a classification schema of manufacturing decisions for the grai enterprise modelling technique

A classification schema of manufacturing decisionsfor the GRAI enterprise modelling technique

Ian McCarthya,*, Michalis Menicoub

aOrganisational Systems and Strategy Unit, Warwick Manufacturing Group, University of Warwick,

IMC Building, Coventry CV4 7AL, UKbManufacturing Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street,

Sheffield, S1 3JD, UK

Received 15 March 2000; accepted 27 October 2001

Abstract

The Graphs with Results and Actions Inter-related (GRAI) methodology and its development the GRAI Integrated

Methodology (GIM) are established enterprise modelling (EM) techniques for representing the decision architecture of

manufacturing systems. However, they lack the support of certain modelling constructs, which in turn can lead to inconsistent

and inadequate results. To help address this issue, this paper proposes a classification schema of manufacturing decisions that

will facilitate the identification and analysis of decisions for constructing GRAI models. The classification schema is based on:

(i) a continuum of organisational decision characteristics; (ii) a categorisation of manufacturing decision domains (DDs) and

(iii) a list of manufacturing system configurations. By using this schema during the initial analysis phase and the model

construction phase, it is possible to improve the process of identifying key manufacturing decisions and the associated processes,

activities and entities. # 2002 Elsevier Science B.V. All rights reserved.

Keywords: Enterprise modelling; GRAI; Domain ontology; Classification schema; Manufacturing decisions

1. Introduction

1.1. Enterprise modelling

Studying complex entities such as organisations

and their sub-systems requires a modular approach

where knowledge from various organisational modules

(perspective) is collected and studied synergistically.

Unfortunately, due to the inherent complexity that

exists in organisations, enterprise modelling (EM)

techniques have evolved to a position where the

tendency is to study specific modules of an organisa-

tion at any one time, i.e. the functional architecture, or

the information flows, or the decision architecture, etc.

Thus, in order to acquire a holistic view of an orga-

nisation, the concurrent use of multiple EM techniques

should be encouraged.

EM studies are not carried out in isolation. They

need the support of modelling constructs to: (i) define

adequately the enterprise domain to be modelled; (ii)

govern and facilitate the modelling exercise for robust

results and (iii) use a common modelling language or

modelling formalisms to adequately describe the find-

ings and serve as a communication tool between

analysts. Aranguren et al. [1] provide an overview

of the tasks involved in the process of EM and

Computers in Industry 47 (2002) 339–355

* Corresponding author. Tel.: þ44-24-76-573-136;

fax: þ44-24-7652-4307.

E-mail address: [email protected] (I. McCarthy).

0166-3615/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved.

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recommend that the following modelling constructs

underpin a competent and comprehensive EM tech-

nique.

� Enterprise domain ontologies: The term ‘‘ontol-

ogy’’ refers to that part of metaphysics that deals

with the nature of being (‘‘on’’ is derived from the

Greek for ‘‘being’’). Aranguren et al. [1] state that

an ontology is a conceptual reference system that

can be represented as a set of: (i) enterprise con-

ceptual templates; (ii) associated glossary and (iii)

classification schemata.

� Libraries of reference models: Models serve as

industry benchmarks, indicating the established

‘‘best practice’’ in the field.

� Knowledge representation formalisms: Formalisms

are used to map the acquired knowledge with

respect to a particular organisational module.

� Organisational metrics: Within the context of EM,

organisational metrics are the basis of a measure-

ment system that could be used to evaluate the

performance of a particular organisational config-

uration with respect to the organisational module

under investigation.

� An appropriate methodology (or migration path):

The overall process of EM is governed by the use of

a methodology that consists of a set of clearly

defined steps (migration path).

1.2. The GRAI EM technique

The organisational module and the EM technique

that are the focus of this paper, are the decision

architecture of a manufacturing system and the Graphs

with Results and Actions Inter-related (GRAI) tech-

nique [2]. Work on GRAI began in the 1970s at the

GRAI Laboratory, University of Bordeaux. The objec-

tives were to model a production management system

to define the specifications needed to select a software

technology for computer-aided production manage-

ment (CAPM) applications. With the subsequent

developments in computer-integrated manufacturing

(CIM) technology, the GRAI methodology was

extended to focus on the design of the entire manu-

facturing system. This was the start of the GRAI

Integrated Methodology (GIM) [3]. To understand

and review GRAI as a method, this paper uses the

modelling constructs proposed by Aranguren et al. [1].

