Industries have their unique transformation and inflection moments. From a planning perspective, however, the traditional static planning models are making way for more dynamic business models. Secondly, the planning model and technology needs to be highly agile and be connected from a data and micro-services perspective so as to support this agility. Hence, time, speed, and agility are valuable for organizations.
For data-driven decision making, companies need to have the right kind of insight. And for that, they are moving away from descriptive analytics and postmortem analytics to having a lot more predictive insight. Large organizations face a challenge in ensuring communication among different departments and sharing of the information available in different silos to develop a single view of the data available. So, systems need to be more interoperable. Some organizations have moved from demand-based planning to consumption-based planning. Hence, flexibility also plays a role in getting at the right decisions.
Industry leaders feel boundaries have blurred across functions. Whether you are a technology leader or functional leader, you need to have an appreciation of the other side because you are leader of your business, not the leader of a silo anymore. Also, both processes and availability of tools are important for efficient planning and decision making.
Some industries such as pharma need to incorporate variability factors also in their planning cycles and the data they have should be close to real-time. Secondly, the quality of data also matters as it is no longer the structured data that needs to be looked at but the unstructured data as well.