Impact of management information systems on decisions

OMEGA Int. J. of Mgmt Sei., Vol. 20, No. 1, pp. 37--49, 1992

0305-0483/92 $5.00 + 0.00 Copyright © 1992 Pergamon Press plc

Printed in Great Britain. All rights reserved

Impact of Management Information Systems on Decisions T MUKHOPADHYAY Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

RB COOPER University of Houston, Texas, USA (Received January 1991; in revised form May 1991) The effectiveness of management information systems (MIS) depends upon their impact on the quality of managerial decision making. This paper explores this impact from a microeconomic production perspective, with MIS as an input to a management decision production function. Using inventory control decisions as an example, analytical and simulation evidence is provided that the production perspective is reasonable. In addition, benefits of this perspective in describing the effect of MIS on decision making and in providing guidance for appropriate MIS investment are illustrated.

Key words--management information systems, microeconomics

1. INTRODUCTION

have provided little help [6]. The underlying reason for MIS effectiveness assessment inadequacy is the lack of relevant theory of MIS effectiveness [1, 16, 24]. In this paper, an economic referent is employed as the foundation for MIS effectiveness theory. This is not a rejection of the calls for the use of political and organizational referents [e.g. 10, 31]. Rather economics is used here with a clear understanding that its appropriateness is contingent at least on the organizational context. Thus, using inventory control decisions as an example, this paper illustrates the advantages of employing microeconomic production theory to help develop an MIS effectiveness theory. We start by defining the effectiveness of MIS in terms of its impact on management decision making: MIS are more effective to the extent that they lead to better quality management decisions. From a production perspective, MIS is input to a management decision making func-

THE REAL INVESTMENT in information technology has been increasing rapidly in recent years, and is expected to follow this trend for the remainder of this century [11]. The appropriate application of this technology is a priority issue for organization competitiveness and survival, Key to taking advantage of this information technology investment is the ability to assess current and future information system effectiveness [4] which enables the diagnosis of current systems as well as the proper setting of system implementation priorities and corporate-wide resource allocation [12, 19]. As a significant component of an organization's information technology, management information systems (MIS) and their effect upon organizations are the subject of this paper, Unfortunately, few organizations can adequately assess MIS effectiveness and researchers 37

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Mukhopadhyay, Cooper--Impact of M1S on Decisions

tion and decisions are output. Applying micro- effectiveness. We examine the descriptive and economic production theory to facilitate the normative insights gained from viewing the modeling and analysis of this function provides impact of MIS on management decisions in both descriptive and normative insights into production terms in Section 6. MIS effectiveness. A limitation of the proposed approach, howThere are a number of microeconomic ever, is its applicability to structured decision production approaches available for modeling making contexts only (transaction processing management decision making. These approaches systems) due to the microeconomic assumptions differ mainly in terms of their conceptions of of non-conflicting goals and a constant producthe production function. That is, in terms of tion technology. This is not a serious problem how MIS attributes affect decision making. For since the bulk of the information technology example, cost-benefit analysis does not attempt investments in most firms are in transaction proto posit a functional form but rather hypoth- cessing systems [20]. Moreover, current work in esizes that inputs affect outputs in some un- microeconomic theory (e.g. including uncertainty specified manner. Thus, methods for determining in production functions and reducing dependthe form are largely intuitive [23]. In contrast, ence upon profit maximization) is decreasing the parametric production function approaches use restrictiveness of microeconomic assumptions. specific functional forms to relate inputs with We discuss the applicability of this approach outputs; as a consequence, a rich set of econo- further in Section 7. metric methods can be used to estimate this much more detailed mathematical function 2. A MICROECONOMIC PRODUCTION [7, 25, 27]. Finally, non-parametric approaches VIEW OF DECISION MAKING fall between cost-benefit analysis and parametric approaches. An example of this is data developA microeconomic view of the decision makment analysis which does not assume a specific ing black box is described here based upon the parametric production function [6]. approach introduced by Cooper [7]. According Thus, all else equal, the availability of sophis- to this view, decisions are produced in much the ticated function estimation methods and the same way as normal goods and services. First, insight coming from the mathematical detail an MIS converts data into information useful of resulting functions make the parametric for decision making. Next, information is input approach of microeconomic production theory to the decision process and decisions are output. very attractive for MIS effectiveness theory. Note that the division of tasks between these A prerequisite to operationalize this approach, two decision production stages depends on however, is the ability to measure information the MIS design. An MIS may present detailed and decisions as inputs and outputs of produc- or summarized information about entities or tion functions. We develop metrics to accomplish events relevant to a decision problem or it can this in this paper, delve further into the decision making process As noted by Crowston and Treacy [8], deter- by suggesting alternative courses of action or mining MIS effectiveness requires that the even recommend a specific action [29]. 'black box' of information usage be opened in In its role as an input in this decision making order to examine the processes by which inform- process, information created by MIS must be ation affects the organization. This black box is characterized in a way which can be measured opened here. In this paper, the use of inform- and which makes a significant impact upon the ation within the decision making process is decision process. There has been much research examined and modeled using microeconomic in the Information Economics (IE) and MIS production theory. We operationalize this literature attempting to characterize information approach by developing metrics for information [2]. According to IE, the decision maker has and decisions in Section 2. In Section 3, we knowledge of every action-state pair, but has use an inventory control system as an example probabilistic knowledge of which state will occur. to illustrate the modeling process. Based on Information is used to refine the probabilistic analytic and simulation modeling in Sections 4 knowledge of state occurrence in accordance and 5, we find the microeconomic perspective with Bayes' theorem of conditional probability. appropriate in this context for examining MIS Thus information attributes considered im-