� A domain ontology: The GRAI ontology is struc-

tured as a set of templates to illustrate how the

decision architecture of a manufacturing system

should be conceptualised as a system of decision

centres. The GRAI technique defines a decision

centre as a group of activities and decision activities

(DAs) that produce an output. The GRAI ontology

consists of a macrostructure template for concep-

tualising the global structure of the decision archi-

tecture and a microstructure template for

conceptualising the architecture of decision centres.

The templates are based on a number of theorems

such as: (i) the theory of hierarchical multi-level

systems [4]; (ii) Simon’s theory on complex sys-

tems [5] and (iii) the Walrasian production model

[3]. It is important to note that the glossary intro-

duced and employed by GRAI is relatively limited

and the associated classification schema is non-

existent.

� Knowledge representation formalisms: For each

conceptual template an associated knowledge

representation formalism was developed. For the

macrostructure, these are called GRAI grids and

they aim to represent the overall architecture of the

decision centres and associated information flows

within a manufacturing system. GRAI nets are then

used to depict the various activities that exist within

each discrete decision centre (microstructure).

� An associated methodology: GRAI models are con-

structed using an approach that consists of: (i) an

analysis phase where the current system is realised;

(ii) a detection of inconsistencies phase where

potential areas of improvement are identified and

(iii) a design phase where a new configuration for

the system is proposed [6].

� Libraries of reference models: Roboam et al. [7] and

Chodari et al. [8] created reference models to help

detect inconsistencies during the analysis phase and

to facilitate the design phase. The objective of these

models was to provide industry benchmarks of good

practice that could reduce the time to redesign

systems. They are not intended to be a substitute

for human design skills, but rather to enhance and

facilitate the process.

With this review, this paper proposes a classification

schema of manufacturing decisions to enhance the

domain ontology of the GRAI EM technique. This

340 I. McCarthy, M. Menicou / Computers in Industry 47 (2002) 339–355

classification schema creates an arrangement of deci-

sions into categories to achieve the following objec-

tives.

� Identify the various types of organisational deci-

sions and describe their inherent characteristics.

� Identify the various DAs that exist within manu-

facturing functions and produce a glossary of defi-

nitions for each DA identified.

� Arrange discrete decision processes (DPs) and

activities into particular decision domains (DDs)

according to the CIM Open System Architecture

(CIMOSA) conceptual template and terminology

[9].

� Review the diversity of manufacturing functions

and identify the main configurations of manufactur-

ing organisations.

2. A classification schema of manufacturingdecisions

The classification schema proposed in this paper,

aims to provide a reference guide for understanding

the diversity of manufacturing decisions. This should

facilitate the identification and analysis of GRAI

decision centres and lead to the creation of compre-

hensive and robust GRAI models. This classification

schema has three axes.

� Axis 1: A continuum of organisational decision

characteristics.

� Axis 2: A categorisation of manufacturing DDs.

� Axis 3: A list of manufacturing system configura-

tions.

It is important to recognise that classifying is a simple

and habitual process that is valuable for storing and

communicating knowledge. It is a cognitive activity

that underpins learning. In fact, much of the knowl-

edge that has been created across all academic dis-

ciplines has been achieved by differentiating between

the similar and dissimilar. Thus, the reasons for con-

structing a formal classification of manufacturing

systems as part of an EM methodology are as follows

[10].

� For mental clarification and communication of

known manufacturing configurations and their

defining characteristics.

� For formally approving and validating the emer-

gence of new manufacturing configurations.

� For developing technological strategies, change

initiatives, organisational structures and operational

procedures that match the requirements of each

manufacturing configuration.

2.1. Axis 1: a continuum of organisational decision

characteristics

The GRAI method focuses on the decision perspec-

tive; but what is a decision? A decision can be thought

of as an action selected among alternatives, i.e. it

attempts to shape the future. It is an activity that

processes information to achieve an objective, whilst

being constrained by factors such as risk, limited

information, time and uncertainty. Tannenbaum [11]

states that etymologically, the verb, ‘decide’ is derived

from the Latin prefix ‘de-’ meaning ‘off’ and the word

‘caedo’ meaning ‘to cut.’ Therefore, decision-making

is a ‘‘cognitive process that cuts off a preferred option,

or elects a particular course of action among a set of

possible alternatives.’’ The process of tackling and

resolving choice-making situations is the essence of

decision-making.

