Forecasting in an Uncertain World

By Chandra Subramanian, Executive Vice-Chairman of ORS GROUP

We live in a world today where we seek decisions made by machines. Machines are steering airplanes; they are deployed for counterintelligence and security, for driving directions, restaurant suggestions, and investment management. In business, machines deliver decisions on mortgage approvals and consumer credits, predict crop yields and the likelihood of diseases and trade stocks.

Today’s businesses survive depending on how effectively they can sense and react to situations in a dynamic and unpredictable world. Influences from geopolitical issues, weather and diseases to rapidly changing customer expectations will require businesses to make decisions at the edge.

Traditional approaches to retailing, which includes forward-looking multi-seasonal planning, merchandising, sourcing and supply-chain decisions, are strangling retail companies. The proof is in the spate of recent bankruptcies. All of these organizations had one common factor — a complete mismatch between demand and supply.

Business models, when examined granularly, are made of two elements: data and decisions. Data, the new oil, is now abundant and getting richer by the minute; and in turn, decisions need to be made rapidly at the edge of the organization — where demand meets supply.

The key to leveraging Artificial Intelligence (AI) in your business is to understand that machines can crunch enormous amounts of data, find patterns and trends in that data, provide scenarios to key decision makers in the value chain, and make those decisions when they need to be made in a split second. Using AI, businesses can move all critical decisions including Capital Budgeting, Merchandising, Sourcing, Supply Chain, Allocation and Fulfillment closer to the market and closer to the season, creating the Supply Chain at the Edge™. Businesses can design multiple levels of supply chain and use machines to suggest mitigation strategies, deciding how — and where — to fulfill a customer request from.

For example, one of our customers, a large global eyeglass manufacturer, uses our solutions to decide what to manufacture, where, when and how much, as well as where to store and where to fulfill demand from. The machine balances the network multiple times during the day and mitigates any disruption to the supply chain. The customer saw zero stockouts; increased customer satisfaction; reduced global inventory; and freed up $200 million in cash flow within six months. AI and Machine Learning will allow organizations to create a cybernetic view of value chains, as opposed to the classical process view, and this will be the key to success in the connected world.

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