An expert system for paperwork optimization in production systems

Computers ind. Engng Vol. 19, Nos 1-4, pp. 407--411, 1990 Printed in Great Britain. All rights reserved

0360-8352/90 $3.00+0.00 Copyright © 1990 Pergamon Press pie

A N EXPERT SYSTEM F O R P A P E R W O R K OPTIMIZATION

IN P R O D U C T I O N

SYSTEMS

Soheil Khajenoori, Ph.D.

Samuel Awoniyi, Ph.D.

Department of Computer Engineering

Deparlment of Industrial Engineering

University of Central Florida

FAMU/FSU College of Engineering

Orlando, Florida

Tallahassee, Florida

ABSTRACT: Much like material handling, process paperwork only contributes to overhead cost (not proportionally included in the f'mal product). The very computing technologies that have produced electronic mail systems (and similar paper-less information systems) have also made it quite easy and tempting to generate excessive paperwork in form of memos, reports, charts, lists, files, etc. This paper describes an expert system for minimizing such paperwork activities in various production systems.

INTRODUCTION In many production systems, especially manufacturing plants, the amount of paperwork generated in the production processes should be kept to the minimum possible because such paperwork activities do not show directly in the final product. In a way, paperwork is only a necessay "waste", much like material handling. A relationship between paperwork and material handling is contained in [1]. The very computing technologies that have produced electronic mail systems (and similar paper-less information systems) have also made it easy and tempting to generate excessive paperwork in form of memos, reports, chart% lists, and files. Today, many organizations simply generate too much paperwork. Excessive paperwork usually means a waste of time, money, and may affect morale adversely; it is not good for productivity. For a new production system, the "process planning" stage is the first place for addressing the problem of determining appropriate amount of paperwork to be generated during production processes. (We shall henceforth refer to the problem of determining appropriate amount of paperwork as the "paperwork optimization" probelm), In the case of an existing production system, the paperwork optimizaiton problem can be tackled periodically in production process reviews. This paper describes a solution method for the paperwork optimization problem. The problem is essentially a communication cost minimization problem. First, the cost minimization problem is shown (in section 2) to constitute a hard combinatorial problem (on communication graphs), the kind that is suited to expert system as a solution approximation method. Then, (in section 3) we describe some rules for such an expert system; these rules are based on an analysis of production system information flow. Finally, in the last section, implementation is briefly considered. An extended version of this paper contains a full discussion on implementation. The concept of communication graph is fundamental to this paper. A communication graph is a set of nodes and arcs (edges)joining the nodes; Figure 1 below gives an illustration. The nodes represent information/material processing stations. There are directed edges from a node to another node if there are possibilities of communications between corresponding processing stations, each edge representing one communication possibility/type. A good introduction to communication networks is contained in [3]. THE PROBLEM OF MINIMIZING COMMUNICATION COSTS For cost considerations, any piece of communication between two processing stations has four basic components, namely, information coding, information transmission, information reception and decoding, and information maintenance (over the life time of the information).

Therefore, in designing mechanisms for communication between any two

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Proceedings of the 12th Annual Conference on Computers & Industrial Engineering

Centr

roces$tl

Figure 1:

Example of Communication Graph

processing stations, there will ordinarily be many options. These options will vary according to the method of information coding (keyboard, bar coding, voice, etc.), medium of transmission (paper, magnetic storage, computer network, broadcast, e¢), method of reception (computer interface equipment with various protocols, other electronic signal processing, manual methods, etc), and method of information maintenance (computer memory, magnetic storage, films, paper files, etc). Accordingly, communication costs components are information coding cost, transmission cost, reception cost, and information maintenance cost We analyse in this section the nature of the problem of minimizing such communication costs in production systems, with a view to showing why the problem is suited to solution by expert systems. We shall begin by making simplifying assumptions on costs and technology. These assumptions are intended to give a background for certain inlricate features of the problem, features that become clearly defined as the assumptions get discarded one by one. The simplifying assumptions (to be discarded presently) are given in (A) below. (i)

All processing stations shall maintain information in the same form. regardless of the Ibrm in which information is transmitted to them.

(A)

(ii)

For all communication media, the costs are constant over the period of interest (technological advances do not cause any cost changes)

(iii)

For all processing stations, the period of time over which any information may be kept and maintained is finite, deterministic and known.

(iv)

Over the period of interest, the required amount of communication between any two processing stations is finite, deterministic and known.

