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Holt, Modigliani, Muth, and Simons work and its role in therenaissance and evolution of operations management

Jaya Singhal, Kalyan Singhal *

Merrick School of Business, University of Baltimore, 1420 N. Charles Street, Baltimore, MD 21201, USA

Available online 17 July 2006

Abstract

Early work in aggregate production planning has evolved into a major business process known as sales and operations planning.

In the 1950s, a team led by Holt, Modigliani, Muth, and Simon, which also included Bonini and Winters, worked on aggregate

production planning and forecasting and published a series of papers and a book. The literature contains reports of at least 73

applications of their work in four companies and three application studies. Holt et al.s work and its visibility led to a renaissance of

the field of operations and supply chain management as we know it today and brought two paradigm changes in the domain and the

role of operations and supply chain management. First, seemingly unrelated and non-managerial individual functions started to

emerge as parts of an integrated system of managing production. Second, aggregate production planning brought to forefront the

central role of operations management by linking it with supply chains and other functions in the organization.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Aggregate planning; Capacity management; Evolution of operations management; Interdisciplinary; Manufacturing planning and

control; Sales and operations planning

1. Introduction: the genesis of Holt, Modigliani,

Muth, and Simons work

In the early 1950s, Charles C. Holt, Franco

Modigliani, John F. Muth, and Herbert A. Simon

(HMMS) began work on a project, Planning and

Control of Industrial Operations, at the Graduate

School of Industrial Administration (GSIA) at the

Carnegie Institute of Technology. William W. Cooper,

who was also at GSIA, initiated the project, and the

Office of Naval Research supported it. Our goal in this

paper is to document the early work of HMMS in

aggregate production planning and to describe how this

work has evolved into a major business process known

as sales and operations planning.

Holt had four degrees in electrical engineering and

economics from MITand the University of Chicago, and

he wanted to study how instability in the economy was

related to firms management of their inventories.

Modigliani (awarded the Nobel Prize in 1985) received

a J.D. in 1939 from the University of Rome and a D.S.S.

in 1944 from the New School of Social Research. He had

worked on production smoothing. Simon (awarded the

Nobel Prize in 1978) received his Ph.D. from the

University of Chicago in 1943. A cognitive scientist,

economist, organizational theorist, and political scientist,

he had worked on the dynamics of inventory feedback

on production rates. He was interested in understanding

how managers made their decisions, and he wanted to

model their behavior. Muth had an undergraduate degree

in industrial engineering and was a graduate student at

the GSIA where he earned a Ph.D. in 1962. He published

a paper on rational expectations in 1961, derived from

www.elsevier.com/locate/jomJournal of Operations Management 25 (2007) 300309

* Corresponding author. Tel.: +1 410 837 4976.

E-mail address:[email protected](K. Singhal).

0272-6963/$ see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2006.06.003
mailto:[email protected]://dx.doi.org/10.1016/j.jom.2006.06.003http://dx.doi.org/10.1016/j.jom.2006.06.003mailto:[email protected]

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the work on the project, Planning and Control of

Industrial Operations. Robert Lucas built on Muths

work and won a Nobel Prize in 1995.

According toSimon (1978a), the goal of the GSIA,

which was newly established in 1949, was to place

business education on a foundation of fundamental

studies in economics and behavioral science. Simonnoted that the work on the HMMS project was a part of

this effort, and that fortunately the computer and the

new management science techniques had just started

appearing.

1.1. Production management just before the HMMS

work

Commenting on the state of operations and supply

chain management in the early 1950s, Muth (2004a)

noted, Textbooks at the time focused on the EOQformula, Gantt chart displays, punched card systems for

dispatching, moving average forecasts, and thats about

it. In the textbook,Analysis of Production Management,

Bowman and Fetter (1957)(then assistant professors at

MIT) essentially covered hypothesis testing, various

charts (Gantt charts, activity charts, and so forth),

mathematical programming, statistical control, sampling

inspection, industrial experimentation, total value ana-

lysis, Monte Carlo analysis, and equipment investment

analysis.Holt (2002)said that a few years before they

started work on the project, a consulting firm had sold Asimple lot size formula to Westinghouse Electrical

Corporation for something like $100,000. It presum-

ably included the consultants time. The HMMS work

was a turning point in the direction of operations

management.