Omega, Vol. 20, No. 1

portant are those that can help measure the impact of information on the probabilities of state occurrence. Examples of such attributes include: • Fineness: The amount of detail contained in i n f o r mation

[28].

[14]. Error is introduced by improper sampling, measurement, transcription, and transformation [24]. • SuOfciency: Includes both fineness and

accuracy [3, 15]. e Timeliness: Lack of delay between the

event occurrence and receipt of information [9]. We expand upon the IE work', and characterize information in terms of accuracy and coverage, Both of these characteristics contain multiple attributes which can be objectively measured and which have direct implications for MIS design, Accuracy refers to the extent to which a description is in accord with reality and is assessed by comparing the description with the corresponding part of reality. The accuracy of a description can be measured as: Accuracy = 1 - Error/Absolute magnitude;

where magnitude refers to the mean of the accurate values of the description. This metric of accuracy represents an interval scale. It conforms to the common notion of accuracy expressed in terms of percentage value. This scheme works well if error does not exceed the absolute magnitude. If it does, then a simple change of this interval scale would be to alter the denominator to a multiple of the magnitude ~To help orient the reader, we include below the explicit relationship between information economics and our

information characterization scheme: Information economics equivalent

Accuracy--Random component

Accuracy[14]

Accuracy--Systematic component Coverage--Spatialdetail

-Fineness[28]

Coverage--Spatial scope Coverage---Temporal detail Coverage---Temporalscope

-Fineness [28] -Timeliness [9]

Coverage--Timeliness

Spatialscope Spatial detail Temporal scope Temporal detail

• Accuracy: The lack of e r r o r o r 'garbling'

Our characterization

Attribute

Timeliness

39

Table1. Attributes of coverage Definition How many events or objects are described? All, none, or some? At what level of aggregation are the events or objects described? At individual level, fully aggregated level, or somewhat aggregated level? What time period is covered by this description? Total relevant period, none of it, some of it? How often is the description updated by the MIS? Every day, never, every week? What is the age of the description? (relevant for information describing current state of reality). Verycurrent,veryold, somewhatold?

such that the maximum value of the error does not exceed the new denominator. The Error term may include up to two components. A random component (which corresponds to the typical IE notion of accuracy) summarizes the impacts of such factors as media transcription error, sampling error, etc. A systematic component reflects the presence of bias caused, e.g. due to incorrect assumptions or the purposeful altering of data by subordinates. To measure error, the deviations between the description contained in information and reality are measured over the relevant time period. Next, standard statistical techniques can be used to calculate Error. For example, systematic and random components can be measured respectively by the mean and standard deviation of the calculated deviations. Similarly, the mean squared error of calculated deviations can be used to measure the error squared when both random and systematic components are present. Coverage is a measure of how inclusively the description represents the relevant parts of reality. The measurement of coverage includes the assessment of five attributes (Table 1). Two attributes, spatial detail and spatial scope, relate to events occurring or objects existing in space at a single point in time while two attributes, temporal detail and temporal scope, relate to events occurring or objects existing over time. The IE notion of fineness is divided here into spatial and temporal components and accounted for by spatial and temporal detail. Spatial detail is the lack of aggregation in a description across events or objects and spatial scope is the number of events or objects included in a description. Thus, a report which includes sales associated with each salesperson has greater spatial detail than a report which reports total sales for all salespersons. With this example, spatial scope is constant since the same events

