-311. Vlacic, Lj. and T.J. WilIiams (1990) . The Application of Multiple Criteria Decision Making to Computer Integrated Enterprise Systems. IXth International MCDM Conference, University of Virginia, Fairfax, USA. Vlacic, Lj., and S. Nof (1994). Real-Time Multiobjective Co-operation Control of MultiMachine Workstations. In Proceedings of IFAC Workshop on Intelligent Manufacturing Systems, June, 13-15, Vienna. Vlacic, Lj., B. Matic and A.P. Wierzbicki (1986). Aggregation Procedures for Hierarchically Grouped Decision Attributes with Application to Control System Perfonnance Evaluation. In Recent Advances and Historical Development of Vector Optimisation (Jahn and Krabs, (eds» Springer-Verlag, pp. 285-311.

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