2. The HMMS model and its solution

The HMMS team started the project by interviewing

managers at about 15 companies. According to Holt

(2002), the managers initially denied that they had any

problems, but after persistent questioning, the teamfound that the managers were simply going from one

crisis to another: inadequate forecasts of wildly

fluctuating demand for thousands of products, huge

fluctuations between overtime and idle time, and gross

incompatibilities between aggregate production plans

and plans for individual products.

The team finally focused on an application

oriented context (Cooper, 2004) for scheduling paint

production at the Springdale plant of the Pittsburgh

Glass Corporation (now PPG Industries) because it

had the generic system problem in a fairly simple

form. Augier and March (2004) observed that the

project was essentially a study of decision making under

uncertainty.

2.1. The HMMS model

The core of HMMS work was a linear-quadraticmodel of aggregate production planning. HMMS

submitted an article based on their findings to

Operations Research, which rejected it on the basis

that the work was not operations research (Cooper,

2004). HMMS then published their findings in two

papers in the second volume ofManagement Science

(Holt et al., 1955, 1956). Several other papers

concerning the Planning and Control of Industrial

Operations project accompanied these two, for

example, Simon (1955, 1956), Bonini (1958), Muth

(1960), and Winters (1960). Holt et al. (1960) thenpublished a book that covered these papers and the two

in Management Science. Bonini and Winters, Ph.D.

students at GSIA at the time, also contributed to the

book. Several other papers on parts of the project and

some derived from it followed (Muth, 1961; Holt and

Modigliani, 1961; Holt, 1962; Winters, 1962).

The Holt et al. model (1960) consists of selecting

production and workforce levels in each ofTperiods so

as to satisfy ordered shipments while minimizing the

sum of the costs over theTperiods. LetPt,Wt,It, andSt

represent production volume, workforce level, end-of-period inventory, and ordered shipment for periodt, and

letI0and W0represent the specified values of the initial

inventory and the initial workforce. The cost in periodt

consists of the following components:

Regular payroll costs : C1Wt C13

Hiring and layoff costs : C2Wt Wt1 C112

Overtime costs :

C3Pt

C4W

t

2

C

5Pt

C6W

tC

12PtW

t

Inventory-related costs : C7It C8 C9St2

The model was thus formulated as

ZXT

t1

C13 C1 C6Wt

C2Wt Wt1 C112 C3Pt C4Wt

2

C5Pt C12PtWt C7It C8 C9St2

(1)

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Subject to

Pt St It It1 (2)

C1, C2, . . ., C12 are constants. Holt et al. used curve

fitting to estimate the values of cost coefficients.

2.2. A solution of the model

Holt et al. (1960, pp. 9495)derived the following

recursive equations to solve the model:

PtC10

C14 C15Wt1 C17Wt C15Wt1 (3)

It C3

C7Pt1 Pt

C14

2C7Wt1 Wt C8 C9St

(4)

where t= 1, 2, . . ., (T1); C10= C1C6;C14= 2C3C4C12; C15= 2C2/C14; C16 2C3C24=C14;C17= C16+ 2C15.

Holt et al. focused on an infinite planning horizon

with stationery costs. Modigliani and Muth constructed

efficient computational algorithms. Holt and Simon

derived the rules for optimal decisions under certainty.

Via a series of tedious linear transformations, they

derived the following two linear decision rules (LDRs)

for the first period:

P1 u1 u2I0u3W0XT

t1

tSt

W1 u4 u5I0u6W0XT

t1

mtSt

whereu1,u2,u3,u4,u5,u6,wt, andmt(t= 1, 2, . . .,T) are

functions of the cost coefficients. The infinite series can

be truncated after an appropriate number (T) of periods.

Bonini (1958) describes a method for disaggregating the

aggregate plan to determine production levels for indi-

vidual products. For more recent alternate approaches to

solving the Holt et al. model, see Singhal (1992)and

Singhal and Singhal (1996).