40

Mukhopadhyay, Cooper--Impact of MIS on Decisions

(sales generated by a group of salespersons)are being reported. Spatial scope would be reduced if sales generated by only a subset of salespersons were included in the report, Temporal detail and scope are analogous to their spatial counterparts. Temporal detail is the lack of aggregation in a description across time and temporal scope is the length of time for which a description tracks reality. Thus, a report which includes monthly sales for a year has greater temporal detail than a report which presents quarterly sales for the same period, Since data over a whole year are presented, temporal scope does not change; temporal scope can be decreased by including data for only six months, The fifth attribute of coverage is timeliness which corresponds to reporting delay modeled by IE researchers such as Feltham [9]. Note that timeliness is relevant for descriptions that deal with the current state of reality. Due to processing delays, often a description may not include most recent events, and may not be rated high in terms of timeliness. Thus even though two descriptions may exhibit the same level of temporal scope and detail, they may differ with respect to timeliness. For example, a report that describes daily sales (a certain level of temporal detail) for the last week (a certain level of temporal scope) and is three days old is less timely than a similar report which is only a day old. In short, timeliness is inversely related to the age of the description. Each attribute is measured in relative terms, Thus the temporal detail of daily information is five times as much as weekly information assuming five working days in a week. The measurement of each attribute is normalized by dividing the raw score by the maximum score possible for that attribute. As an output from the decision production process, decisions are measured in the same way as information. For example, decision accuracy is measured by comparing the decision alternative chosen with that which would be chosen by a rational decision maker with perfect information 2. This is analogous to the measurement of information accuracy and thus decision accuracy

can be assessed in terms of the two error components: random and systematic. Decision coverage is also measured in terms of the five components described above. For example, temporal detail is measured in terms of how often decisions are taken. It is evident that measuring information or decisions is a difficult problem. We do not claim that the proposed metric is comprehensive. For example, this metric does not include behavioral attributes such as presentation format. Although we may include such variables in future, one difficulty with most behavioral attributes at present is the paucity of objective measurement schemes. However, the inability to model user interface characteristics is somewhat mitigated by the fact that our approach is less applicable for systems such as decision support systems (see Section 7). Despite the shortcomings, the metric in its current form allows us to examine certain fundamental dimensions of MIS designs. Each of the five attributes of coverage has direct implications for MIS design decisions. Information accuracy, on the other hand, is affected by common data processing problems such as transcription and transposition errors (random error component) and/or bias (systematic error component) introduced by wrong interpretations and assumptions used. Finally, decision accuracy is determined by both information accuracy and coverage. Next we apply this model to an inventory control context to illustrate the operationalization of the input, output, and function components. 3. APPLICATION OF THE MODEL TO INVENTORY CONTROL DECISION MAKING This section applies the decision production model to inventory control decision making. Inputs (information provided by the MIS), outputs (decisions made), and functions (decision making process) are operationalized in this context so that their interaction can be compared to what is expected from a microeconomic production perspective. In particular, two key microeconomic properties are examined:

(1) Diminishing marginal productivity: 2Note that in our framework only totally accurate decisions require the decision maker to be rational. One reason decisions may be inaccurate is that decision making may be affected by behavioral aspects of information,

Does information input to the decision making process have a positive but diminishing impact upon decision quality?

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Omega, Vol. 20, No. I

Though it makes intuitive sense that, say, increases in information accuracy will lead to increases in decision accuracy, it is not clear that this relationship is everywhere positive and diminishing as required by microeconomic theory, Alternative forms for this relationship include linear, non-monotonic, discontinuous, etc.

(2) Substitutable inputs: Are information accuracy and coverage substitutable in the sense that more of one can replace less of the other and still produce the same decision quality? Alternatives to this substitutability requirement include limitational inputs where a given level of decision quality can only be produced by a specific combination of information accuracy and coverage. In addition, substitutability among information inputs requires that both of these inputs contribute to the production of decision quality, Three popular reorder point approaches to inventory control are used for independent demand items such as finished goods, spare

parts, and retail and wholesale items [5]: fixed reorder cycle (also known as the S, R system), fixed reorder quantity (or s, Q system) and optional replenishment (or s, S system). Of these, the fixed reorder cycle system which allows for customer backordering is the most widely employed [13], and will be used to illustrate the microeconomic approach. The MIS for fixed reorder cycle inventory control consists of two subsystems (Fig. 1). The transaction processing subsystem keeps track of shipments, customer orders and returns, purchase orders, receipts, scrap records, etc. These data are recorded in a file and are used to update quantity on record for each inventory item during each reorder cycle. (Quantity on record is equal to quantity on hand plus quantity on order minus the quantity backordered. The quantity backordered is that which a customer has ordered but is not available in inventory for sale.) The forecasting subsystem selects an appropriate reorder point (also called order-upto point) for each inventory item by comparing the costs of holding inventory with the potential lost sales when not enough inventory is available. Data required for selection of this point include holding costs, spoilage rates, and demand schedules.

