Operational Research and Corporate Planning in Railway Management

The current corporate planning process in Westrail is described and the roles played by operational research and mathematical modelling identified. They do not include forecasting the future. Instead of the "predict and prepare paradigm" a planning approach that tries to design the future has been adopted. The organisation is described in terms of how it has evolved, what it now is, and the key features of its environment. The three stages of planning are described-strategic, tactical and operational. These provide the means for management control of the railway. Environmental assessments linked to goal setting activities are incorporated within the planning process. The significance of operational research in planning, in particular mathematical modelling, is also described. Instead of finding problems to which existing techniques can be applied, O.R. is used to provide effective operational and planning systems as a means of assisting management to design the future for the organisation.     

An Interactive,Graphical Approach to Linear Financial Models

Early attempts to utilize linear algebraic (L.P.) techniques in financial planning met with little success. This was probably due to a misunderstanding of the process by which financial and corporate plans were formulated. The planning process is now better understood and the use of computer-based models is well accepted. However current modelling systems perform only simple arithmetic evaluations. There is a prima facie case for re-examining linear algebraic methods to exploit the considerable amount of information which is available within a plan. This paper describes the work done in the South of Scotland Electricity Board to develop an interactive financial modelling system.     

Strategic planning of transportation services for petroleum products An application of capacitated gravity models

Strategic planning of physical facilities and pricing of services for the distribution of petroleum products require a comprehensive consideration of the alternative means by which the products can be shipped to consumers from refineries, a mechanism for estimating the percentage of volumes that would be shipped via each alternative, and an assessment of the financial impact of changes in physical configurations and tariff structures of the distribution networks. Capacitated gravity models were created by adapting multiplicative competitive interaction (MCI) models for market share to accommodate capacity constraints at individual facilities and the aggregate capacity of groups of facilities served by the same pipeline segment. Procedures for calibrating the models were developed to cope with the problem of having only partial information about competitors' deliveries. Described in this paper are the structure of the models, calibration procedures, and their application to corporate planning in a large independent pipeline company which serves the midcontinental U.S. as a common carrier of refined petroleum products.     

Some Tentative Findings on Corporate Financial Simulation Models<Superscript>†</Superscript>

A framework for classifying corporate models is presented. Criteria for selecting the approach most appropriate for strategic planning are suggested. Experience of developing corporate financial simulation models in three separate companies is analysed. This is related to findings from an analysis of thirty-four models reported in the literature and of two surveys in the United States.     

Planning under uncertainty using parallel computing

Industry and government routinely solve deterministic mathematical programs for planning and schelduling purposes, some involving thousands of variables with a linear or non-linear objective and inequality constraints. The solutions obtained are often ignored because they do not properly hedge against future contingencies. It is relatively easy to reformulate models to include uncertainty. The bottleneck has been (and is) our capability to solve them. The time is now ripe for finding a way to do so. To this end, we describe in this paper how large-scale system methods for solving multi-staged systems, such as Bender's Decomposition, high-speed sampling or Monte Carlo simulation, and parallel processors can be combined to solve some important planning problems involving uncertainty. For example, parallel processors may make it possible to come to better grips with the fundamental problems of planning, scheduling, design, and control of complex systems such as the economy, an industrial enterprise, an energy system, a water-resource system, military models for planning-and-control, decisions about investment, innovation, employment, and health-delivery systems.     

Achieving ‘Desirable and Feasible’ Change: An Application of Soft Systems Methodology

Rational intervention in human affairs, if it is to constitute not only action but also research, so that future interventions may be made more effective, needs a well-defined methodological framework. Soft systems methodology (S.S.M.) provides one such framework. S.S.M. is doubly systemic: it is itself a learning system, and within that system it uses systems models, models of human activity systems. It accepts that such models are not models of parts of the real world, only models of ways of perceiving the real world, that is to say, models relevant to debate about ‘reality’ (one man's ‘terrorism’ is another's ‘freedom fighting’).S.S.M. is here illustrated by means of an account of a systems study of change in the information function of a sophisticated science-based company. The study is described from the point of view of a professional analyst who was helping a team of three managers carry out the study. The course of the study is described, and a separate commentary relates its activity to the seven stages of S.S.M. The study involved three cycles round stages 2–3–4–5 of S.S.M., the stages in which models of relevant human activity systems are built and compared with the real world in order to construct a rich debate about changes whose introduction in the problem situation world be both (systemically) desirable and (culturally) feasible.     