As indicated earlier, these assumptions will give us a starting point. Assumptions (A) (i) ensures that the costs of communication from a processing station are independent of the means by which information is stored and maintained at that station. In terms of the communication graph, this means that the number of edges emanating from any (processing station) node does not depend on the nature of the edges entering into that node: this point will be clarified later when (A) (i) is discarded.

The other ussumpfions in (A) ensure that communication costs (that is, costs on edges of the

communication graph) are finite, constant and known over the period of interest. Hence, with assumption (A), the communication cost minimization problem becomes easy. It can be solved by a simple greedy procedure. Let us now consider discarding those assumptions one by one. We begin with assumption (A) (i). Suppose, for example, that Figure 2 below is a subgraph of the communication graph under assumption (A) (i).

Now, without

assumption (A) (i), the situation may become as depicted in Figure 3 below, if the three edges into station 2 in Figure 2 correspond to three distinct means of information maintenance at the station. The reason for that substantial increase in the number of edges is that, for cost considerations, the edges from station 2 to station 3 are not independent of the edges from station 1 to station 2, unless assumption (A) (i) or its equivalent

Awoniyi and Khajenoori: Paperwork Optimization Systems

)

)

)

)

409

Figure 2: Under (A)(i)

f

Figure 3: (A)(t) Discarded holds, since the cost of information coding at station 2 may depend on how information is kept at station 2. As a result of this lack of independence among edges, the cost minimization problem does not lend itself to any easy solution method, if assumption (A) (i) is discarded; rather, the problem, considered as a combinatorial optimization problem, appears to be NP-hard. Hence, the use of various heuristics is justified, even if the cost minimization problem is to be solved only once in a while. If assumption (A) (ii), (iii), (iv) are discarded, the result is that costs will vary dynamically with changes in technology and volume of production system operations. These cost changes may not be definite or predictable, or they may behave like random variables, involving complicated stochastic considerations. Accordingly, the use of hetwistics and experimental open-ended procedures (that is, procedures that augment themselves when new information get generated) is justified, when assumptions (A) (ii), (iii), (iv) do not hold. Hence, when all the assumptions in (A) are discarded, the communication cost minimization problem translates into a hard, nondeterministic combinatorial optimization problem, the kind that is amenable to a heuristic-based or role-based expert system. Using an expert system as a medium for applying heuristics in this case is justifiable because expert systems are suited to capturing and applying vague, difficult-to-accumulate, perishable knowledge and rules that require constant updating. As a step in developing such an expert system for the task of minimising paperwork in production systems, we shall give in the next section a numbex of rules that indicate when to use paperwork as a mechanism for communication.

SOME RULES ON PAPERWORK IN PRODUCTION SYSTEMS In the context of production systems information flow, paperwork is only one of many communication media to choose from. Mechanisms using computers and other electronic components are often used as alternatives. In this section, we shall give some rules that indicate when to use paperwork. For ease of presentation, we shall assume there are two broad possibilities -- paperwork and non-paperwork; this assumption will save us details about computer communcation networks (details such as network types, topologies, and related signal processing questions), without ~,ly loss of generality. The rules developed in this section are not "do's and don'ts" of the most general kind. They are rules associated with specific parts of production systems. Accordingly, we shall first present an analysis of production system information flow, with a view to showing the basis for those rules. Figure 4 below shows the system of information flow and material flow for a typical production system. As it does not have to do with ownership of materials and workpieces, but is concerned with processing only, the system in Figure 4 is independent of whether production is manufacturing type or not (manufacturing systems are different from non-manufacturing systems only in the ownership of materials and workpieces during processing)

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Proceedings of the 12th Annual Conference on Computers & Industrial Engineering

Production

~°'

planning

.(

To illustrate, refering to Figure ~, "Production Control" might send aggregate plan goals down to the department level in terms of production per wc~k, whereas production plans might be made at the department level in terms of days. In such a case, we would say that production cuntrol is not fully centralized, since daily control is done at the deparunem level The concept introduced in the following definition is aimed at capturing the communication network implication of the situation just described. Definition~

If plan/control instructions from Production Control to a department are given in terms of production per units of time, and production control plans at the department level are made in terms of production perp units of time, then we shall say that the degree of control centralization is ~ B / ~ .