Transaction Data

Transaction Processing Subsystem

Demand Data

Management Information System

Quantity on Record Accuracy and Coverage

Forecasting Subsystem

Reorder Point Accuracy and Coverage

Order Quantity Decision Making

I

Decision Accuracy Fig. 1. A fixed reorder cycle system.

Mukhopadhyay, Cooper--Impact of MIS on Decisions

42

Decisions are made on a periodic basis. At the end of each review period, a tentative order quantity is determined for each inventory item by subtracting its quantity on record from its reorder point. If this order quantity is greater than zero and the decision maker determines that it is reasonable, then an order is sent out to the supplier. Thus the relevant attribute of decision coverage is temporal detail which is fixed by the reorder cycle. As a result, decision coverage in this context is not a dependent variable. However, the accuracy of these order quantity decisions is dependent upon the coverage and accuracy of quantity on record and reorder point information generated by the MIS: (1) where Da is decision accuracy, Qc and Qa are quantity on record coverage and accuracy, Rc and R~ are reorder point coverage and accuracy, Of the five prospective attributes of coverage, the two spatial attributes, spatial detail and scope, are not relevant for quantity on record and reorder point information. This is because a decision is made on each inventory item separately and the quantity on record and reorder point information used for each decision are each a single number, In the case of quantity on record, temporal scope is constant since quantity on record, by definition, represents the stock situation at a specific instant in time. Moreover, temporal detail (updating frequency) of the information is fully determined by the reorder cycle. However, timeliness, referring to the age of quantity on record information, is relevant because quantity on record of an item reflects the current stock status. Thus timeliness of this information is O~ =g(Qc, Qa, Re, Ra)

3The error in quantity on record information is the results of many factors such as sampling errors, measurement errors, transcription errors, transposition errors, logical errors, etc. Thus a normal distribution can be used to

approximate this error, 4Such a bias would not be present in an 'ideal' system that can predict demand accurately. 5In addition to the typical data processing errors as in the case of quantity on record information, the reorder point is subjected to errors arising out of the limitations of the forecasting model and forecaster's judgment. Moreover, the reorder point error is assumed to be independent of the quantity on record error because the inventory monitoring and forecasting subsystems use different data and different procedures to generate reorder point and quantity on record information,

used as the measure of quantity on record coverage: Q~= 1/Tq (2) where Tq denotes the age of quantity on record information. Reorder point describes the projected future requirement of an item at a specific instant of time (demand conditions change with time) resulting in a constant temporal scope. Timeliness is not a relevant attribute here, because reorder point does not concern with the current state of reality. The relevant design issue for the forecasting subsystem, and hence for reorder point information, is how frequently this information is updated. Thus temporal detail of the information is used as a single measure of reorder point coverage:

Rc= 1/T, (3) where Tr is the time between updates. Information accuracy is subjected to systematic and random error terms. For the quantity on record, the persistence of systematic error is unlikely since it would be detected and corrected during periodic physical inventory counting. The error associated with quantity on record results from many unrelated factors such as transcription errors, transposition errors, sampling errors, logical errors, etc. [24] and can be summarized by a random component: Qa= l-trq/(1 (4) where O'q is the standard deviation of the error term eqi and ~ is the mean of the accurate quantity on record information over the production period. The error term is assumed normally distributed 3 with a zero m e a n : eqi... N ( 0 , O'2q). For the reorder point, systematic error comes from a positive bias 4 which is typically created by overestimating the point to protect against stockouts (e.g. using a safety stock). Random c o m e s from the effect of stochastic demand conditions upon the forecasting model and forecaster's judgment limitations [5]. Thus, reorder point accuracy can be measured as: error

R~ = 1 - ([MSE(e.)]t/2)/~

(5)

where M S E ( e , i ) is the mean squared error of the reorder point e r r o r t e r m (eri) and ~ is the average of the accurate reorder points. The error term is assumed normally distributed 5 with a positive mean: eri ~' N(#,, a~), #, > O.

