Journal of Operations Management 25 (2007) 300–309
www.elsevier.com/locate/jom
Holt, Modigliani, Muth, and Simon’s work and its role in the
renaissance 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 Simon’s 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.
* 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
Holt had four degrees in electrical engineering and
economics from MIT and 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
J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
the work on the project, ‘‘Planning and Control of
Industrial Operations’’. Robert Lucas built on Muth’s
work and won a Nobel Prize in 1995.
According to Simon (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’’. Simon
noted 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 EOQ
formula, Gantt chart displays, punched card systems for
dispatching, moving average forecasts, and that’s 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 analysis, 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 ‘‘A
simple lot size formula to Westinghouse Electrical
Corporation for something like $100,000’’. It presumably 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 team
found 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
301
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-quadratic
model 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 of Management 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) then
published 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 of T periods so
as to satisfy ordered shipments while minimizing the
sum of the costs over the T periods. Let Pt, Wt, It, and St
represent production volume, workforce level, end-ofperiod inventory, and ordered shipment for period t, and
let I0 and W0 represent the specified values of the initial
inventory and the initial workforce. The cost in period t
consists of the following components:
Regular payroll costs :
Hiring and layoff costs :
C1 W t þ C13
C 2 ðW t W t1 C 11 Þ2
Overtime costs :
C3 ðPt C4 W t Þ2 þ C5 Pt C6 W t þ C 12 Pt W t
Inventory-related costs :
C 7 ðI t C8 C9 St Þ2
The model was thus formulated as
Z¼
T
X
½ðC 13 þ C1 C6 ÞW t
t¼1
þ C 2 ðW t W t1 C 11 Þ2 þ C 3 ðPt C4 W t Þ2
þ C 5 Pt þ C 12 Pt W t þ C7 ðI t C 8 C 9 St Þ2 (1)
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J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
Subject to
Pt St ¼ I t I t1
(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. 94–95) derived the following
recursive equations to solve the model:
C 10
Pt ¼
C 15 W tþ1 þ C 17 W t C 15 W t1
(3)
C 14
It ¼
C3
C 14
þ C 8 þ C 9 St
C 7 ðPtþ1 Pt Þ 2C7 ðW tþ1 W t Þ
(4)
where t = 1, 2, . . ., (T 1); C10 = C1 C6;
C14 = 2C3C4 C12; C15 = 2C2/C14; C16 ¼ 2C3 C42 =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 ¼ u 1 þ u 2 I 0 þ u 3 W 0 þ
T
X
’ t St
t¼1
W 1 ¼ u 4 þ u 5 I 0 þ u6 W 0 þ
T
X
m t St
t¼1
where u1, u2, u3, u4, u5, u6, wt, and mt (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 individual 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 theorem 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 theorem
‘‘was 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 probability
distribution 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. 15–36) reported 73 applications
in four companies: 70 factories of Pittsburgh Glass
Corporation, 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 publications), 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
J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
impacting payoff. That problem was ultimately solved,
and the system extended to all 70 of the paint company’s
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 the
factory demand fluctuations were generated by the
company’s 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 their
judgment 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 meant
that 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’’.
303
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 and
workforce levels. The analysts revised the rules based
on managers’ judgment, and the company used its
‘‘customary method’’ to allocate production to individual 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 1000
products. The sales department made higher forecasts
than those made by the production department, and the
conflict led to fluctuations in production and employment. 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 fall
lower than if the distribution became unbalanced and
one or several warehouses were in danger of stockouts’’. 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. Other applications
3.2.1. Westinghouse Electrical Corporation (an
electrical manufacturer)
One of Westinghouse’s factories supplied about 500
different models of transformers to 30 warehouses.
Operations research specialists and the personnel of the
transformer department jointly developed a mathematical 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,
3.2.4. A fiber manufacturer
For a fiber manufacturer, the linear decision rule
‘‘gave decisions that were no better than those
previously 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
Master’s Thesis at MIT under 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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J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
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 fluctuations were high, and vice versa.
