July 10, 2023
Demand Planning in today's world: a demand planner's perspective in manufacturing
In this video: A demand planner is frustrated. The video opens with him pouring his heart out about the messy and inefficient process of demand planning. He describes it as a chaotic chore, notorious for being inaccurate, fostering poor team collaboration, and essentially turning the job into a grind. He's had enough, and he's here to tell us how things can get better, with a twinkle of hope in his eyes - a future where machine learning algorithms and leading demand indicators have a place in the process.
First, he walks us through his current process. It starts with him making a round of calls to no less than ten sales teams, gathering updates on sales activities and promotions.
Then it's onto the shared product management folder to download a host of product information, often presented in formats as diverse as a bag of assorted jelly beans. Spotting what has changed from the previous month is like playing a guessing game. And to make matters worse, their demand planning system spits out statistical forecasts that are as rigid as an old pair of jeans, totally ignoring any upcoming sales actions.
Then, he explains how he stitches all these disparate pieces of information together. His tools? A custom spreadsheet and a good old PowerPoint presentation. He crafts a summary of changes from the last cycle for his boss to review and approve. Once he gets the nod, it's a mad dash to pour the spreadsheet data back into their antiquated planning system, just in time for the supply chain teams to download it.
But then, he hits a wall. The supply chain planners often dismiss his carefully crafted forecast. They can't see the logic behind his numbers, the assumptions he painstakingly included in his plan. In response, they go rogue and create their own forecast. The result is chaos, a lack of alignment that drives a wedge between the demand planning and supply chain teams.
Exasperated but not defeated, our demand planner has some ideas up his sleeve. He suggests a radical transformation of the system, one that harnesses the power of machine learning to weave leading indicators of demand into the forecasting process. His vision extends to a more collaborative process, one that ensures everyone is reading from the same script, with equal access to data and insights. He yearns for a future of demand planning that's smarter, more efficient, and more unified. And so do we.