Shull et. al. [12] state that ‘‘in spite of the fact that

various types of hardware may be employed to aid the

decision-making process, decision acts are human

events.’’ Therefore, even though technology may

enhance the speed, capacity, and accuracy of evalua-

tions, humans must design and program this technol-

ogy and an appropriate human must evaluate the

computational outputs. Within the context of organi-

sational decisions, Shull et al. [12] view the decision-

making process as a conscious activity involving the

totality of a human’s mind and not merely as an

automatic behavioural response. This view of deci-

sion-making includes the following.

� Cognition: Those activities of the mind associated

with the perception and conception of knowledge.

� Conation: Those activities of the mind associated

with characteristics such as endeavour, willing,

desire, and aversion.

� Affectation: Those activities of the mind associated

with emotion, feeling, mood, and temperament.

With this account of DAs, it is evident that the domain

of organisational decisions is vast. Such decisions

I. McCarthy, M. Menicou / Computers in Industry 47 (2002) 339–355 341

range from important to trivial and can have long-term

and short-term validity. They can also be found exclu-

sively in specific functions and configurations of

organisations. To help understand this diversity of

decisions and to increase the effectiveness of decision

analysis techniques, various ways of classifying deci-

sions have been developed. Within the context of this

paper, two classifications have been adopted to under-

stand the different types of decision that exist and their

defining characteristics.

� Time of functional validity/level of importance

(strategic, tactical and operational).

� Programmed and non-programmed (categories I

and II decision)

2.1.1. Time of functional validity/level of importance

Traditionally managerial decisions have been clas-

sified using strategic, tactical and operational criteria

to indicate the time of their functional validity and

their level of importance. Quinn [13] describes stra-

tegic and tactical decisions as follows.

Strategic decisions are those that determine the

overall direction of an enterprise and its ulti-

mate viability in light of the predictable, the

unpredictable and the unknowable changes that

may occur in its most surrounding environments

(p. 5).

Tactics are the short-duration, adaptive, action–

interaction realignments, which opposing forces

use to accomplish limited goals after their initial

contact. Strategy defines a continuing basis for

ordering these adaptations towards more broadly

conceived purposes (p. 6).

In the context of this paper, operational decisions are

regarded as those that are concerned with the daily

operation of an organisation and are responsible for

the realisation of strategies and tactics. They have the

shortest time horizon and often take place on a real-

time basis. This classification of decisions implies that

the level of importance and time validity of strategic

decisions is greater than that of tactical decisions, and

likewise tactical decisions are of greater importance

than that of operational decisions. However, despite

the general validity of this view, it is not correct

in absolute terms. Fig. 1 illustrates the blurred

boundaries between operational, tactical and strategic

decisions.

2.1.2. Programmed/non-programmed decisions,

and categories I and II decision

This second classification of decisions and their

characteristics is derived from a study of the behaviour

and operations of organisations by Gibson et al. [14]

who adopted the decision classification system intro-

duced by Simon [5]. This classification distinguishes

between two types of decisions: programmed and non-

programmed (see Table 1).

Programmed decisions: If a particular situation

occurs regularly, a routine procedure can usually be

worked out for solving it. Thus, decisions are pro-

grammed to the extent that problems are repetitive and

routine and a definite procedure has been developed

for handling them.

Non-programmed decisions: Decisions are non-pro-

grammed when they are novel and unstructured. No

established procedure exists for handling the problem,

either because it has not arisen in exactly the same

manner before, or because it is complex or extremely

important. Such problems deserve special treatment.

Gibson et al. [14] (p. 434). A similar and supporting

classification was presented by Harrison [15]. It is

Fig. 1. Axis 1: the continuum of organisational decisions.


