Optimization is designed to solve the pressure points in operations where there are many different moving parts and resources are spread thin. It is for the equations that are too complex for humans to solve.
Managers often have to make critical decisions under tight time constraints based on imperfect knowledge. By crunching the numbers, optimization models are able to point towards more favorable decisions, and provide a fuller picture of operations well into the future. Data science can create any crucial data that’s missing and feed it into the optimization system, generating more accurate forecasts. That allows for further efficiencies that before now have never been possible.
At Fourkind, part of Thoughtworks, we’ve revolutionized many industries that before had not yet made the optimization leap. We’ve helped build the world’s first optimized airport and brought in the disruptive technology to sectors as varied as retail, forestry, beauty, and health. For our clients, operational optimization has resulted in more accurate forecasts, major savings, enhanced operational speed, and improved decision making.
Before, optimization and data science sat as two separate functions. Now, they can work in tandem. By combining operations data with machine learning, and by building a model, operational forecasts can become more accurate and stretch further out into the future. It is possible to predict with startling accuracy for example the daily demand for each product at every individual store, for the next year.
Increased efficiency from optimization means greater savings, and not only in monetary terms. Optimization can eliminate waste, bring down emissions, and streamline workflow for overburdened employees.
Automation means that a business’ operations can react to sudden demand and supply changes in real-time. The system automatically adjusts to reflect the changing variables, be it inventory, occupancy rate, or some other critical aspect of operations. That big picture view then aids all organization-level decision making.
When decisions are based on analytical data rather than gut instinct, it is easier and quicker to find the best way forward, and secure widespread organizational buy-in for that decision.