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Omega, Vol. 20, No. 1

The measurementofdecisionaccuracyshould allow for both random and systematic errors, The presence of systematic error is borne out by fact that average shortage (negative deviation) is negligibly small compared with average excess inventory (positive deviation) thus leading to a positive bias [13, p. 195]. Decision accuracy can thus be measured as:

Da= 1-([MSE(ed;)]I/2)/~ (6) where MSE(ed;) represents the mean squared error of the error term (ed;) and ~ is the average of the accurate order quantity decisions, The general decision production model has thus been fit to the fixed order cycle inventory control decision context. This formulation is used for the following analytical work and the subsequent simulation, 4. ANALYSIS OF THE MODEL This section derives analytical solutions to the inventory control decision making model in order to examine the two microeconomic properties (diminishing marginal productivity and substitutable inputs) in this context. If these are found to be reasonable descriptions of decision making behavior, the use of the microeconomic production model is supported as an MIS effectiveness theory referent. In order to make this analysis tractable, the following simplifying restrictions must be made, which are relaxed later when a simulation is used to evaluate decision making behavior: (1) Reorder point and quantity on record coverage (Re and Qc) are fixed at constant levels by assuming that reorder point is updated as often as decisions are made, and quantity on record information is current. Thus, decision accuracy becomes: Da = g(Ra, Q,,). (2) Order quantity is assumed to be positive indicating that orders are always

placed.

(3) Only high values of quantity on record accuracy are allowed. Since quantity on record error (eq;) is assumed to be normally distributed, low values of accuracy may lead to negative quantity on record. (Even though backordering

may result in negative quantity on record, the sum of quantity on hand plus quantity on order should still be positive; large errors may make this sum negative.) (4) Similarly, only high values of reorder point accuracy are allowed. The assumption of positive order quantity implies that the order quantity decisions can be made by subtracting quantity on record from the reorder point. Thus, if decision error is denoted by ed;, then the following relation holds: ea;= e,,-eq,.

Since er; and eqi are independently and normally distributed, ea; must also be normally distributed. In particular, since E[e,;] =/~, and E[eqi ] = O, ed,=NLu,. ,

(tr~+a~)].

(7)

Equation (7) indicates that decision error (ed;) consists of a systematic component captured by /tr and a random component captured by tr~ "~ O'2q. Substituting (7), (4) and (5) in (6) we derive the decision accuracy function: Pc, = 1- If2(1- R°)2+~#2(1 - Q,,)2]~/2

(8)

From (8) the two microeconomic properties, diminishing marginal productivity and substitutable inputs can be tested. Diminishing marginal productivity

The marginal product of an input [first order partial derivative of (8)] represents the rate of increase of decision accuracy, for a small increase in the input. It can be shown that the marginal product of Qa or Ra is positive but diminishing. Thus increasing either quantity on record accuracy or reorder point accuracy leads to an increase in decision accuracy although at a diminishing rate. This result corresponds to a microeconomic production process.

Substitutable inputs The ease with which one input can be substituted for another is given by the elasticity of substitution of a production function. The elasticity of substitution is defined as the proportionate rate of change of the input ratio divided by the proportionate rate of change of the technical rate of substitution. It can be shown

44

Mukhopadhyay, Cooper--Impact of MIS on Decisions

that the elasticity of substitution is positive (except for the case of perfect information, for which it is undefined). This result is in accord with a microeconomic production function, With some simplyfying restrictions, the analyses above support the notion that the effects of information upon decision making are in accord with microeconomic production relationships. The next section relaxes these restrictions and demonstrates that the microeconomic relationships still hold. 5. A SIMULATION STUDY OF INVENTORY CONTROL DECISION MAKING Four restrictions were required for the analytic results: (1) Quantity on record is current and the reorder point is updated when decisions are made. (2) Orders are always placed, (3) Quantity on record is assumed to be very accurate. (4) Reorder point is assumed to be very accurate, By relaxing these four restrictions, a more realistic evaluation of whether microeconomic production theory is an appropriate MIS effectiveness referent can be undertaken. Since an analytical approach is not possible under these conditions, a simulation study is employed. In this study, variable definitions for MIS information input to the decision making process remain the same as previously described. In addition, customer demand is based upon the actual demand faced by a paint factory over a six year period [18]. Following simulation literature suggestions [26], this demand is converted to a standard probability distribution; the distribution used is normal as suggested by Buffa and Miller [5]. A chi square goodness of fit test comparing the resulting distribution with the actual demand stream indicates an excellent fit (alpha =0.05). 6