Therefore, the degree of control centralization (henceforth dec) is 1 when there is full ceat~alization of control (that is, Production Control gives all details everytime), and less than l otherwise. Next, let us consider conditions that might cause several departments to have a common source of instruction or infoxmation besides Production Control. For instance, suppose two departments need the same type of infomlation, say type F, from Production Control for the purpose of doing their daily material processing. If it gives control instructions and data on a weekly basis, Production Control might find it more economical to create an intermediate common information source or database for giving information of type F to the two departments on a daily basis. This idea about database creation is stated as a "principle" below. Database Creation Principle~ Suppose two departments ne.ed the same type of information from Production Control, and suppose the dec relative to each department is less than 1, then it may be economically justifiable to have a database to furnish that information to the departments. Even if the economic advantage

is small, one may still justify such a database on account of information

consistency. Note that if the dec relative to each department is 1, then there may be no need for a database, since the departments' information needs will be satisfied directly by Production Control everytime. Before we get Io rules on paperwork, there is one more basic concept to introduce - the concept of distance from final product. As an illustration, "demand forecasting" and "materials receiving" are usually far from final product despatching, whereas aspects of "product testing" and "product quality assurance" arc usually close to final product despatching. This observation is of interest in designing how Production Control might communicate with departments responsible for forecasting, materials receiving, and quality assurance, since this has to do with s p e ~ and frequency of communication. This reasoning is the motivation for the following definition. Definition_:

The number of time units between the performance of a department's function and the shipping/despathing of corresponding final product shall be called the department's

distance

from

final

tax)duct.

henceforth abbreviated as dfp. For a good number of production systems, there should be a linear relationship between average dec and average dfp; that is, the further a department is from final product despatching, the less frequently the department might have

Awoniyi and Khajenoori: Paperwork Optimization Systems

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communications from Production Conlrol. Now, we are prepared to translate the foregoing analysis into some rules on when to use paperwork for production system communications. As paperwork method is essentially manual, whereas non-paperwork methods are essentially automated, the rules given here reflect some practical automation boundary economy. A basic economic principle behind these rules is that if paperwork will require extensive or intensive human work, then it may be better to adopt a nonpaperwork alternative; otherwise paperwork may be cheaper, when information coding, wansmission, reception, and maintenance are all considered. Rule 1. If the dec relative to a department is less than 1/2, then Production Control may use paperwork in transmitting communications to that department. Rule 2 If the dfp of a department is large enough, then Production Control may use paperwork in transmitting communications to that department. Rule 3 If the difference of two departments' dfp's has a suitably large magnitude, then those two departments may use paperwork in transmitting communcafions between each other. Rule 4 Paperwork may be used to maintain a record copy for a database created according to the Database Creation Principle stated earlier, even though the database itself may store information with a non-paperwork method so as to be able to sustain frequent queries. The rules above may be generalized by replacing "department" with "subdepartment" and "Production Control" with "department". These rules are not hard and fast; but they provide a useful orientation. In the next section, where we consider implementation, we shall indicate how to combine other rules with these rules. We shall also indicate how these rules multiply themselves in specific applications.

IMPLEMENTATION REMARKS In specific applications, the rules given in the last section will ordinarily translate into many equivalent, but more specific rules, depending on

size and extent of computerization of the production system communication network.

Consider, for instance, a university as a production system - a non-manufacturing type, since the university does not own the students! Here, the rules of the last section might translate into the following more specific rules: (i)

Once-in-a-while policy announcements by the university administration to the colleges may be

(ii)

Student advising rgulations from Academic Affairs Office may be given through a common database (on

transmitted on paper (Rule 1);

computer), with a record copy kept on paper (Rule 4); (iii)

Once-in-a-while policy announcements from colleges to their departments may be transmitted on paper (Rule I again);

(iv)

Student counseling instructions from colleges to their departments may be given through a common database (on computer), with a record copy kept on paper (Rule 4 again);

(v)

Course instructions to individual faculty may be transmitted on a computer LAN (Rule 1 by implication);

(vi)

Personnel policy information from university and college administration may be transmitted on paper (Rule 1);

(vii)

Communications from Student Recruitment Office to Commencement Office may be transmitted on paper (Rule 3);

(viii)

Regulations from university administration to Student Recruitment Office may be transmitted on paper (Rule 2).

(i) - (viii) give an illustration of how the rules of the last section may multiply in specific applications. In a longer version of this article, we describe a more complete case study, together with details of an expert system implementation, based on these rules and other well-known general rules pertaining to computer communication networks. REFERENCES 1. Graham, B.S. Panerwork Simplificatioll Johnston Associates, Watkinsville, Georgia, USA (1987) 2. Harmon, P. & D. King ~

John Wiley & Sons (1985)

3. Pimentel, J.R. Communication Networks for Manufacturin~ Prentice Hall, (1990) 4. Rowe, N.C. Artifical Intellieence Through Prolog, Prentice Hall (1988)