2.3. Certainty-equivalence

Simon (1956)proved a certainty-equivalence theo-

rem so that the Holt et al. model could also be applied

under conditions of uncertainty. In his Nobel memorial

lecture, Simon (1978b) pointed out that under

uncertainty about future sales, only the expected values,

and not higher moments, of the probability distributions

enter into the decision rule. Recently, Muth (2004b) said

that the results of the theorem were important because

they showed that the LDRs were appropriate for a

quadratic criterion function under uncertainty and that

the expected value was a sufficient statistic. Muth added

that linear programming or other techniques did not

share this property. Holt (2002) noted that Simon

proved that the solution of this model was optimal

both statically and dynamically, and that the theoremwas a powerful tool for dealing with large dynamic

systems under uncertainty.

Holt et al. (1960, p. 123)noted the following specific

implications of the certainty-equivalence theorem for

the linear quadratic model: When costs are quadratic,

the only datum about future sales that enters into the

optimal decision rule is the expected value; that is, an

average estimate of what the sales for each relevant

future time period are likely to be. The probable

dispersion of actual future sales around this predicted

average and the finer characteristics of the probabilitydistribution of sales are irrelevant. The procedure

involves using a sales forecasting method that does not

consistently overestimate or underestimate sales. In

other words (Holt et al., 1956, p. 176), A forecasting

method should be used whose expected error is zero, or

more loosely, whose algebraic average error is zero.

3. Industrial applications of the HMMS model

Holt et al. (1960, pp. 1536)reported 73 applications

in four companies: 70 factories of Pittsburgh GlassCorporation, Westinghouse Electrical Corporation (a

manufacturer of electrical products), a company in a

process industry, and a manufacturer of cooking

utensils. They also reported two application studies,

for a fiber company and for an ice cream company.Lee

and Khumawala (1974) also reported an application

study in the capital goods industry. We summarize these

applications.

3.1. Seventy factories of Pittsburgh Glass

Corporation (now PPG industries)

The factory, where the model was originally tested

(nicknamed as Paint Factory in Holt et al.s publica-

tions), had nearly 1000 products. The implementation

originally covered only 10% of the sales volume of the

factory. With encouraging results in reducing back

orders, inventory levels, and fluctuations in aggregate

production, the factory first extended the model to cover

25% of the sales volume and then gradually to 100%.

Holt (2002)noted, after the new methods for the paint

company applications were fully developed and tested,

the paint company had difficulty in achieving a profit

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impacting payoff. That problem was ultimately solved,

and the system extended to all 70 of the paint companys

factories. But that did not occur until a few years later,

when a new quantitatively oriented manager was given

specific responsibility for implementing the new

system.

Muth (2004b) noted, It was discovered that thefactory demand fluctuations were generated by the

companys own distribution system. Final demand

was fairly flat. This led to something like distribution

requirements planning for the production schedule.

The factory demand fluctuations clearly appear to

have resulted from the bullwhip effect.Gordon (1966)

also reported on the state of implementation at the

original paint factory several years after the initial

study. According to him, the foremen were using the

results of the decision rules only if the rules agreed

with their judgment; otherwise they went by theirjudgment and did so in half of the cases. The

management meanwhile believed that the rules were

being used except in the odd case when judgment

indicated that they should be overruled. Gordon

further reported that at a later date, the company

centralized the calculations of decision rules. Then at

some point, the finished goods inventory increased

to alarming levels. An investigation showed that

the decision rules for reducing the workforce were

not followed because it was against the company

policy to lay off workers. Gordon noted, This meantthat each period the work force rule was indicating a

reduction in the work force; the production rule,

attempting to minimize costs given the present work

force level but anticipating layoff, called for some

production for the excess work force. The rules

were interactive, but in this case the interaction had

been eliminated.

3.2. Other applications

3.2.1. Westinghouse Electrical Corporation (an

electrical manufacturer)One of Westinghouses factories supplied about 500

different models of transformers to 30 warehouses.

Operations research specialists and the personnel of the

transformer department jointly developed a mathema-

tical model of the system. The model was similar to the

one used for the paint factory. For 3 months, the new and

old systems operated in parallel. The benefits included

fewer stock-outs, a reduction of 20 percent (or about $

three million in 1960 money) in inventory, improved

service to warehouses as reflected in a 50% reduction in

long distance calls that were normally service related,

and facilitation of design changes since the system

could control the inventory of both the old and the new

products.

3.2.2. A company in a process industry

In the process company, the decision rules of the

HMMS model determined the aggregate production andworkforce levels. The analysts revised the rules based

on managers judgment, and the company used its

customary method to allocate production to indivi-

dual products. The decision rules required smaller

changes in the workforce than the traditional method.

