GoldSim Modeling Methodology

The GoldSim Modeling Methodology that we utilize to help our customers build complex GoldSim models is designed to support the full spectrum of modeling projects, from a strategic level to a more detailed operational level. The key tenets of the GoldSim methodology are described in this article.

Establish Clear Objectives

The methodology starts by reviewing and clearly stating the objectives of the exercise, and an assessment of their feasibility. Defining the objectives is critical to keep the analysis focused, on time, under budget, and ultimately, successful.
 

Decomposition

It is important to understand that a GoldSim model will not provide useful results if it isn’t based on a reasonably good understanding of the system to be modeled (in other words: garbage in, garbage out). Therefore, building a conceptual model of your system is probably the most important part of any simulation effort. The greater your understanding of the critical factors that determine the behavior of your system, the more likely your simulation effort will provide useful results.

Building a good conceptual model of the system involves an analysis phase that results in decomposition of the system into a series of linked subsystems that define the key components of the system, the relationships between these components, and all relevant feedback mechanisms. Decomposition results in an influence diagram that is a conceptual picture of the system, its main components, and their interactions. A simple example of such a diagram is shown below:

 

Integration

In order to address the full range of influences identified in the decomposition, the analysis must provide an integrated model of the system that couples each of the subsystems, rather than treating each part of the system independently. Developing such an integrated understanding of the system typically involves input and feedback from many people within the organization and thoughtful investigation of how the different elements of the system relate.

Most people find that the exchange of information and ideas that occur while formulating the conceptual model results in valuable insights and better understanding of the system prior to even building a simulation model. In addition, the integration phase provides a critical opportunity to get buy-in and support from a broad range of constituents within the organization (e.g., operational managers, technical experts, senior management).
 

Top-down/relevance driven

Models of large, complicated systems can be difficult to calibrate, explain, and maintain. As a result, the analysis should begin at a high level and detail should only be added only when the preliminary results indicate that the additional detail is necessary and relevant. To paraphrase Einstein, “A simulation model should be as simple as possible, but no simpler”.
 

Explicit uncertainty

Complex systems have many uncertainties: How much will it cost to develop a new product? How will prices for raw materials change? How will competitors respond to market conditions? What new technologies will emerge in the next five years? How will the general economy influence sales?

Since most planning activities address systems with significant uncertainty, it is critical that the analysis explicitly accounts for the full range of possibilities (rather than reliance on conservative estimates). This includes uncertainties in costs and durations, uncertainties in the consequences and effects of carrying out various activities, and uncertainties regarding the occurrence of outside events (e.g., accidents, lawsuits) or new developments (e.g., a change in interest rates, changes in political office).

Incorporating uncertainty regarding the consequences of carrying out various activities and/or the occurrence of unanticipated incidents or developments can alert the planner/analyst to flaws in the strategy and provide guidance for improving the strategy. Typically, it is not possible to eliminate the possibility of unanticipated incidents or developments (e.g., a drop in commodity prices). However, if these possibilities are explicitly considered in the planning stage of the project, then additional activities can be carried out beforehand and/or contingency plans can be prepared that will reduce the likelihood of the incidents or lessen the impact should they occur.
 

Dynamic Simulation

Sound strategic planning must account for changes in the operational guidelines depending on future conditions. It should be expected that managers will make future decisions based on information available at the time. For example, plans to expand production facilities may be canceled if sales are less than projected.

In short, the simulation model should specify the planned responses to the uncertain aspects of the strategy, and how these in turn will affect the manner in which the system behaves from that point forward. Thus, good strategies incorporate the contingency plans necessary to respond to new developments or incidents in the system. Given the complexity of most strategic planning efforts, the system and each of the alternative strategies must be expressed using mathematical relationships with quantitative performance criteria. Dynamic (time dependent) simulation provides the mechanism to predict the full range of possible futures, analyze the results, and communicate findings to stakeholders and decision-makers.

Communication

The process should be conducted in a clear and transparent manner that provides the means to communicate the structure of the model and the results to the stakeholders. This communication element is critical for several reasons:

• Communication during the model development phase is necessary to ensure that the conceptual model accurately represents reality.
• Stakeholders find it much easier to trust an analysis that they can understand.
• Decision makers need to be able to quickly understand a model and the associated results to make informed decisions.

Development of a complex GoldSim model requires a team approach that typically includes the following roles:

• Sponsor: Usually a senior-level manager who is focused on improving operations and bottom-line results. This person should have the authority and the knowledge to marshal the proper resources throughout the organization and direct the model development activities effectively.
• Operational Managers: These managers are usually responsible for a part of the business such as production/manufacturing, logistics, purchasing, marketing and sales, information systems, financial management, and human resources. These are typically the people who know how things work, what processes are in place, and how decisions are made in the real world. Their participation is critical to ensuring that all the important components and decision rules that make up the system are identified and incorporated into the conceptual model.
• Modelers: These are the people that translate the conceptual models into the mathematical expressions and relationships that make up the computer model. The modelers should be familiar with general computer modeling concepts (e.g., mass balance, unit consistency, Monte Carlo analysis, probability distributions, etc.). It is critical that the modelers be involved in the conceptual model development to ensure that the operational managers are providing adequate information regarding relationships between critical components of the system, documented or undocumented operational procedures, and feedback mechanisms.