Table 1

A summary of the classifications of decisions developed by Harrison [15] and Simon [5] (in [14])

[15] (p. 14) [5] (in [14], p. 434)

Category I decisions Routine, recurring decisions that are handled with a high

degree of certainty

Programmed

decisions

Specific procedures developed for repetitive and

routine problems

Classifications Programmable, routine, generic, computational, negotiated,

and compromise

Problem Frequent, repetitive, routine; much certainty regarding

cause and effect relationships

Structure Proceduralised, predictable, certainty regarding cause/effect

relationships, recurring, within existing technologies,

well-defined information channels, definite decision criteria,

outcome preferences may be certain or uncertain

Procedure Dependence on policies, rules, and definite procedures

Strategy Reliance upon rules and principles, habitual reactions,

prefabricated response, uniform processing,

computational techniques, accepted methods of handling.

Category II decisions Non-routine, non-recurring decisions characterised by

considerable uncertainty as to the outcome

Non-programmed

decisions

Decisions required by unique and complex management

problems

Classifications Non-programmable, unique, judgmental, creative, adaptive,

innovative, and inspirational

Problem Novel, unstructured. Much uncertainty regarding cause

and effect relationships

Structure Novel, unstructured, consequential, elusive, and complex,

uncertain cause/effect relationships, non-recurring,

information channels undefined, incomplete knowledge,

decision criteria may be unknown, outcome preferences

may be certain or uncertain

Procedure Necessity for creativity, intuition, tolerance for

ambiguity, creative problem solving

Strategy Reliance on judgement, intuition, and creativity,

individual processing, heuristic problem-solving techniques,

rules of thumb, general problem solving processes

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based on the work of various authors, including

[16,17,18,19]. Harrison divided decisions into two

categories, namely, categories I and II (see Table 1),

based on three characteristics: (i) routine and non-

routine; (ii) recurring and non-recurring and (iii)

certainty and uncertainty. Decisions under relative

certainty and under moderate risk belong to category

I, whereas decisions with relatively high risk and

uncertainty belong to category II.

Using the functional validity/level of importance

and the categories I and II characteristics the first axis

(Axis 1) of the proposed classification schema is

created (Fig. 2). The lower end of this axis indicates

decisions with minimal time validity, low levels of

importance and uncertainty. The upper end of Axis 1

indicates decisions with considerable uncertainty and

high-levels of importance and time validity. It is

important to note that this understanding of decisions

is a relative concept. For instance, although opera-

tional decisions appear next to category I decisions in

Fig. 1, this does not mean they take place with absolute

certainty and no risk. On the contrary, operational

decisions are still decisions and, thus, are human

events that will have inherent, but varying levels of

uncertainty, risk and programmability. The next two

axes of the classification schema (Fig. 2) are described

in the following sections.

2.2. Axis 2: a categorisation of manufacturing

decisions domains

CIMOSA is a guide to how an enterprise should be

described [9,20,21,22,23]. CIMOSA considers enter-

prise domains to be comprised of processes, which in

turn are comprised of either other sub-processes or

activities. Based on this approach and the decision

perspective that has been adopted by this paper,

organisations can be represented using the following

factors.

� DDs: This is any subset of an organisation that has

the necessary interest and importance to warrant a

study, i.e. operations, quality, technology, etc. It will

consist of a number of DPs, DAs and decision

entities (DEs).

� DPs: These receive inputs and produce an output

that is value of to the company and its’ customers.

DPs connect to other DPs and consist of DAs that

enable the process to achieve an overall objective

or task.

� DAs: These are the basic decisions performed in an

organisation. They also receive inputs, produce

outputs and require allocation of time and resource

for the full duration of their execution. As per the

GRAI decision centres, they attempt to shape the

future of the organisation by selecting options

according to business objectives and under given

information and restrictions.

� DEs: These are physical or human objects that exist

within the organisation and are responsible for

undertaking a DA.

Fig. 3a and b illustrate the relationship between DDs,

DPs, and DAs. This shows how a system consists of

multiple layers of control and is consistent with the

view of physical systems discussed by Doumeingts

et al. [24]. Fig. 3a shows a DD DD1 consisting of four

DPs, namely, DP11, DP12, DP13 and DP14. The arrows

represent the flow and co-ordination of information.

