HARD SYSTEMS THINKING IN ACTION

Given the range of hard systems approaches covered in this chapter, it is di⁄cult to provide one representative example of hard systems thinking in action. We shall tackle this problem by showing di¡erent aspects of use for each of OR, SE and SA.

Once the original pioneering spirit had faded, operational researchers, or at least those of a more academic persuasion, began to concentrate their e¡orts on developing mathematical models to apply to what they recognized as frequently occurring types of problems. Each problem type was assumed to have a particular form and structure, which determined its nature and

Hard systems thinking in action 57

how it could be tackled, regardless of the context in which it was found ^ military, manufacturing industry, service sector, etc. Fortuin et al. (1996) present 15 case studies of OR at work in application areas as diverse as transport and logistics, product and process design, maintenance and

¢nancial services, health care and environmental decision-making. Keys (1991), and Cavaleri and Obloj (1993), provide good introductory material on the most common OR problems; a typical list being:

. queuing problems;

. inventory problems;

. allocation problems;

. replacement problems;

. co-ordination problems;

. routing problems;

. competitive problems;

. search problems.

Queuing models seek an optimum trade-o¡ between the costs of providing service capacity and keeping customers happy. Inventory models aim to establish the optimum reorder point for stocks of resources so that pro-duction £ow can be maintained while the costs of holding excess inventory are minimized.

Allocation models seek to apportion scarce resources in the most e⁄cient manner, maximizing output or minimizing costs, while achieving overall objectives. Keys comments on an example involving a farming enterprise that both reared cattle for beef and produced crops that could themselves be sold or, alternatively, used to feed the cattle. A linear programming-type model was constructed containing 640 constraints and 1,801 variables. A solution that maximized pro¢t was discovered in 34 seconds of computer time.

Replacement models help to minimize costs by identifying the point at which acquisition of new assets is justi¢able. Co-ordination techniques, such as PERT (Programme Evaluation and Review Technique) and critical path analysis, calculate how tasks must be sequenced in a project to ensure completion in minimum time and at minimum cost. The goal of routing models is to determine the most e⁄cient route between di¡erent locations in a network. Competitive problems are conceptualized in terms of games, the aim being to maximize outcomes for one or more participants. Search models try to maximize the e⁄ciency of a search (say, for a location for a new factory) by minimizing both costs and the risks of error.

A recent INCOSE document (see www.incose.org) sets out systems engineering pro¢les for 18 di¡erent application domains: agriculture, commercial aircraft, commercial avionics, criminal justice system and legal processes, emergency services, energy systems, environmental restoration, facilities systems engineering, geographic information systems, health care, highway transportation systems, information systems, manufacturing, medical devices, motor vehicles, natural resources management, space systems, and telecommunications. There are also pro¢les for seven cross-application domains: e-commerce, high-performance computing, human factors engineering, Internet-based applications, Internet banking, logistics, and modelling and simulation. Not surprisingly, a number of these pro¢les are rudimentary, with the most extensive being in areas of traditional systems engineering practice, such as the design and development of commercial aircraft.

The commercial aircraft industry operates in a very competitive environ-ment and depends on complex manufacturing processes arising from highly integrated subsystems, advanced technologies, use of advanced materials, detailed speci¢cations and very rigorous testing. The systems engineering speci¢cations for this domain insist on the principle that commercial aircraft are considered as wholes, and not as collections of parts. Both customer and regulatory requirements are ¢rst identi¢ed. Aircraft architecture is then seen as a hierarchy in which the functions and constraints operating at the top level, the aircraft system itself, £ow down into require-ments for the subsystems. A typical decomposition of the aircraft system into parts would identify the mechanical, propulsion, environmental, airframe, avionics, interiors, electrical and auxiliary subsystems. These sub-systems are then further decomposed into subordinate components with their own requirements deriving from those of the subsystems. Thorough monitoring and control is essential at all stages of design and construction to ensure that requirements at the di¡erent levels are veri¢ed and validated by testing.

The IIASA handbooks provide some comprehensive descriptions of SA applications, which are then referred to and analysed throughout the three volumes. The main illustrations are of improving blood availability and utilization (also described in Jackson, 2000), improving ¢re protection, protecting an estuary from £ooding, achieving adequate amounts of energy for the long-range future, providing housing for low-income families and controlling a forest pest in Canada. Of these examples, the ¢re protection case is regarded as one that closely follows the prescribed systems analysis methodology.

Hard systems thinking in action 59

The ¢re protection study began in 1973 in Wilmington, DE and was conducted by a local project team with technical assistance from the New York-based RAND Institute. The eight existing ¢rehouses in Wilmington were getting old and the mayor wanted to ¢nd out if they o¡ered adequate protection, whether they were located in the right places and whether any new ¢rehouses needed building. The main objectives of ¢re protection were pretty obviously to protect lives and safeguard property while, at the same time, keeping costs low. Unfortunately, there was no reliable way of evaluating how di¡erent deployment strategies related directly to these objectives. Three ‘proxy’ measures were therefore developed: approximate travel time to individual locations, average travel time in a region and company workload. The consequences of changes in locations and numbers of ¢rehouses were then considered against these.

The next stage required the analysts to build models that could be used to test various deployment alternatives. The primary tools employed to encap-sulate the data were a parametric allocation model, based on a mathematical formula for allocating companies to di¡erent regions, and a more descriptive, simulation model, known as the ¢rehouse site evaluation model. The transparency of this latter model was crucial as it enabled city o⁄cials to be involved in suggesting alternatives.

The recommendations to close one of the ¢re companies and reposition most of the remainder provoked a long battle with the ¢re¢ghters union before they were eventually implemented. When the results were ¢nally evaluated it was found that the ¢re protection service was just as e¡ective as before, but with costs signi¢cantly reduced.