In today’s uncertain and competitive world, it is mandatory to make better business decisions faster, and with better execution, in order to improve performance. Selecting one course of action (hopefully the best possible) from a set of available alternatives may turn into an intricate optimization problem.
Despite the challenge of transforming insights and foresights into actionable smart and granular recommendations (e.g. optimize a production process or a marketing campaign, or design a robust supply network), the value derived from optimization (mathematical models and algorithms, and AI techniques) is enormous, as summarized in the next five tangible benefits:
Rules-of-thumb or simple decision rules underperform when used to solve complex decisions, and are useless on settings with high levels of uncertainty that may cause disruptions. Such empirical approaches deliver solutions far away from optimality. Prescriptive analytics impacts KPIs by expanding the solutions space (e.g. considering a wider planning horizon or integrating different functional areas of the organization), by comprehensively framing the problem in several manageable building blocks, differencing soft constraints (nice to meet) from hard constraints (violation is forbidden), and by guiding the search dynamically in function of the business goals to be optimized (thus exploring better tradeoffs between various conflicting criteria, such as customer satisfaction and operational efficiency).
The power of understanding the effect of decisions and of anticipating outcomes under changing assumptions and sources of uncertainty is critical in complex business challenges. By allowing the comparison of different solutions for alternative scenarios and inputs (e.g. demand, supply, pricing, costs and technological-related) in short running times, a prescriptive engine fosters new and innovative ways of solving the business problems, and of identifying business opportunities for growth and efficiency. Ultimately, identifying and better quantifying the risk, and devising mitigation strategies.
A new way of problem solving and thinking in many organizations emerge, more holistic, moving from “gut-feel” decision-making to fact-based decisions. Actionable steps to be taken are supported by data. Prescriptive analytics brings more transparency to decision making (clarifying why a certain course of action is taken), blurring silos within the organizations and supporting effective, cross-functional interaction between teams. Enterprise-wide performance is thus favored, in contrast to improving performance in one area which may undermine the metrics in other process or functional areas.
Non-standardized, highly manual and time-consuming processes that are mostly not supported by analytical layers do not enable a forward-thinking approach. The necessary change of companies’ mindset regarding the use of optimization models and business decision support systems, requires more than just appropriate technology, people and processes. It requires proper change management which allows processes to get less dependent of the stakeholder involved (e.g. planner) and to avoid biased courses of action based on historical practices that may not have a proper fit for today’s volatile environment.
Never before timely decisions were so critical. Unstructured decision-making processes on top of complex organizational structures hinder agility.
By generating in short running times optimal solutions, prescriptive analytics helps us adjusting and improving companies’ response to rapidly changing conditions.
Moreover, as data is being collected almost instantaneously, in many applications there is a need to reduce lead-times and potentially move to real-time decision making (e.g. order picking and order fulfillment related processes).
Prescriptive analytics is the ultimate level of advanced analytics and is critical to gain competitive advantage now and in the future. More accurate and faster decisions, mitigating inherent risks, bring a transformational value into businesses.