The decision-making process can be illustrated as a proposal considered by decision-makers in the context of the organisation and its strategic position. Alternatives, risks and potential outcomes are considered and then a decision is reached. The decision-making process is subject to human error as the decision-makers have personalities, prejudices and a self-interest bias. Importantly, they have different attitudes to and appetites for risk.
All decisions hinge on timing if you spend too little time deciding you may end up with buyer’s remorse, the feeling that you’ve made an uninformed decision. Spend too long making the decision and you may end up convincing yourself that the grass is greener in another pasture. Finding the perfect timing, in general, is hard but even harder in the business sense, hierarchy, differing managerial structures, board approval and proof of concept meetings could push a simple decision into a long drawn out affair which could’ve been circumvented by going through smaller more defined processes.
Decision-making ability is reserved for the upper echelon of a business and thus an attempt to affect change from a lower level is extremely tough, but not impossible.
When making a critical business decision there are steps in place that govern how these decisions are implemented. A decision usually follows the hierarchical chain of the company, as can be seen in figure 1. The entry point for providing a solution heavily impacts the ability to affect change in the company. Enter at the wrong level with your idea and you may end up not being able to get a decision out of that firm and entering at a higher level is often far more difficult. One then also needs to look at not ending up in a feedback loop; back and forth email exchanges that ultimately lead nowhere. Effective decision making relies on the managerial processes which a firm has in place and this is dependent on a manager’s ability to:
Relay and communicate this information effectively
Understanding the relevance to their business
Understanding the product that is being implemented
How a new system could impact their business with regards to cost vs performance output (trade-offs)Ability to influence the final decision
Managing the future performance and risk effectively.
So how do firms make better decisions?
In more and more companies, managerial decisions rely less on a leader’s “gut instinct” and instead on data-based analytics. We are currently witnessing the fourth industrial revolution; firms have an ability to gather extremely detailed data from various sources and use them to determine the right course of action. Part of this trend is due to the widespread diffusion of enterprise information technology such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) systems along with various other software solutions that don’t fall into these categories. These systems capture and process vast quantities of data as part of their regular operations and churn out results. Increasingly these systems are imbued with analytical capabilities, and these capabilities are further extended by Business Intelligence (BI) systems that enable a broader array of data analytic tools to be applied to operational data and Financial reporting software such as Quick Consols that report the final results to management.
What quality and efficient decisions mean for a company?
With the readily accessible information at hand to firms, decisions that affect the performance outcomes of a company can be readily improved. With the increase in the frequency of feedback and the ability to make changes, this should lead to enhanced performance as decision-makers response time greatly increases to environmental changes. Thus, impacting the consequences of their decisions. In theory; more concise information leads to quicker response time, which in turn leads to better, faster implementation thus a quicker turnaround for improved results. A study done on the business practices of listed firms has shown that there is an increase of 5-6% on output and productivity for firms that implement data-driven analytics.