To represent these DPs as a GRAI grid they need to be

decomposed into their constituting DAs. Therefore,

focusing on DP11 this DP consists of two further DPs

(DP111 and DP112) and a DA (DA111). The two DPs are

decomposed into their constituting DAs (DP111 is

decomposed into DA112 and DA113; and DP112 is

decomposed into DA114 and DA115). Finally, Fig. 3b

shows how DP11 is comprised of five linked DAs

(DA111; DA112; DA113; DA114 and DA115), each of

which will be appear on the GRAI grid. Fig. 3a and b

illustrate the theory of hierarchical multi-level sys-

tems. This theory states that decision layers share the

following common features ([4], p. 54).

� A high-level layer is concerned with a larger and

broader aspect of the overall system behaviour.

� The decision period of a high-level layer is greater

than that of lower units.

� A high-level layer is concerned with the slower

aspects of the overall system behaviour.

� Descriptions and problems at high-levels are less

structured, more uncertain and more difficult to

formalise quantitatively.

The long-term competitiveness of any manufacturing

organisation is strongly related to the ability of dis-

crete DDs to satisfy market needs and expectations

[25,26]. Many researchers have created lists of these


Fig. 2. A schematic overview of the classification schema of manufacturing decisions.

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strategic DDs (decision areas in their terminology) to

provide managers with an organising framework for

discrete manufacturing decisions (see [25–29]).

Despite minor variations, there is general agreement

on what constitutes the major DDs in manufacturing

and for the purposes of this paper the comprehensive

list proposed by Hayes et al. [28] is employed. This list

consists of 10 DDs: (1) facilities; (2) capacity; (3)

technology; (4) vertical integration; (5) quality; (6)

organisation; (7) workforce; (8) new product devel-

opment; (9) performance measurement systems; (10)

production planning and control.

Using this list and the CIMOSA conceptual tem-

plate, a categorisation of manufacturing decisions and

an accompanying glossary can be produced to assign

discrete manufacturing DAs to particular manufactur-

ing DDs (see Fig. 4). This figure illustrates Axis 2, in

detail, from the overall classification schema shown in

Fig. 2, using the production planning and control.

domain from the case study described in Section 3 of

this paper.

2.3. Axis 3: a list of manufacturing system

configurations

The defining function of a manufacturing organisa-

tion is the ability to convert an idea or need into a

marketable product. This revolves around a series of

transformation processes that take inputs (materials,

labour and energy) and convert them into products

using operations such as machining, forming, shaping

and assembly. Manufacturing organisations achieve

this using a variety of techniques, routines and

resources. The result is a diversity of manufacturing

configurations.

The term manufacturing configuration refers to the

make up of a manufacturing organisation, its form and

defining characteristics in terms of strategy, capabil-

ities and routines. Different configurations exist

because there are competitive and innovative forces

that govern manufacturing variety and, thus, give rise

to these configurations, see [30–36]. From this rich

diversity of manufacturing configurations, there are

Fig. 3. The anatomy of a DD (a and b).


the following five classic configurations or modes of

manufacturing ([37], p.129).

� Project: Used for one-off products, which tend to be

fixed and built on site, because it is difficult or

impossible to move them, until they have been

completed, e.g. ship building.

� Job-shop: Used for small batches and one-off pro-

ducts, which can be moved around the manufactur-

ing facility e.g. tool making and machine building.

Fig. 4. The production planning and control DD.


� Batch: used when there is an increase in volumes

and repetition of products.

� Line: Used when demand is sufficient to justify the

use of dedicated technology to manufacture high

product volumes on a partly or fully automated

production lines.

� Continuous processing: Used when the demand for

a product is such that the volumes necessitate a

process being used all day and every day. The

technology is likely to be fully automated, e.g. food

and drink manufacture.

Each of the above configurations have different

characteristics (working practices, technology, lay-

outs, etc.) which in turn lead to different decision-

making situations. Several researchers have found a

relationship between the type of organisation and the

dominant types of decision-making processes ([38],

p. 153), Hayes et al. [29], Hill [37] and Schmenner

[39].

In summary, Fig. 2 provides a schematic overview

of the proposed classification schema of manufactur-

ing decisions using three axes. It contains two bold

wavy arrows that indicate how the position of a DA

can vary according to the specifics of the DD and the

manufacturing configuration under consideration. In a

detailed and comprehensive form, this classification

schema would consist of a series of tables illustrating

the characteristics for the variety of manufacturing

decisions that exist in different configurations. Fig. 5

illustrates the system that has been employed. It is

based on the work of [29,37,39] and for each DD, the

relevant organisational characteristics of the various

configurations have been collated. The result is that

individual analysts undertaking GRAI modelling are

able to perform the following functions.