As with any simulation, issues of data reliability (independent and identically distributed observations at steady state) must be addressed by the appropriate selection of run length, number of replications, and variance reduction technique. Based upon pilot runs, • Steady state conditions are identified by the Kelton and Law [21] method--which looks for an output time series of zero-and confirmed by the Schruben [30] method--which is based upon convergence of deviations around the average of a Brownian bridge process. • Run length and number of replications are determined to be 300 observations and 10 replications based upon budget limitations and a study by Kelton and Law [22]. • Antithetic variates variance reduction technique [26] is employed which results in a 50% reduction in output variance and significant CPU time reduction. Pilot runs were also used to determine the most effective experimental design within the budget constraints. It was found that reorder point coverage has an insignificant effect upon decision accuracy. This makes intuitive sense since it indicates that more frequent sampling from a stationary demand distribution has little benefit. Thus, reorder point coverage is eliminated leaving the following decision accuracy function: Do= g(R,, Qo, Qc)

A full 5 x 5 x 5 factorial design with 125 data points is used to study this function. Each data point is determined by averaging across 10 replications with each replication consisting of 300 inventory control decisions. Candidate microeconomic functional forms which can be used to fit the simulation data include: (1) constant elasticity of substitution (CES); (2) variable elasticity of substitution (VES); (3) linear; (4) fixed proportions; (5) Cobb Douglas; and (6) translog. The CES and VES functions in three inputs are not applicable since 6The mean and standard deviation of the resulting distri- they cannot be directly estimated from their bution are 524 and 153 such that the probabilityof negative demand (customer returns greater than customer inputs and outputs by the Taylor series method. purchases) is less than 0.001. Linear and fixed proportions functions are not

Omega, Vol. 20, No. I

applicable since the pilot data suggested that marginal products are not constant and that there is significant potential for substitution among inputs. Thus only translog and Cobb Douglas functions are estimated; that function which best fits the data is then used for further analysis, The following translog function results from the simulation data (significance levels are shown parenthetically): In Do = - 0 . 1 8 2 + 0.411 In Ro - 0.161 In Qo - 0.308 In Qc

(0.000)

(0.000)

(o.037)

(0.000)

+0.022(1n Ra) 2 - 0.282(1n Qo)2 _ 0.225(1n Q c ) 2

(0.251)

(0.001)

(0.000)

-O'0921nR~lnQa+O'O481nRalnQc-O'5541nQalnQ~

(0.007)

(0.005)

(0.000)

(9) which has an R 2 of 0.977 (adjusted R 2 of 0.975) and is significant at alpha = 0.000. The translog function provides an excellent fit and the Cobb Douglas function alternative is not viable because the null hypothesis that the second order terms of the translog function are zero (i.e. the production function has a Cobb Douglas form) is rejected at alpha<0.01. The estimated translog function is thus accepted as properly representing the data. This function (9) conforms to the microeconomic production process. Marginal products of the inputs are positive and diminishing in the relevant range of production. The elasticity of substitution between any two inputs of the decision accuracy function is also positive. The condition for increasing returns to scale can be given as: - 1.20 In Qa - 0.96 In Q~ > 1.06.

(10)

Equation (10) indicates that the decision accuracy function exhibits increasing returns to scale for low values of inputs. However, as input values are increased, beyond a threshold level ( R a : > 0 , Q~=Qc>0.61), decreasing returns to scale sets in. 6. DISCUSSION With some significant restrictions, the analytical modeling was able to show that the impact of information upon inventory control decisions is in accord with the typical microeconomic production process. Dropping the restrictions, a

45

simulation was run which again demonstrated the accord. This simulation is empirically grounded in the inventory ordering decision context in the following ways. The decision rule is that most widely employed by organizations (fixed reorder cycle) [13], the appropriate components of information quality and decision quality as well as their operationalizations specifically reflect this decision context, and actual inventory item demand from a paint manufacturer is used as a basis for the simulation. The analytical work and the simulation thus provide support for using microeconomic production theory to examine the effectiveness of MIS in the inventory ordering context. To scrutinize the microeconomic model further, we compared it with a naive model which does not contain microeconomic properties. If the naive model fits data just as well, our claim of support for the microeconomic view would be weakened. To test this, we used a simple regression model:

