A Survey of the Application of Quantitative Techniques to Production/Operations Management in Large Corporations

Green, Thad B.; Newsom, Walter B.; Jones, Stephen

doi:10.5465/255365

Cited by 31 publications

(13 citation statements)

References 16 publications

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“…Most of the studies concentrate on a narrowly defined set of analytical techniques such as cash management, capital budgeting, or operations research models. For example, Vatter (1967), Klammer (1972), Weston (1973), Gaither (1975), Green, Newson and Jones (1977), and Kiani-Aslani (1977) traced the deployment of various operations research techniques. Vatter concluded that the utilities and transportation industries were more active practitioners of such procedures than the manufacturing industries.…”

Section: Review Of the Literaturementioning

confidence: 99%

An Analysis of the Financial Management Techniques Currently Employed by Large U.S. Corporations

Moore

Reichert

1983

Business Fin & Account

116

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Economic conditions have placed increased importance upon rigorous financial analysis. In order to determine which analytical techniques are currently emp loyed by management, a questionnaire was sent to each fm on the May 1980, FORTUNE 500 list. The researchsought to establish a profile of the respondents' organizational structure and to identify the primary procedures used in risk assessment, working capital management, capital budgeting, and operations research modeling. The results do suggest a basic profile of the more active employers of analytical techniques. Relatively sophisticated capital budgeting procedures appear to be accepted across most industries, and many firms support their decision making with a “package” of formal tools.

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Section: Review Of the Literaturementioning

confidence: 99%

An Analysis of the Financial Management Techniques Currently Employed by Large U.S. Corporations

Moore

Reichert

1983

Business Fin & Account

116

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“…Hillier and Lieberman (2001) and Taha (2003) had a complete treatment of OR methods such as linear programming, network analysis, dynamic programming, game theory, queuing theory, inventory theory, Markovian decision processes, reliability theory and non-linear programming. Surveys conducted by Forgionne (1983), Green et al (1977), Shannon and Biles (1970), Thomas and DaCosta (1979) and Turban (1972) report low utilisation of some of the OR techniques such as queuing theory, integer programming, stochastic programming, search techniques, non-linear programming, dynamic programming and game theory. Vatter (1967), Gaither (1975) and Radnor and Neal (1973) mentioned that size of the firms and extent of OR methods used are positively related.…”

Section: Approach Methodsmentioning

confidence: 99%

“…Vatter (1967), Gaither (1975) and Radnor and Neal (1973) mentioned that size of the firms and extent of OR methods used are positively related. Other barriers to usage of OR methods are listed as mathematical complexity, lack of training, knowledge or education (Gaither, 1975;Green et al, 1977;Shrock and Ostrom, 1981;Thomas and DaCosta, 1979;Watson and Marrett, 1979).…”

Section: Approach Methodsmentioning

confidence: 99%

A review of literature on manufacturing systems productivity measurement and improvement

Muthiah

Huang

2006

IJISE

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Globalisation is posing several challenges to the manufacturing sector. Design and operation of manufacturing systems are of great economic importance. Factory performance remains unpredictable in spite of the considerable literature on manufacturing productivity improvement, and the long history of manufacturing as there is no widespread agreement on how best be performed (Gershwin, 2000). Productivity measurement and improvement goes hand in hand, because one cannot improve what one cannot measure. The review of literature on manufacturing system productivity measurement and improvement has been summarised under four categories; they are Operations Research-(OR-) based methods, system analysis-based methods, continuous improvement methods and performance metrics-based methods. A survey of commercial tools available to measure manufacturing system performance is also performed. The review indicates that quantitative metrics for measuring factory level productivity and for performing factory level diagnostics (bottleneck detection, hidden capacity identification) are lacking. To address this gap, a factory level effectiveness metrics-based productivity measurement and diagnostic methodology is proposed.

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“…Whilst it is tempting to dismiss Forrester's work, there are other more comprehensive surveys that have reached similar conclusions about industry's lack of use of QM (Alpander, 1976;Green et al, 1977;Kathawala, 1988;Coccari, 1989). Nor is Forrester alone in suggesting too much time is allocated to QM by Business Schools.…”

Section: Statistics Mbas and Mba Programmesmentioning

confidence: 99%

Statistics in Management Education

Stray¹,

Naudé²,

Wegner³

1994

British J of Management

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Results of two surveys are presented and indicate the statistical knowledge of past students from Master of Business Adminstration programmes, their usage of statistics together with their opinions about the statistical content of Master of Business Administration degree courses. These views are contrasted with the results of a second survey of academic staff who teach statistics on such programmes. We find that past students recommend a greater statistical content than is currently taught but, paradoxically, most of the academic staff associated with teaching the subject believe that the statistical content of their programme will decrease in future years.

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A Survey of the Application of Quantitative Techniques to Production/Operations Management in Large Corporations

Cited by 31 publications

References 16 publications

An Analysis of the Financial Management Techniques Currently Employed by Large U.S. Corporations

An Analysis of the Financial Management Techniques Currently Employed by Large U.S. Corporations

A review of literature on manufacturing systems productivity measurement and improvement

Statistics in Management Education

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