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Our management built one of the first fully comprehensive Enterprise Decision Analysis Models to run on a personal computer in 1989-1990. This program called Seel-Telesis1 made use of a set of algorithms constituting a model building kit to code and quantify the physical, economic and financial inter-relationships between equipment, inputs, energy, real estate, information and manpower, taking into account time and locational factors. This system allowed managers to simulate multiple decision outcomes as a basis for selecting the lowest cost and risk paths for profitable growth. The system combined short term optimization of output as well as strategic decision-making for medium term investment.

The successful demonstration of the system took place in March 1990. It was made possible through the use of dynamic link libraries and a new compiler developed by Neils Jensen of TopSpeed Corp which created the most compact executable at that time.

Seel-Telesis was the first computer program to integrate the locational-state method (see locational-state theory)

1 This initiative was funded by the Manpower Services Commission of the Department of Trade & Industry of the Government of the United Kingdom.

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Enterprise models & strategies

The development of an enterprise-wide decision analysis model was an objective of early attempts in the development of so-called decision support systems. In practice, decision analysis has tended to be applied within companies to certain key differentiated functions such as logistics, production planning, process optimization and financial strategies.
More information ...

Simulations ...simulations enable the dynamic review of the sensitivity of outcomes (scenarios) to controlled and uncontrolled determinant values before any decision to commit resources is taken ...

Structured calculations ... the process of logical calculation is the fundamental method for supporting decision analysis ...

 Goal seeking & optimization ... many decisions relate to a specific decision objective such as minimizing costs, maximising profits or achieving an optimised solution when there are countervailing constraints on the achievement of an objective ...

 Deduction ... how we think and deduce can be automated to provide sophisticated diagnostic systems on importance in biomedicine and various forms of investigation ...

 The learning curve ... with practice people improve performance and use less resources to produce better quality output. This learning curve effects has a wide range of decision analysis applicaitons ...

 Governance ... when decisions are taken on behalf of large groups or constituencies there is a need for transparent coding of group preferences into decision analysis models ...
This has worked quite well because such well-defined company functions often had a dedicated management, were handled as profit centres and were are able to apply decision analyses to gain improvements in the performance in their areas of responsibiity. One of the outcomes of the emphasis on differentiated units was lower marginal performance gains in comparison with those realizable through the use of more integrated, albeit more complex, models.

The objective of "whole enterprise" decision analysis models is to neutralise "profit centre tension" by creating decision-making criteria whose priciples can be applied through common business rules which contribute to the maximisation of overall corporate performance. In terms of corporate culture and work group, a good enterprise decision analysis model can help improve communications between different operations so as to highlight mutual interests satisfied through higher performance, individual incomes and security of future prospects.

Structural Production Functions

SEEL-Systems Engineering Economics Lab has pioneered the development of Structural Production Functions1 (SPF). These permit the transparent aggregation or disaggregation of process models which integrate enterprise process cells, enterprises (microeconomy), economic sectors or whole economies (macroeconomy). SPFs provide a "solution" to the problem of solving enterprise process "functions" in terms of quantitative functional input and output relationships between several factors of production. SPFs represent an advance on conventional input-output analysis (first developed by Wassily Leonitief for the macro-economy in 19492) and activity input-output nodes to a fully functional model where the nodes have been "solved". Solutions often take the form of formulae which describe the cell relationships as opposed to a input-output table. This is why the final formula model is referred to as a Structural Production Function. The model provides a transparent insight to the structure, connections and flows of factors within the enterprise used in production and any part of the enterprise processes can be "solved" because each node is made up of a function relating the quantitative inputs, determinants and outputs.


The benefits of integration

Enterprise models based on Structural Production Functions can uncover unexpected effects which powerful insights to decision analysis. Thus when optimization procedures are applied to a specific corporate activity the key relationships might be assumed to be linear. Where the method of data set selection and processing is based on the locational-state method (see locational-state theory) the simulation procedures shift from a multi-frame-instance format to a fluid continuous format. The effect of this is for the SPF Enterprise Model to manifest different functional relationships in the sense that the optimization relationships assumed to be linear show become transformed into more realistic and quantitatively precise non-linear functions. This is not a flaw in the model but rather a more realistics representation of the interactions of corporate activities creating constraints on others. The benefit is that these more realistic relationships, which tend to be company-specific, can be measured, tracked and used to make accurate estimates of decision option outcomes. In this way SPFs provide more precise estimates of potential corporate performance achievable from different decision options.

How we help

We can assist our clients build enterprise decision analysis systems in a stepwise fashion building completely operational components, each providing useful decision analysis in key areas. This process can continue to build a corporate structural production function integrating all key operational areas into the system. We can then hand over an operational system to clients, including personnel training and technical support.


1 Structural Production Functions were developed by Hector W. McNeill in 1976 as a means of describing how enterprises can respond to exogenous economic impacts such as petroleum price shocks, slumpflation or a financial crisis. The original purpose of this analysis was to identify appropriate macroeconomic policies for managing such crises. This approach provides a non-linear programming approach to sustain performance orientated towards the lowering of unit costs and raising of product quality.
2 Wassily W. Leontief built a 500 sector input-output model of the US Economy as a computer program in 1949. This linear equation-based system was the first significant computer-based economic model. Before computers existed, the Frenchman, François Quesnay (1694-1774) created the concept of the aggregation of flows of inputs to create sector output and these were represented in Tables, known as Tableau Économique. Quesnay also conceived of the economic concept of value-added.
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