Do=O.150+O.456Ro+O.432Qo+O.248Qc. (9a) The significance of this model is less than 0.001 which compares well with the microeconomic model (9). However, the R 2 is 0.70 which indicates much poorer fit compared to the microeconomic model. Thus the production model provides more explanatory power than the equivalent naive model; this strengthens our claim that the microeconomic view is appropriate in this context. Having thus tested the microeconomic model, we proceed to examine both descriptive and normative insights that can be gained when employing production theory to examine MIS effectiveness. The following discussion provides some examples of these insights. Note that these descriptions take on an ideal cast without digressions and caveats concerning limitations of applicability, availability of data, etc. Subsequent discussions then address these limitations and potential weaknesses associated with a microeconomic production view of MIS effectiveness.

Descriptive examination of MIS effectiveness Descriptive insights focus on relationships among the various MIS inputs and their impact on decision accuracy. Examining the decision production function (9), we find that if reorder point accuracy is increased with the other inputs

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Mukhopadhyay, Cooper--Impact of MIS on Decisions

held constant, then decision accuracy increases but at a diminishing rate. This is also true of quantity on record accuracy and coverage, Thus, increases in any of these inputs always increase decision accuracy. Interestingly, increasing the accuracy of inaccurate information has a greater impact on decisions than increasing the accuracy of more accurate information, If all three inputs are increased together by the same scale (e.g. all multiplied by 10%), then their combined effects are positive and ultimately diminishing (decision accuracy increases by less than 10%). In addition, all three inputs are substitutable in that the same level of decision accuracy can be maintained by, e.g., increasing reorder point accuracy and decreasing quantity on record accuracy. Thus all three inputs seem to act as normal inputs in a production process. However, upon examining their cross marginal product, an interesting interaction between quantity on record accuracy and coverage is revealed. For low to medium values of these two inputs, quantity on record accuracy and coverage are complements, This means that when the transaction processing subsystem (see Fig. 1) is poor, increasing its accuracy enhances the impact on decision accuracy due to an increase in the coverage of the monitoring subsystem; similarly, increases in the monitoring subsystem's coverage improves the impact of the subsystem's accuracy on decision accuracy. When the monitoring sub-

system is good, these inputs act competitively. Increases in one reduce the impact of the other on decision accuracy. This movement from complementarity to competitiveness (see Fig. 2) can be explained in terms of the output limitations on decision accuracy. We expect that inputs to decision production would initially be complementary as is typical of economic inputs [17]. However, since the production of decision accuracy has an upper bound, one can conceive of a congestion effect, where at higher levels of all inputs, increasing one inputdiminishes the productivity of that input as well as other inputs. In this case, we find the congestion effect acting between quantity on record accuracy and coverage. These insights into the impact of information on decisions provide an increased understanding of this process. In sum, we find: • Increased investment in MIS plays a positive role in increasing decision accuracy. However, due to diminishing marginal products, additional investment in the quality of MIS inputs will have less impact on decisions for a currently good MIS than the same investment for a currently poor MIS. • Ultimately diminishing returns to scale indicates that, say, doubling the investment in all MIS inputs simultaneously

1

Quantity o n Record Accuracy

T

0

1 Quan~ty o n Pa~ozd Coverage

Fig. 2. Joint effects of quantity on record accuracy and coverage (reorder point accuracy at 0.75).

Omega, Vol. 20, No. I will ultimately result in less than double the increase in decision accuracy, • With the substitutability among MIS inputs, investment among MIS inputs can be traded off against each other to maintain the same level of decision accuracy. For example, if demand forecasts become less accurate due to changes in demographics, within reasonable limits this can be compensated for by increasing quantity on record accuracy and/or coverage, • When monotoring the results of phasing in increases in MIS inputs there may be times when the productivity of, say, increased quantity on record accuracy will be less than expected. This can directly result from the prior increase in, say, quantity on record coverage if the inputs are within the competitive region rather than the complementary region, This understanding can be combined with information about costs associated with MIS inputs in order to identify appropriate or optimal employment of these inputs. Examples of such normative guidance are provided next. Normative examination of M I S effectiveness

Consider a situation where the accuracy of inventory order decisions is 0.60 (60%). Management feels that this is too low and would like it increased to 0.70. The Data Processing Department suggests two alternative ways to increase decision accuracy to this level. The first is to develop a better demand forecasting algorithm, thus increasing reorder point accuracy, The second is to develop a better inventory monitoring subsystem, thus increasing quantity on record accuracy. (Currently reorder point accuracy is at 0.65, quantity on record accuracy is at 0.37, and quantity on record average is at 1.00). Based on the decision production function (equation 9), in order to increase decision accuracy from 0.60 to 0.70 by enhancing the forecasting algorithm, reorder point accuracy must be increased from 0.65 to 0.89. To have the same effect on decision accuracy by enhancing the inventory monitoring subsystem, quantity on record accuracy must be increased from 0.37 to 0.64. Assuming the cost of increasing reorder

