We are moving towards a highly connected global marketplace with oceans of data and information at our fingertips. It is said that 90% of data available to the world today, has been created over the last couple of years. With data becoming the single most important ingredient in doing business, tools and techniques such as AI, machine learning, predictive analysis and big data have become the hot topics among demand planners. At the same time, planners have become a bit sceptical about their job security, questioning whether a robot running on an AI base would take their job in near future. A survey done by the International Business Organization(IBF) has shown that 70% of demand planners have the said scepticism and as a result, embracing and actioning of above hot topics have become a challenge to employers, across the globe.
Companies, irrespective of their industry or domicile, make decisions based on forward-looking projections. This is fundamental. As long as this fundamental remains intact, I believe demand planners' jobs will not be at risk. The fact of the matter is, it's the demand planners who craft a story around their numbers and paint a picture filled with valuable insights so that the board could make informed decisions. All above tools and techniques(the tool being an AI and techniques being its enables such as machine learning, predictive analysis and big data) are going to better generate information leading to vital insights. Therefore demand planners would need to try to embrace the next wave of demand planning, where the predominant task of a demand planner would be generating high-level interpretations from a plethora of segmented information. The challenge therefore would be to shift the thinking of being an operational manager to a portfolio manager. However, the point to note would be the importance of having demand planning fundamentals strongly in place because no matter how advanced demand planning is going to be, the use of fundamentals would be of the same importance to be successful at the job. Research done by IBF shows that a 15% improvement in Forecast Accuracy would lead to a 3% increase in profits, globally. This goes to show the importance of getting the set of fundamentals right.
In my opinion, the biggest challenge would have to be borne by the employers. Automation, especially in the South Asian context comes with the end objective of reducing headcount. However, automation does not need to couple with headcount reduction always. Even if it does, remaining staff members should be upgraded, not compensated and sent home. In the demand planning context, implementation of AIs should happen only if current demand planners have acquired sufficient skills to be better interpreters, better storytellers and better portfolio managers. On the other hand, with data starting to be available at product/outlet/daily level, having monthly planning cycles is also going to be detrimental. Companies will have to amend their planning cycles. With e-commerce becoming increasingly popular, traditional planning approaches and forecasting techniques will have to be revised. Having powerful AIs with the same old planning practices will not be able to take demand planning functions forward at all. Therefore, it's the responsibility of the employer to feed this information to planners while helping them to change their mindsets to embrace upcoming changes. Proper change management processes would be required to ensure smooth transitions from current practices to new practices. Without all such initiatives, there would not be any result other than the bubble reputation of implementing an AI.
In conclusion, I would suggest demand planners to be optimistic about the massive next wave that is about to happen in their function while recommending top tier management to prudently guide their staff towards the change.
Kalindu Matheesha Somatilaka