� Identify and select decisions to form the basis of the

GRAI grid.

� Identify and assess decision characteristics and

information requirements.

� Assist in the positioning of individual decision

centres on GRAI grids by considering a decision

centre to be a DA as defined by CIMOSA.

Fig. 5. A template for collecting and recording information on DDs and their constituting DPs and DAs.


3. Case study

In this section, a case study is presented to illustrate

how the classification schema was used to facilitate the

identification and modelling of the manufacturing

decision centres in a GRAI enterprise model. Existing

guidelines on how to construct a GRAI model provide

a definition of a decision centre, but offer limited

information on how to identify decision centres and

understand how they may differentiate from processes

and activities. The classification schema and the con-

cept of DDs, DPs, DAs and DEs are intended to

address this issue.

The case study company is a manufacturing orga-

nisation located in the outskirts of Birmingham, in the

United Kingdom. It is a small family owned business

that was established in 1960 and currently employs 15

people. Originally, the company offered metal polish-

ing services to other local manufacturers, but this has

now been extended to include other metal finishing

operations such as the electroplating of zinc, nickel

and tin. Approximately, 60% of the company’s turn-

over is generated by the automotive sector. The com-

pany is a batch manufacturer processing a large

variety of product families on a number of dedicated

process lines.

� Main zinc line: Where small zinc fabrications are

barrel electroplated.

� Rack line: Where large or fragile zinc fabrications

are electroplated.

� Auto zinc line: This is identical to the rack line but is

fully automated.

� Nickel and tin line: Where nickel and tin fabrica-

tions are barrel electroplated.

� Degreasing line: This is a pre-finishing process.

� Polishing shop: This is a finishing process.

Although, the company frequently introduced new

technology, its manufacturing facilities were clearly

organised into functional units. This resulted in a

focused material flow, but with information flows

and production decisions occurring in relative

isolation. The result was ineffective communication

and variable performance in terms of quality, lead-

times and productivity. Therefore, in addition to

applying and testing the classification schema, the

GRAI model was constructed to perform the following

objectives.

� Map the information and decision requirements for

performance measurement.

� Compare and redesign both information and deci-

sion perspectives to achieve effective and efficient

operation.

� Provide a graphical model for discussion and eva-

luation of any systems changes.

� Develop rules and controls for managing the system

on a real-time basis.

The classification schema proposed by this paper was

used as a framework during the analysis and data

collection phase. It helped to communicate to the case

study company, the variety and characteristics of

manufacturing decisions that can exist. It was also

the basis for conducting the analysis interviews and for

recording information about the company’s DDs, DPs,

DAs and DEs (Figs. 5 and 6). Information such as

decision requirements, time horizons, review periods,

decision authority and decision risk, uncertainty and

repeatability were recorded. This analysis phase

involved interviewing a team of five people consisting

of the Chief Executive, two managers and two line

operators. This synthesis group [40], was responsible

for the strategic, tactical and operational aspects of the

manufacturing system. They provided information

about the hierarchy and links between decisions and

the corresponding responsibilities, functions and plan-

ning. This phase of the study identified eight DDs, and

corresponding processes, activities and entities within

the case study company (Tables 2 and 3). This infor-

mation was then mapped onto a GRAI grid as shown in

Fig. 7.

The GRAI technique does not simulate activities,

but examines the structure of the decision centres and

the flow of information within this structure. It

assumes that decisions start and terminate manufac-

turing tasks and events, and thus, shape the perfor-

mance and operating characteristics of the

organisation. Manufacturing organisations are open

and dynamic systems, serving both internal and exter-

nal customers. Thus, the time validity of decisions

reflects this dynamic factor and this is modelled in the

GRAI grid using layers of decisions with varying

horizons (H) and review periods (P) (Fig. 7).

In order to model the eight DDs identified, a GRAI

grid was constructed for each domain. Within the

limits of this paper, it is not possible to show all of


Fig. 6. Questionnaire design.