3.2.6. A factory in the capital goods industry
The capital goods factory was a ‘‘job-shop manufacturing 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 the
organization. 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 factory’s 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 (1723–1790) published in 1776, of Thomas R.
Malthus (1766–1834) published in 1798, of David
Ricardo (1772–1823) published in 1817, and of Karl
Marx (1818–1883) 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 management.
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
primary links between strategic and tactical 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 operations
with both upstream and downstream supply chains. It has
several specific roles:
Aggregate production planning serves as a major
vehicle for implementing manufacturing strategy
because it concerns trade-offs between cost, flexibility, and delivery time.
It serves as an input to, and is constrained by, longrange capacity planning. Therefore, it plays a role in
investments in physical facilities.
It is a mechanism for implementing supply chain
strategy since it mitigates the impact of the bullwhip
effect and determines product mix, material requirements, 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 multiplant
operations.
It determines the levels of accounts receivable and
accounts payable and also the short-term to mediumterm 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 hypothesis 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 management, 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
J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
labor relations desire a stable work force; finally, the
comptroller generally wants as low inventory as possible.
. . .Therefore, a cross-departmental (or systems) approach to the solution of the problem is essential’’.
The renaissance in operations management paralleled other changes in business schools. Geoffrion
(2003) pointed out, ‘‘The emergence of modern
business 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 of
our 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 managers’ past performance to develop decision rules for
aggregate planning. Bowman’s work had an impact far
beyond operations management, particularly in artificial 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 School’s
Management Simulation Game. Taubert (1968) converted 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
305
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 Bowman’s, Jones’, and Taubert’s 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 found
that, 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, and
design 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 to
assess 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 have
made 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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J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
During the last 50 years, the integration of 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) management. The scope of integration now also includes such
issues 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 strategies (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 various
dimensions of integration.
Lee and Ng (1997) pointed out that the interdisciplinary 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 operations management lies in two dimensions of integration
and coordination: organizational integration and flow
coordination. . . .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 effectively 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 becoming
more 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 Linda
Sprague for several useful suggestions.
Appendix A. Biographies of Holt, Modigliani,
Muth, and Simon
A.1. Charles C. Holt (1921–till 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 Economics, the University of Wisconsin, and the Urban Institute.
Holt’s research concerns a wide range of topics,
including automatic control, computer simulation,
control theory, decision support systems for unstructured 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 taught
in 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 (1918–2003)
Franco Modigliani was born in Rome, Italy. He was
educated at the Sorbonne and the University of Rome,
J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
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 taught
there 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 of
what 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 (1930–2005)
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 1957–1958, and
he spent 1961–1962 at the Cowles Foundation at Yale
University. He was a research associate (1956–1959), an
assistant professor (1959–1962), and an associate
professor (1962–1964) at the Carnegie Institute of
Technology. He served on the faculty of Michigan State
University from 1964 to 1969 and on the faculty of
Indiana 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 Muth’s 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-
307
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 (1916–2001)
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. He
coined 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’’.
References
Augier, M., March, J.G., 2004. Herbert A. Simon, scientist. In:
Augier, M., March, J.G. (Eds.), Models of a Man: Essays in
Memory of Herbert A. Simon. The MIT Press, Cambridge, MA,
pp. 3–32.
Baker, T.K., Collier, D.A., 2003. The benefits of optimizing prices to
manage demand in hotel revenue management systems. Production and Operations Management 12, 502–518.
Bitran, R., Caldentey, R., 2003. An overview of pricing models for
revenue management. Manufacturing and Service Operations
Management 5, 203–229.
Bitran, G.R., Haas, E.A., Hax, A.C., 1981. Hierarchical production
planning: a single stage system. Operations Research 29, 717–743.
308
J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
Bitran, G.R., Haas, E.A., Hax, A.C., 1982. Hierarchical production
planning: a single stage system. Operations Research 30, 232–
251.