The Use of Models for Environmental Forecasting and Corporate Planning

This paper presents briefly, the forecasting and planning activities which have evolved over the years in Esso Petroleum Company. It describes three computer models developed in the Corporate Planning Department for these activities an econometric model for short-term environmental forecasting and economic analysis; a horizon year optimization model for long-range corporate objectives setting; and a multi-time period simulation model for financial forecasting and planning both in short and long term. The structure, use and limitations of each of the models are discussed.     

A Bivariate Model for Markov Manpower Planning Systems

Markov models are being extensively used for analysis of manpower planning systems. Most of these models concentrate either on estimating the gradewise distribution of future manpower structure, given the existing structure and promotion policies, or on deriving policies towards promotion, given the required future structure. However, in many large organizations, agreements between employee unions and management result in the framing of policies towards promotion based either on seniority (length of service in the grade) or on performance (as in the case of ‘high fliers’). In this paper these two criteria are considered in a bivariate distribution framework. The transition probabilities for promotion obtained from the Markov model are further translated into required seniority and performance rating. The procedure is illustrated through an example.     

Database-Oriented Design of Planning Models

There exists a close correspondence between relational databaseorganization and the structure of mathematical planning models.This correspondence will be used (i) for the explanation ofstandard planning models, (ii) for the integration of planningmodels in information systems, and (iii) for the process ofsystematic design of large-scale planning models.     

A Priori and A Posteriori Aggregation Procedures to Reduce Model Size in MIP Mine Planning Models

Mine planning models have proved to be very effective in supporting decisions on sequencing the extraction of material in copper mines. These models have been developed for CODELCO, the Chilean state copper mine and used successfully. Here, we wish to develop a corporate model, including all mines of CODELCO. The original models are extremely large MIPs. In order to run a global model, the original models need to be reduced significantly. We develop an approach to aggregate the models. The aggregation is done both on the original data of the mine as well as on the MIP original models. The aggregation is based on clustering analysis. Promising results were obtained with data of a large underground mine.     

Applications of the analytic hierarchy process to long range planning processes

This paper considers the long range planning process from the point of view both of projecting forward likely or desired changes from the current position to define a possible future state, and of identifying desirable future states and working backwards from those to consider ways in which they could be reached. This process is repeated until consistency between the forward and backward looking processes is achieved. A theory of analytic hierarchies is used to give a formal structure of the process. An application to an actual corporate plan is briefly discussed.     

Considerations for building knowledge-based expert systems in highway maintenance planning

Genuine, nontrivial planning problems in pavement and bridge maintenance are generally beyond the capabilities of expert systems. However, the diagnostic, interpretive and predictive features of such systems can be combined with algorithmic planning tools to produce comprehensive maintenance planning and management systems.After a discussion of the relevant issues, this paper addresses the potential application areas for knowledgebased expert systems in highway maintenance planning. The foxus is on how and where expert systems can interface with optimization models to yield meaningful results.     

Simulating the strategic financial planning process

Strategic financial planning should (1) involve the key variables in the corporate growth process, (2) reflect the dynamic interaction in the system and (3) incorporate the dimension of uncertainty. Decision makers need a stochastic model that links the interaction between the investment and financing process for the planning period. The model presented in this paper integrates the investment and financing process by the use of simulation. The measure used to link these two systems is the rate of return required on new investment in order for decision makers to achieve their desired earnings-per-share growth goal. The model provides top management a tool to communicate their expectations to lower levels of management, thereby allowing them to measure and evaluate the impact of various sets of assumptions on the company's strategic plans.     