Table 2

The case study DDs and constituting DPs and DAs

(DD1) production planning & control (DD2) quality

(DP11) manage products (DP21) definition of customer requirements

(DP111) manage quotations (DA211) set specifications towards quality performance of new

hardware acquisitions

(DA1111) manage orders received (DA212) invest in new hardware

(DA1112) prepare and reply to quotations (DA213) define attributes to be ‘inspected for’ and ‘met by’

individual orders

(DP112) manage raw materials (DA214) allocate work to ensure specifications’ satisfaction

(DA1121) order raw materials in large quantities (DP22) conformance to specifications

(DA1122) order ‘as needed’ raw materials (DA221) ensure conformance to specifications of raw materials to be

used

(DA1123) order ‘trivial’ materials (DA222) final inspection of completed products

(DP12) plan manufacturing (DA223) inspection at completion of each process

(DA121) sequence of order completion (DD3) organisation

(DA122) sub-contract to meet demand (DP31) organisational structure

(DA123) draw daily schedule (DA311) reform organisational structure

(DA124) sign the ‘works order card’ (DA312) improve organisational effectiveness

(DA125) inspect complete products (DA313) change ways people do things

(DA125) keep track of various orders (DA312) daily allocation of duties & responsibilities

(DA125) conduct the process (DA313) suggestion box

(DA125) update the ‘works order card’ (DA312) performance of current configuration

(DP13) manage resources (DP32) control/reward systems

(DP131) manage human resources (DA321) appraisal/promotions

(DA1311) employ more staff (DA322) how to reward good suggestions

(DA1312) arrange overtime to meet demand (DA323) current configuration of administration/operations

(DA1313) allocate duties & responsibilities (DD5) workforce

(DP132) manage physical resources (DP51) job design

(DA1321) sub-contract annual maintenance (DA511) review/assign duties & responsibilities

(DA1322) monthly maintenance checks & updates (DA512) daily assignment of duties & responsibilities

(DA1323) weekly maintenance checks & updates (DA513) actual employee’s performance

(DA1324) allocate work to various departments (DP52) appraisal/reward & wages/motivation

(DP14) quality control/maintenance (DA521) conduct annual appraisal

(DA141) final inspection (DA522) set minimum wage

(DA142) inspection at each process (DA523) set employees’ remuneration packages


these grids. Therefore, to illustrate how the classi-

fication scheme and the CIMOSA framework contrib-

uted to the construction of a GRAI techniques, this

section describes one of the case study GRAI grids

(production planning and control). Referring to

Figs. 7 and 8, the elements of the grid include the

following.

� Grid: This represents a specific DD. It is the macro

structure of the system.

� Columns: These represent the various DPs present

in a DD.

� Rows: These represent the hierarchical layers, as

captured by the theory of hierarchical multi-level

systems, in which the various DAs are operating.

� Boxes: These represent individual DAs. The DEs

that are responsible to and for each DA are recorded

in the bottom of the boxes in brackets.

� Arrowed lines: A GRAI grid utilising two types of

arrowed line. The larger and bold arrowed line

represents the transfer of a decision output between

two or more decision centres. The smaller and

dashed arrowed line represents the transmission

of internal and external information between deci-

sion centres.

Based on Fig. 8 and the analysis that took place, a

number of recommendations were drawn up for the

production planning and control domain. These

recommendations included the following.

Table 2 (Continued )

(DD4) performance measurement systems (DP53) professional development/training & learning

(DP41) set targets (DA531) annual re-training scheme

(DA411) set targets for production (DA532) identify needs for new expertise

(DA412) ensure satisfaction of production targets (DA533) training on an ‘as needed’ basis

(DA413) materialise production targets (DA534) conduct training

(DP42) define productivity (DA535) real-time needs for new expertise

(DA421) define productivity (DP54) health & safety

(DP43) measure productivity (DA541) COSHH system

(DA431) produce a profit & loss balance sheet (DA542) conduct annual maintenance

(DA432) produce a detailed statement of sales (DA543) conduct weekly maintenance

(DA433) inspect ‘works order cards’ (DA544) wear protective uniforms

(DA434) update ‘works orders cards’ (DD7) vertical integration

(DD6) facilities (DP71) procurement policies

(DP61) location (DA711) monthly purchase of raw materials

(DA611) location of facilities (DA712) level of PM’s monthly budget

(DP62) size & structural design (DA713) bargain procurement of large quantities of raw material