Bitran, G.R., Hax, A.C., 1977. On the design of hierarchical production planning systems. Decision Sciences 8, 28–54.
Bonini, C.P., 1958. Decision rules for buffer inventories. Management
Science 4, 457–471.
Bowman, E.H., 1963. Consistency and optimality in managerial
decision making. Management Science 9, 310–321.
Bowman, E.H., Fetter, R.B., 1957. Analysis for Production Management. Irwin, Homewood, IL.
Boyaci, T., Ray, S., 2006. The impact of capacity costs on product
differentiation in delivery time, delivery reliability, and price.
Production and Operations Management 15, 179–197.
Boyer, K.K., Swink, M., Rosenzweig, E.D., 2005. Operations strategy
research in Production and Operations Management. Production
and Operations Management 14, 442–449.
Bradley, S.P., Hax, A.C., Magnanti, T.L., 1977. Applied Mathematical
Programming. Addison-Wesley, Reading, MA.
Buhman, C., Kekre, S., Singhal, J., 2005. The future of operations
management research: establishing the enterprise networks. Production and Operations Management 14, 493–513.
Cooper, W.W., 2004. Memorial to Herbert A. Simon. In: Augier, M.,
March, J.G. (Eds.), Models of a Man: Essays in Memory
of Herbert A. Simon. The MIT Press, Cambridge, MA, pp.
67–74.
Dawande, M., Geismar, H.N., Hall, N.G., Srikandarajah, C., 2006.
Supply chain scheduling: distribution systems. Production and
Operations Management 15, 243–261.
Ferguson, F., Ketzenberg, M.E., 2006. Information sharing to improve
retail product freshness of perishables. Production and Operations
Management 15, 57–73.
Gallego, G., Van Ryzin, G., 1997. A multiproduct dynamic pricing
problem and its application to network yield management. Operations Research 45, 24–41.
Geoffrion, A.M., 2002. Progress in operations management. Production and Operations Management 11, 92–100.
Geoffrion, A.M., 2003. Quoted in P. Horner, The last word. OR/MS
Today, p. 72.
Gordon, J.R.M., 1966. A multi-model analysis of an aggregate
scheduling decision. Ph.D. Dissertation. MIT.
Hax, A.C., Candea, D., 1984. Production and Inventory Management.
Prentice-Hall, Englewood Cliffs, NJ.
Hax, A.C., Meal, H.C., 1975. Hierarchical integration of production
planning and scheduling. TIMS Studies in Management
Science, Logistics, vol. 1. North Holland/American Elsevier,
pp. 53–79.
Holt, C.C., 1962. Linear decision rules for economic stabilization and
growth. The Quarterly Journal of Economics 76, 20–45.
Holt, C.C., 2002. Learning how to plan production, inventories, and
work force. Operations Research 50, 96–99.
Holt, C.C., Modigliani, F., 1961. Firm cost structures and the dynamic
responses of inventories, production, work force, and orders to
sales fluctuations. In: Inventory Fluctuations and Economic Stabilization, Part II, Joint Economic Committee of the Congress of
the United States.
Holt, C.C., Modigliani, F., Muth, J.F., 1956. Derivation of a linear
decision rule for production and employment. Management
Science 2, 159–177.
Holt, C.C., Modigliani, F., Muth, J.F., Simon, H.A., 1960. Planning
Production, Inventory and Work Force. Prentice Hall, Englewood
Cliffs, NJ.
Holt, C.C., Modigliani, F., Simon, H.A., 1955. Linear decision rule for
production and employment scheduling. Management Science 2,
1–30.
Jones, C.H., 1967. Parametric production planning. Management
Science 13, 843–866.
Ketokivi, M., 2006. Elaborating the contingency theory of organizations: the case of manufacturing flexible strategies. Production and
Operations Management 15, 215–228.
Kleindorfer, P.R., Singhal, K., Van Wassenhove, L., 2005. Sustainable
operations management. Production and Operations Management
14, 482–492.