The managers, however, produced more than the rules

recommended, but when the inventory started to build

up, their confidence in the decision rules increased.

3.2.3. A manufacturer of cooking utensils

The manufacturer produced approximately 1000products. The sales department made higher forecasts

than those made by the production department, and the

conflict led to fluctuations in production and employ-

ment. Their joint participation in the HMMS study led

to greater agreement on the desirable levels of

inventory. The system developed for this company

had an additional feature (Holt et al., 1960, p. 33):

When the inventory of a product fell low enough, a

new production lot was triggered. If the distribution of a

product among the warehouses had remained in

balance, the total inventory level was allowed to falllower than if the distribution became unbalanced and

one or several warehouses were in danger of stock-

outs. Winters (1958) described this trigger. A

simulation study with different stock-out costs showed

that the company could reduce warehouse inventory by

40% while decreasing stock-outs. These results

encouraged the company to try the new system for a

sample of 26 representative products.

3.2.4. A fiber manufacturer

For a fiber manufacturer, the linear decision rule

gave decisions that were no better than thosepreviously made by management (Holt et al., 1960,

p. 34) because with large jumps in the workforce and

production level, the quadratic cost functions were not a

good fit.

3.2.5. An ice cream plant

van der Velde (1958) worked on applying the Holt

et al. model at an ice cream plant as a part of his

Masters Thesis at MITunder the supervision of Edward

H. Bowman. During a 2-year study period, the LDRs led

to savings of the order of one percent.Holt et al. (1960)


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compared this study with that for the original paint

factory and noted that although the performances of

the decision rules were somewhat similar in the two

cases, the respective rules depended upon the cost

functions (Holt et al., 1960, p. 36). As an example, Holt

et al. pointed out that a company would have small

inventory fluctuations if the costs of inventory fluctua-tions were high, and vice versa.

3.2.6. A factory in the capital goods industry

The capital goods factory was a job-shop manu-

facturing facility, and the study was done with the

cooperation and encouragement of its management.

The factory assembled finished goods for inventory and

to fill customer orders, and manufactured parts for

inventory. Lee and Khumawala (1974) developed a

corporate simulation model that closely followed the

accounting system and the material flow through theorganization. They evaluated four models, including the

Holt et al. model. They found that with imperfect

forecasts, application of the Holt et al. model would

have increased the factorys annual profits by more than

nine percent to $ 4,821,000 compared to its actual profit

of $ 4,420,000.

4. The role of HMMS work in renaissance of

operations management and of business

education

4.1. Production as the core of economics

The production of goods and services has been the

core of all economic activities since the dawn of

civilization. It remained the core of the field of

economics as modern economics developed with the

Industrial Revolution and with the works of Adam

Smith (17231790) published in 1776, of Thomas R.

Malthus (17661834) published in 1798, of David

Ricardo (17721823) published in 1817, and of Karl

Marx (18181883) published in 1848, 1867, 1885, and

1894. However, since the beginning of the 20th century,many new issues related to manufacturing surfaced.

They were distinct from mainstream economics and

constituted an emerging field called production man-

agement.

4.2. A renaissance

Holt et al.s work and its visibility led to a renaissance

of the field of operations management, as we know it

today. The issues of aggregate production planning and

disaggregation that Holt et al. addressed represent the

primarylinks between strategic andtactical decisions in a

firm. Aggregate production planning links operations

with strategy. It plays a key role in enterprise resource

planning and organizational integration by linking

operations with accounting, distribution, finance, human

resource management, and marketing. It also drives

interorganizational coordination by linking operationswith both upstream and downstream supply chains. It has

several specific roles:

Aggregate production planning serves as a majorvehicle for implementing manufacturing strategy

because it concerns trade-offs between cost, flex-

ibility, and delivery time.

It serves as an input to, and is constrained by, long-range capacity planning. Therefore, it plays a role in

investments in physical facilities.

It is a mechanism for implementing supply chainstrategy since it mitigates the impact of the bullwhip

effect and determines product mix, material require-

ments, levels of procurement, the flow of products in

the downstream supply chain, and the timing of order

fulfillment.

It is the primary vehicle for coordinating multiplantoperations.