(DA621) size of facilities (DA714) buy ‘low-cost’ consumables

(DA622) structural design of facilities (DA715) establish production needs

(DA623) current layout/sequence of processes

(DP63) layout

(DA631) layout of new premises

(DP64) materials’ handling systems

(DA641) develop a system to manage the ‘in process’ materials

(DA642) manage materials

(DA643) current material flow

(DD8) technology

(DP81) organise process flow

(DA811) ensure smooth process flow

(DA812) manage bottle-necks

(DP82) streamline process flow

(DA821) review suggestions

(DP83) dimensions of technology (DOT)

(DA831) decide DOT for each line

(DA832) implement new automations

(DP84) hardware investments

(DA841) new hardware investments


� Errors in transmitting information: This concerns

any information flows that are redundant or inade-

quate for performing a DA. For instance, when

making decisions for the ordering of raw material,

it is important to have accurate information about

work in progress, stock levels and forecasts.

� Problems specific to a DA: A DA must have at least

one decision output arrow. If a DA has no output,

then the role of this activity should be examined.

Also, if the decision output is transferred upwards,

i.e. to a higher level on the grid, then issues about

decision-making autonomy and the value added by

this upward delegation should be investigated.

� Co-ordination between DAs: This concerns any

conflict in goals and objectives that may exist

between the DDs and activities. For instance, sales,

design and production have traditionally had con-

flicting performance objectives.

Specific observations and recommendations for the

case study, company included the following.

� The absence of any DE and decision output for the

quality control/assurance process that took place in

the company.

� The absence of any decision output in the manage

resources process.

� The absence of performance measurement activities

for understanding how decision outputs influenced

manufacturing and service activity.

� The lack of strategic planning and decision outputs,

i.e. the horizon and review periods were relatively

short, but this is a common issue for many small

businesses due to resource constraint and a focus on

operational tasks.

Table 3

The case study DEs

DEs DE acronyms

Managing Director MD

Production Manager PM

Project Manager PRM

Works Foreman WF

Production Team Leader PTL

Inspector IS

Accountant AC

Operators OP

Daily Production Meeting DPM ¼ PM þ WF þ PTL

Focus Group FG ¼ MD þ PM þ WF þ PTL

Customers CU

Suppliers SU

Subcontractors SUB

Future Logistics Manager FLM

Fig. 7. A GRAI grid illustrating the DAs present in a particular DD.


Fig. 8. As–Is grid for the DD production planning and control.

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4. Summary

Organisations are entities that pursue objectives that

cannot be achieved by individuals acting alone. They

are a system of human centred DAs. The quality of

managerial decisions is often an indicator of a man-

ufacturing organisation’s effectiveness [41]. The

increasing role and importance of decision-making

is reflected, by the current interest in knowledge

management. This relatively new discipline is con-

cerned with helping organisations generate, collect

and process information, in such a way that indivi-

duals and organisations benefit from competitive and

innovative decisions.

As a modelling approach, GRAI offers a good

starting point for understanding and designing busi-

ness processes based on effective decision architec-

tures. However, although GRAI aims to model such

architectures, it lacks any guidance on how to formally

identify the DDs, processes, activities and entities that

constitute the architectures. As a result, poor and

inconsistent models representing only a fraction of

the decisions present can be produced.

To address this situation this paper presents a

classification schema for identifying the primary deci-

sions involved in the management of different man-

ufacturing organisations and the corresponding range

of functions and importance. This schema identifies

and allocates manufacturing decisions to particular

DDs and illustrates the range of characteristics among

the DPs and activities that belong to the same domain.

Also, manufacturing diversity creates a range of DPs

and DA characteristics. This range is represented in

the classification by including five classic manufactur-

ing configurations and describing the change between

decision characteristics in relation to the context and

demands of each particular configuration.

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Dr. Ian McCarthy is a principal fellow

at the Warwick Manufacturing Group,

University of Warwick. He heads the

Organisational Systems and Strategy

Unit (OSSU) which studies industrial

organisations using systems methods

and models that focus on evolutionary

and decision concepts.

Michalis Menicou is a researcher at the

University of Sheffield’s Manufacturing

Group. His research focuses on decision

and EM methodologies.


a classification schema of manufacturing decisions for the grai enterprise modelling technique

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