The dynamics of the UK chemical plant investment cycle

Orders for new chemical plant (sanctions) and investment in chemical plant both show marked business cycle like variations. Amongst the effects of the UK cycle on the Chemical Industry are variations in: new plant additions, requirements for capital expenditure finance and capital utilization. As far as the UK Process Plant Contractors and Process Plant Manufacturers are concerned, the cycle is reflected in a varying work-load arising from the UK Chemical Industry together with varying manpower requirements and profitability.The project described in this paper involved the construction of both System Dynamics and Control Systems models of the Investment Cycle as a guide to strategic planning for the industries affected by it.The implications of the results from both types of model for corporate and strategic planning in the industries concerned are discussed.     

Contagion in cyber security attacks

Systems security is essential for the efficient operation of all organizations. Indeed, most large firms employ a designated ‘Chief Information Security Officer’ to coordinate the operational aspects of the organization’s information security. Part of this role is in planning investment responses to information security threats against the firm’s corporate network infrastructure. To this end, we develop and estimate a vector equation system of threats to 10 important IP services, using industry standard SANS data on threats to various components of a firm’s information system over the period January 2003 – February 2011. Our results reveal strong evidence of contagion between such attacks, with attacks on ssh and Secure Web Server indicating increased attack activity on other ports. Security managers who ignore such contagious inter-relationships may underestimate the underlying risk to their systems’ defence of security attributes, such as sensitivity and criticality, and thus delay appropriate information security investments.     

A reference model for customer-centric data mining with support vector machines

Supervised classification is an important part of corporate data mining to support decision making in customer-centric planning tasks. The paper proposes a hierarchical reference model for support vector machine based classification within this discipline. The approach balances the conflicting goals of transparent yet accurate models and compares favourably to alternative classifiers in a large-scale empirical evaluation in real-world customer relationship management applications. Recent advances in support vector machine oriented research are incorporated to approach feature, instance and model selection in a unified framework.     

Planning Business Strategies in Diverse Companies

The most important problems for corporate planners in industry are planning the cash flow to ensure growth and helping the company to see itself, explicitly, as a portfolio and to assist the development of that portfolio. Planners have dismissed the role of operational research groups and O.R. techniques and basically simple techniques, usually developed by general consultants, have made a considerable impact, particularly "Boston" concepts of portfolio management. Planning processes are dominated by human judgement and the work of Godel is invoked to suggest that the use of better models to influence these corporate decisions is a forlorn hope. O.R. groups should concentrate on helping to solve perceived and real problems after seeking to display alternatives better, rather than to optimise. They should concern themselves more with improving the quality of presentation and ensuring the clarity of basically simple messages.     

Sequencing mixed-model assembly lines: Survey,classification and model critique

Manufacturers in a wide range of industries nowadays face the challenge of providing a rich product variety at a very low cost. This typically requires the implementation of cost efficient, flexible production systems. Often, so called mixed-model assembly lines are employed, where setup operations are reduced to such an extent that various models of a common base product can be manufactured in intermixed sequences. However, the observed diversity of mixed-model lines makes a thorough sequence planning essential for exploiting the benefits of assembly line production. This paper reviews and discusses the three major planning approaches presented in the literature, mixed-model sequencing, car sequencing and level scheduling, and provides a hierarchical classification scheme to systematically record the academic efforts in each field and to deduce future research issues.     

Tactical supply chain planning models with inherent flexibility: definition and review

Supply chains (SCs) can be managed at many levels. The use of tactical SC planning models with multiple flexibility options can help manage the usual operations efficiently and effectively, whilst improve the SC resiliency in response to inherent environmental uncertainties. This paper defines tactical SC flexibility and identifies tactical flexibility measures and options for development of flexible SC planning models. A classification of the existing literature of SC planning is introduced that highlights the characteristics of published flexibility inclusive models. Additional classifications from the reviewed literature are presented based on the integration of flexibility options used, solution methods utilized, and real world applications presented. These classifications are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.