It determines the levels of accounts receivable andaccounts payable and also the short-term to medium-

term requirements for cash to support operations and

inventory. It sets the levels of employment, the number of shifts,and the utilization of the workforce.

The renaissance brought two paradigm changes in

the domain and the role of operations and supply chain

management. First, what had seemed unrelated and

non-managerial individual functions, such as hypoth-

esis testing, industrial engineering and quantitative

tools, statistical quality control, sampling inspection

and industrial experimentation, value analysis, and

equipment-investment analysis, started to emerge as

parts of an integrated system of managing production.Second, the focus on the issues related to aggregate

production planning brought to forefront the central role

of operations management in linking other functions,

such as accounting, finance, human resource manage-

ment, information systems, marketing, and strategy.

Commenting on the interdisciplinary nature of

aggregate production planning, (Silver, 1972, p. 15),

observed, The production supervisor desires long runs

of individual items so as to reduce production costs; the

marketing personnel wish to have a substantial inventory

of a wide range of finished goods; those concerned with


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labor relations desire a stable work force; finally, the

comptroller generally wants as low inventory as possible.

. . .Therefore, a cross-departmental (or systems) appro-

ach to the solution of the problem is essential.

The renaissance in operations management paral-

leled other changes in business schools. Geoffrion

(2003) pointed out, The emergence of modernbusiness schools dates from about 1959, when the

Carnegie and Ford foundations issued separate reports

lamenting the lack of rigor and research in US business

schools. The developments at the GSIA at the

Carnegie Institute of Technology during the 1950s

had a major influence on the two reports. Thus, Holt

et al.s work also played a role in renaissance of business

education as we know it today.

4.3. A standard of excellence to judge our current

research

Sprague et al. (1990) used Holt et al.s work as an

exemplar in reviewing the research on production

planning, inventory management, and scheduling, and

they observed, The translation of demand for a product

into load on operational resources constitutes a critical

problem that is never solved. The astute manager seeks

a process by which the ever present problem can be

solved, rather than a specific solution (p. 297). They

suggested that the HMMS problem definition and

methodology of attack were exemplary models ofour research questions and attempted to find

solutions to the problems of practicing managers,

and that the vocabulary and research methodology

HMMS developed were seminal and remain a

standard of excellence by which current research

could be judged.

5. Alternate approaches to aggregate production

planning

Bowman (1963) used regression analysis on man-

agers past performance to develop decision rules foraggregate planning. Bowmans work had an impact far

beyond operations management, particularly in artifi-

cial intelligence, but a detailed discussion is beyond the

scope of our paper. Jones (1967) developed two

heuristic rules, one for size of workforce and another

for production rate, and tested both his model and the

HMMS model using the Harvard Business Schools

Management Simulation Game. Taubert (1968) con-

verted the HMMS model into a 20-dimension response

surface and used a search decision rule to find the

solution. His model eliminated all restrictions imposed

by linear or quadratic cost models.Lee and Khumawala

(1974) studied a factory in the capital goods industry

and used simulation to compare the HMMS model with

the Bowmans, Jones, and Tauberts models. They

found that all four models performed credibly with

perfect forecasts and that the Taubert and HMMS

models provided the best results. They further foundthat, with imperfect forecasts, the Taubert model

performed the best, closely followed by the Jones,

HMMS, and Bowman models.

Hax and his colleagues (Hax and Meal, 1975; Bitran

and Hax, 1977; Bitran et al., 1981, 1982) developed

hierarchical production-planning systems. Hax and

Candea (1984, p. 393)noted, Early motivation for this

approach can be found in the pioneering work of Holt,

Modigliani, Muth, and Simon. Hax and his colleagues

grouped production management decisions in three

broad categories (Hax and Candea, 1984, p. 393):

Policy formulations, capital investment decisions, anddesign of physical facilities.

Aggregate production planning.Detailed production scheduling.

Hax and Candea (1984, p. 393)suggested that these

three categories of decisions differ markedly in terms

of level of management responsibility and interaction,

scope of the decision, level of detail of the required

information, length of the planning horizon needed toassess the consequences of each decision, and degree of

uncertainties and risks inherent in each decision.Hax

and Meal (1975) noted, It is only natural, therefore,

that a system designed to support the overall planning

process should correspond to the hierarchical structure

of the organization. Bradley et al. (1977) described

two real-world applications of hierarchical production

planning, one in the continuous manufacturing process

in the aluminum industry (Chapter 6) and another in a

naval job shop (Chapter 10). Ritzman et al. (1979)

described related works.