Kouvelis, P., Chambers, C., Yu, D.Z., 2005. Manufacturing operations
manuscripts published in the first 52 issues of POM: review,
trends, and opportunities. Production and Operations Management
14, 450–467.
Krishnan, V., Loch, C.H., 2005. A retrospective look at Production
and Operations Management articles on new product development. Production and Operations Management 14, 433–441.
Lee, H.L., Ng, S.M., 1997. Introduction to the special issue on global
supply chain management. Production and Operations Management 6, 191–192.
Lee, W.B., Khumawala, B.M., 1974. Simulation testing of aggregate
planning models in an implementation methodology. Management
Science 20, 903–911.
Majumdar, P., Srinivasan, A.A., 2006. Leader location, cooperation,
and coordination in serial supply chains. Production and Operations Management 15, 22–39.
Muth, J.F., 1960. Optimal properties of exponentially weighted forecasts. Journal of the American Statistical Association 55, 299–
306.
Muth, J.F., 1961. Rational expectations and the theory of price
movements. Econometrica 29, 315–335.
Muth, J.F., 2004a. Herbert Simon and production scheduling. In:
Augier, M., March, J.G. (Eds.), Models of a Man: Essays in
Memory of Herbert A. Simon. The MIT Press, Cambridge,
MA, pp. 377–380.
Muth, J.F., 2004b. Personal communication, October 21.
Rao, U.S., Swaminathan, J.M., Zhang, J., 2005. Demand and production management with uniform guaranteed lead time. Production
and Operations Management 14, 400–412.
Ritzman, L.P., Krajewski, L.J., Berry, W.L., Goodman, S.H., Hardy,
S.T., Vitt, L.D. (Eds.), 1979. Disaggregation Aggregation Problems in Manufacturing and Service Organizations. Martinus
Nijhoff, Boston.
Schroeder, R.G., Linderman, K., Zhang, D., 2005. Evolution of
quality: first fifty issues of Production and Operations Management. Production and Operations Management 14 (4), 468–481.
Silver, E.A., 1972. Medium range aggregate production planning:
state of the art. Production and Inventory Management 13 (first
quarter), 15–39.
Simon, H.A., 1955. Some properties of optimal linear filters. Quarterly
of Applied Mathematics 12, 438–440.
Simon, H.A., 1956. Dynamic programming under uncertainty with a
quadratic cost function. Econometrica 24, 74–81.
Simon, H.A., 1978a. Herbert A. Simon—autobiography. http://nobelprize.org/economics/laureates/1978/simon-autobio.html.
Simon, H.A., 1978b. Rational decision-making in business organizations. Nobel Memorial Lecture, 8 December 1978. http://nobelprize.org/economics/laureates/1978/simon-lecture.pdf.
Singhal, K., 1992. A non-iterative algorithm for the multiproduct
production and work force planning problem. Operations
Research 40, 620–625.
J. Singhal, K. Singhal / Journal of Operations Management 25 (2007) 300–309
Singhal, J., Singhal, K., 1996. Alternate approaches to solving the Holt
et al. model and to performing sensitivity analysis. European
Journal of Operational Research 91, 89–98.
Sprague, L.G., Ritzman, L.P., Krajewski, L., 1990. Production
planning, inventory management and scheduling: spanning
the boundaries. Managerial and Decision Economics 11,
297–315.
Taubert, W.H., 1968. A search decision rule for the aggregate scheduling problem. Management Science 14, 343–359.
309
van der Velde, R.L., 1958. An application of a linear decision rule for
production and employment scheduling. Master’s Thesis. Massachusetts Institute of Technology.
Winters, P.R., 1958. Inventory control in a multiple warehouse system.
Ph.D. Dissertation. Carnegie Institute of Technology.
Winters, P.R., 1960. Forecasting sales by exponentially weighted
moving averages. Management Science 6, 324–342.
Winters, P.R., 1962. Constrained inventory rules for production
smoothing. Management Science 8, 470–481.