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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.

Hi. I'm a demand planner in a global manufacturing company. Management isn't happy with the forecast I've generated. I get grief for management on how our poor forecast accuracy is resulting in excess inventory and poor on shelf availability.

You see, a lot of the stress associated with the planning process is targeted at me, and nobody has any idea what I do every week to come up with those numbers.

At the beginning of the week, I make phone calls to ten different sales to collect new sales activity and promotion information.

Some give me spreadsheets that they've created, but they're all formatted differently.

Some even give me information verbally. Others don't even bother returning my calls. Then I have to give updates on new product launches and costs.

I go to the product management shared folder to download information for our products, again, more spreadsheets, all in different formats.

I'm really struggling to tell what's changed from last month, and the fun part is putting all of this data into our demand planning system. It makes a statistical forecast based on past data, but the numbers just don't add up.

How could they when they don't take into account any sales action we're planning to take in the future. I put all the information together in my custom spreadsheet. Then I create a PowerPoint summary for my boss on what's changed from the last cycle so that he can review and approve it. It's approved. Great. I then scrambled to load my spreadsheet back into our outdated planning system, so our supply chain teams can download the forecast and drive their supply chain planning activity.

Now, here's the final kicker. I find out that our supply chain planners are ignoring the forecast I provide because they don't see any of the assumptions I made in my plan. So they go ahead and create their own forecast because they don't want to be stuck with inventory and get blamed for it. But with all those different forecast numbers floating around, Management doesn't even know who should be held accountable.

There must be a better way. Something with the ability to create accurate forecasts, somehow incorporate leading indicators of demand, machine learning algorithms, insightful plans, something that makes work a bit more fun.

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