The developments during the last 50 years havemade it easier and simpler to plan aggregate production.

The authors of current textbooks describe the use of

spreadsheets and optimization done with the Excel

Solver.

6. Conclusions: where we are now

The problem Holt, Modigliani, Muth, and Simon

addressed is now viewed in practice as sales and

operations planning. It plays a pivotal role in integrating

the operations, marketing, and finance functions.


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Duringthe last 50 years, the integrationof these functions

has been greatly facilitated by the availability of

optimization software and enterprise resource planning

systems and the advent of the Internet. It has made a

profound impact on the evolution of operations and

supply chain management and yield (revenue) manage-

ment. The scope of integration now also includes suchissues as detailed scheduling (Dawande et al., 2006),

delivery guarantees (Rao et al., 2005; Boyaci and Ray,

2006), interorganizational coordination (Buhman et al.,

2005; Ferguson and Ketzenberg, 2006; Majumdar and

Ashok Srinivasan, 2006), manufacturing flexibility stra-

tegies (Ketokivi, 2006), and the role of pricing. The

evolving role of integration on a range of topics has been

widely covered in the literature. Five review papers

(Boyer et al., 2005; Kleindorfer et al., 2005; Kouvelis

et al., 2005; Krishnan and Loch, 2005; Schroeder et al.,

2005) describe a number of papers that address variousdimensions of integration.

Lee and Ng (1997) pointed out that the interdisci-

plinary perspective, combined with the benefits of

interorganizational coordination, has been primarily

responsible for the new paradigm in supply chain

management and for the tremendous excitement and

top management attention on this subject. They noted,

It seems that the distinction between the so-called

supply chain management today and traditional opera-

tions management lies in two dimensions of integration

and coordination: organizational integration and flowcoordination. . . .Companies are also overcoming the

functional boundaries, so that the different disciplines

and functions, such as manufacturing, distribution,

marketing, accounting, information, and engineering,

are better integrated.

In the rapidly growing area of yield and revenue

management, the models for matching short-term

supply and demand have become a fundamental

component of the daily operations of manufacturing

and service companies because managers can effec-

tively manipulate price to encourage or discourage

demand in the short run (Bitran and Caldentey, 2003).Geoffrion (2002)pointed out that the digital economy

was facilitating dynamic pricing (better and faster

changes in posted prices in response to market

conditions, costs, demand, inventory, and competitors

behavior and better price discrimination through better

real-time segmentation) and added, This seems to be a

point of convergence of Marketing and Operations

Management as management disciplines. Pricing is

becoming less like a class of decisions made

episodically by marketing specialists and more like

an operational process in which pricing decisions are

dynamically integrated with the traditional steps of the

online sales process and also with operating data and

decisions that have been traditional OM concerns. The

applications of models in industry have also been

driving academic research (Gallego and Van Ryzin,

1997; Baker and Collier, 2003).

The integrated approach to planning is becomingmore and more a standard practice in companies. It is

also facilitating and is being facilitated by globalization

and the emergence of distributed supply chains. Holt,

Modigliani, Muth, and Simon were clearly way ahead

of their time in understanding the importance of this

integrated approach to planning.

Acknowledgements

We are grateful to Charles Holt and Jack Muth for

sharing a number of ideas with us. We also thank LindaSprague for several useful suggestions.

Appendix A. Biographies of Holt, Modigliani,

Muth, and Simon

A.1. Charles C. Holt (1921till date)

Charles Holt is professor emeritus at the Red

McCombs School of Business of the University of

Texas at Austin. He earned his B.S. and M.S. degrees in

electrical engineering from MIT and M.A. and Ph.D.(1955) in economics from the University of Chicago. He

has held positions at the MIT Servo Lab, the Carnegie

Institute of Technology, the London School of Econom-

ics, the University of Wisconsin, and the Urban Institute.

