Introduction

Sound decision making is the cornerstone of organisational success and informs how, as individuals, we perform our work and plot the course of our lives. Decisions can range from the simple to the highly complex, where complexity depends on many factors. Such factors include the number of options to consider in the decision, the consequences of the decision, the volume of information involved, the number of constraints encapsulated in the decision, and the risks inherent in the outcome.

While some decisions may be almost reflex, made easily and quickly without significant formal analysis, other decisions may require significant effort to collate relevant information, identify constraints and remediate risks. In some cases, it may take a team to assemble all relevant data, apply robust decision making processes, and ultimately take a judgement call on the correct outcome.

When making decisions, there are times when the amount of information to collect and the need to "get it right the first time" can be overwhelming. It is at times like these when we may benefit from a tool or system that can help us to organise our decision making process, keep all information in one place and allow us to return to it at any time with all of the key characteristics of the decision available for review. The need for a tool or system is magnified when many decisions need to be made simultaneously. Decision Making Modelling provides the process that allows the complexities of multiple decisions (whether simple or complex) to be considered while collating all decision information and criteria in the right place at the right time.

What is decision making modelling?

Put simply, Decision Making Modelling facilitates the following features:

• Define and allocate all decision options, for example all of the alternatives we are deciding between. Consider a visit to a restaurant where we ask ourselves "What’s for lunch?" All choices on the menu are alternatives (options) to choose from. One of these options will be the outcome – the item we decide to eat.
• List and assess the criteria that will inform our decision. The criteria for a decision can be split into things that must be addressed (Givens) and things that are nice to have (Wants).
• Givens (or given criteria) are mandatory criteria, and must be delivered by the option we choose when making the decision. If an option cannot address all of the Givens, it should be excluded.
• Wants are “nice to haves” and each option can be ranked by how well it addresses each want. 
• For example, in our restaurant, for lunch let’s get something that is a food that we like, that none of our lunch guest are allergic to. Both these requirements are givens – lunch will not be eaten unless they are met. However, what about our preferences for food. If we meet the givens, we can choose from items we like on the menu. Is the preference pasta, beef, chicken, vegetarian, fish…? Each possible selection (or want criteria) will likely have a different weight based on the preferences of our group of guests. Once we establish these weightings, then each option can be rated against its ability to deliver that want.

In Decision Making Modelling, we use options, givens and wants to score a decision and identify an ordered set of options, ranked from most to least appropriate. Using the options, wants and given criteria, we can assess which of the options meet all our mandatory (given) requirements. Once we have a set of viable options (where all givens are met), we can then determine the most appropriate option by ranking each want in terms of its relative importance, and scoring each option on how well it meets the want criteria. Once the decision scores are revealed, a qualified and informed decision can be made.

However, there is still a risk the decision can be invalid. An additional set of criteria should be considered – that is, the risk that the chosen option will cause undesirable flow on effects or consequences. For this reason, a robust decision making model allows each option to be assessed against one or more risks. A risk is an outcome we do not want to happen because of the decision. For example, at lunch, a risk may be that we do not like the meal and it will not be eaten. While we remediate this risk by choosing from options that meet all of our mandatory given requirements, and score highly against out “nice to have” wants, we may wish to consider an additional risk that some of the food will be wasted, and therefore what is the likelihood that some portion of the group will not eat the option we choose? The addition of risk allows us to consider undesirable consequences of our decision, and lets us inform and qualify the decision by assessing the risks and thus reducing the likelihood of choosing an unpalatable option.

The option that meets all of the givens, with the lowest risk and highest want score, is typically the best option to select. However, it is not always that simple. If the scores for each option are close, additional options, more given, want or risk criteria may be needed to clarify the way forward. Running the decision making model will allow the decision to made without emotion and no matter the outcome, it provides clarity and a clearly documented data-based and robust process for making the decision.

What is required from a robust Decision Making Modelling system?

A Decision Making Modelling system should provide a number of key attributes and capabilities.

• The ability to name our decision.
• The ability to add and track options.
• The ability to add, track and compare decision criteria for givens (mandatory) and wants (optional) criterion.
• The ability to add, track and compare risk factors.
• The ability to allocate outcomes and ratings to all of the decision attributes:
• Givens – either met or not met.
• Wants – the relative importance of each want and how well an option delivers on the want.
• Risk Severity/Consequence – the level of consequence if the chosen option fails to address a specific risk.
• Risk Probability/Occurrence – how likely is it the risk to occur for a specific option.
• The ability to see all options, their criteria and ratings side by side
• The ability to report on decision outcomes.
• The ability to add more data to the decision model as and when required.

What are the limitations of decision making modelling processes?

Decision making modelling processes allow you to collate options, criteria and assessment outcomes for a decision, and use this data to assess and inform your decision-making process. By using the data, rather than emotion, you can make repeatable, explainable, quantifiable decisions.

The tool provides a framework as well as assessment and reporting capabilities around your decision model. However, the decision-making model is only as good as the information and the accuracy of assessment provided to it. This requires human input, taking a robust view of the problem and considering all relevant options, criteria and all flow consequences once the decision is made.

No matter the capabilities of the tool, though, the one thing the decision-making modelling process does not do is make the decision for you. The decision-making process, and this tool, will provide you with a powerful and advanced guide to making your decisions in a robust and repeatable manner – but ultimately the selected option belongs to the decision maker - you!

Summary

Decision Making Modelling from Online Process System is a module of the iProgent suite of applications, providing a comprehensive toolset for Organisational Robustness Analysis. Decision Making Modelling delivers all of the requirements for sound decision making and lets you make informed, robust and repeatable decisions.

Please contact Online Process Systems for more information on Organisational Robustness Analysis or the iProgent Suite.