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0:08:03Retail Demand Planner Frustrations: How to Improve Forecast Accuracy and Collaboration
In this video, a demand planner shares their frustrations with the current demand planning process. He explains how they currently generate forecasts. He says that they start by making phone calls to ten different category management teams to collect new sales activity and promotion information. He then has to go to the merchandising shared folder to download information for their products, which is often in different formats. He said that it is difficult to tell what has changed from last month, and that the statistical forecast that their demand planning system generates does not take into account any sales actions that they are planning to take in the future.
The demand planner then explains how they put all of this information together in a custom spreadsheet. He then creates a PowerPoint summary for their boss on what has changed from the last cycle so that they can review and approve it. Once the forecast is approved, the demand planner has to scramble to load their spreadsheet back into their outdated planning system so that the supply chain teams can download it.
The demand planner then explains that the supply chain planners often ignore the forecast that they provide because they do not see any of the assumptions that the demand planner made in their plan. As a result, the supply chain planners create their own forecast. This leads to confusion and poor collaboration between the demand planning and supply chain teams.
The demand planner concludes by saying that there must be a better way to generate forecasts. He suggests using machine learning algorithms to incorporate leading indicators of demand into the forecasting process. He also suggests making the forecasting process more collaborative by giving all of the stakeholders access to the same data and insights.
Hi. I'm a demand planner in a retail 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, markdowns, poor on shelf availability and long lead times in our e commerce channel.
You see, a lot of the stress associated with the planning process is targeted at and nobody has any idea what I do every week to come up with these numbers.
At the beginning of the week, I make phone calls to ten different category management teams to collect new sales activity and promotion information.
Some give me spreadsheets that they created, but each one is formatted differently.
Some give me information verbally. Others don't even bother returning my calls. Then I have to give updates to new product launches and costs. I go to the merchandising shared folder to download information for our products, again, more spreadsheets all in different formats.
I'm struggling to tell what's changed from last month, and then 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 actions 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 last cycle, so he can review and approve it. It's approved. Great. I then scramble to load the spreadsheet back into our outdated demand plan system, so our supply chain teams can download the forecast and drive their replenishment planning activity.
Now, here's the final kicker. I find out that our replenishment planners are ignoring the forecast I spent all this time providing because they don't see any of my assumption 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 of these 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 forecasts, incorporate leading indicators of demand, machine learning algorithms, insightful plans, something that makes work a bit more fun.
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