Holts research concerns a wide range of topics,

including automatic control, computer simulation,

control theory, decision support systems for unstruc-

tured problems, macroeconomic theory, and operations

research. He worked with Winters to develop the Holt

Winters exponential smoothing models of forecasting

that are widely used in business forecasting. They are

embedded in almost all forecasting software and taughtin almost all business programs.

Holt led the Holt, Modigliani, Muth, and Simon

team. In a 2002 article in Operations Research, he

observed, Looking back all members of the team

would likely agree that their GSIA years were among

the most interesting and exciting of their careers.

A.2. Franco Modigliani (19182003)

Franco Modigliani was born in Rome, Italy. He was

educated at the Sorbonne and the University of Rome,


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where he earned a Doctor Juris degree in 1939. The

same year, he moved to the United States and joined the

New School for Social Research. One of his mentors

there was Jacob Marschak who later worked with

Kenneth Arrow and Theodore Harris to lay the

foundations of inventory theory under uncertainty.

Modigliani earned his D.S.S. there in 1944 and taughtthere from 1944 to 1949. He was a research consultant

to the Cowles Commission at the University of Chicago

from 1949 to 1952.

Modigliani moved to the Carnegie Institute of

Technology in 1952 where he collaborated with

Charles Holt, John Muth, and Herbert Simon on

production smoothing and with Merton Miller on the

effect of financial structure and dividend policy on

the market value of a firm. During this period, he

also worked with Richard Brumberg, a Ph.D. student

at John Hopkins University, to lay the foundations ofwhat later became the life cycle hypothesis of saving.

In 1960, he moved to MIT where he remained for the

rest of his career.

Modigliani was awarded the Nobel Prize in

economics in 1985 for his pioneering analyses of

savings and financial markets. He also served as

president of the International Economic Association,

the Econometric Society, the American Economic

Association, and the American Finance Association.

A.3. John F. Muth (19302005)

John Muth had an undergraduate degree in industrial

engineering, and he earned his Ph.D. from the Graduate

School of Industrial Administration at the Carnegie

Institute of Technology in 1962. He was a visiting

lecturer at the University of Chicago in 19571958, and

he spent 19611962 at the Cowles Foundation at Yale

University. He was a research associate (19561959), an

assistant professor (19591962), and an associate

professor (19621964) at the Carnegie Institute of

Technology. He served on the faculty of Michigan State

University from 1964 to 1969 and on the faculty ofIndiana University from 1969 until his retirement in

1994.

Muth made notable contributions to learning theory

and was one of the first to study artificial intelligence.

While still a Ph.D. student, he published Rational

expectations and the theory of price movements in

1961. Robert Lucas built on Muths work and won a

Nobel Prize in 1995. Muth is known as the father of

rational expectation theory, which changed almost

every area of economic research. Economist Ike

Branon wrote, While he (Muth) would have appre-

ciated the recognition of a Nobel Prize, Muth was a shy

gentleman who would have been uncomfortable with

the notoriety that comes with the prize. He was much

more at home at the various pubs in downtown

Bloomington, where he was not averse to holding his

office hours. [http://www.cato.org/pub_display.php?-

pub_id=5362].

A.4. Herbert A. Simon (19162001)

Herbert Simon earned a B.S. from the University of

Chicago in 1936 and Ph.D. from the University of

California at Berkeley in 1942. He then taught at the

Illinois Institute of Technology and participated in the

seminars of the Cowles Commission for Research in

Economics at the University of Chicago along with

Jacob Marschak and several future Nobel Laureates:

Kenenth Arrow, Miton Friedman, Tjalling Koopmans,and Franco Modigliani. His serious participation in

economic analysis came when he participated with

Marschak in a study of the prospective economic effects

of atomic energy. He moved to the Carnegie Institute of

Technology in 1949 and worked there for the rest of his

career.

Simon was a quintessential renaissance man who

made major contributions to a number of fields: applied

mathematics, business and public administration,

economics, information sciences, operations research,

philosophy, political science, and psychology. Hecoined the terms satisficing and bounded rationality

to explain human behavior and decision-making

processes. He worked with Alan Newell to lay the

foundations of artificial intelligence. In his book,

Sciences of the Artificial, he used the natural science

analog to create a scientific framework for designing

and analyzing social systems. In 1978, Simon was

awarded the Nobel Prize for his pioneering research

into the decision-making process within economic

organizations.

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