47

point accuracy and quantity on record accuracy by 1% are Pr and Pq respectively, management should choose the forecasting project if Pr(0.89 -- 0.65) < Pq(0.64 -- 0.37). Of course the assumption that Pr and Pq are constant is a simplification which probably does not exist in the real world; Pr and Pq probably increase with increases in the level of reorder point and quantity on record accuracy. However, the approach illustrated above, using the production function to determine the required changes in the MIS and comparing costs associated with developing or purchasing (via software packages) is still applicable wih this increased cost complexity. This is one example of how MIS costs can be combined with the production function to provide guidance for appropriate MIS investment. Using the microeconomic criterion which sets marginal product divided by marginal cost equal for all inputs, the optimal MIS investment can be determined for a desired decision accuracy. For example, based on the production function (9) and prices for reorder point accuracy, quantity on record accuracy and coverage of 0.40, 0.45 and 0.45 respectively, a desired decision accuracy of 0.71 should be obtained using the following levels of MIS inputs: Reorder point accuracy: Quantity on record accuracy: Quantity on record coverage:

0.63 0.86 0.33

If prices change then the optimal input levels also change. For example, if reorder point accuracy, quantity on record accuracy, and quantity on record coverage prices change to 0.45, 0.35, and 0.15 respectively, the same decision accuracy should be obtained using: Reorder point accuracy: Quantity on record accuracy: Quantity on record coverage:

0.59 0.81 0.50

The kind of analysis to determine optimal MIS investment levels can also be done in circumstances when the desired amount of decision accuracy changes. Thus for any cornbination of prices, marginal and total cost functions can be constructed where the cost of producing various levels of decision accuracy is determined based upon the optimal employment of MIS inputs.

Mukhopadhyay, Cooper--Impact of MIS on Decisions

48

7. CONCLUSIONS

returns to scale, etc. In addition, inter-industry comparisons can be made to identify the existThe discussion above took an ideal cast where ence of different decision making 'technologies'. decision making is well described by microAs described, there are significant benefits for economic production functions, where data on developing MIS effectiveness methods which information attributes and decision are avail- result from a microeconomic production view of able, etc. This will not be the case in many decision making in organizations. However, contexts. As described in Cooper [7], important these benefits depend upon the descriptive and assumptions underlying our microeconomic normative strength and validity of a decision approach include non-conflicting goals and making theory which is based upon this view. preferences as well as a relatively deterministic As such, much more work is required in order environment. These assumptions restrict the to develop and validate the theory. For exapplicability of our model to more structured ample, simulated and actual data from relatively decisions. In addition, the microeconomic structured operational and managerial control assumptions of constant production technology decisions such as those associated with other and continuous and repetitive production tend types of inventory control approaches, material to be less violated with more management or requirements planning approaches, and budget operational control types of decisions. Thus, variance analyses should be examined to confirm using Keen and Scott Morton's framework [20], their accord with microeconomic properties. management decision contexts such as inventory Also, to gain the full potential from the microordering, budgeting, and production scheduling economic referent, a function linking decisions are prime candidates for microeconomic to organization goals needs to be developed. production modeling. This will allow the direct impact of MIS upon Within these contexts, the full power of organization profit to be evaluated. microeconomic modeling may be used. As demonstrated above, this approach enables descriptive insight which is unavailable with ACKNOWLEDGEMENTS other approaches. The precise mathematical The authors gratefully acknowledgehelpful comments from functions provide a better understanding of the Jan Kmenta, Manfred Kochen, Peter GW Keen, Charles H effects various MIS attributes have upon each Kriebel, Thomas J Schriber, and an anonymous reviewer. other and upon the quality of decision making. Also as demonstrated above, this approach enables significant normative insight. CombinREFERENCES ing decision p r o d u c t i o n functions with M I S

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function

may

be

a

reasonable

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formation attributes, the degree of diminishing

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ADDRESSFOR CORRESPONDENCE:ProfessorT Mukhopadhyay, GraduateSchool of Business Administration, Carnegie Mellon University, Pittsburgh, PA 15213, USA.