Have you ever heard the Chinese proverb about the farmer? I originally heard it on a podcast featuring Dan Bilzerian.

The story goes:

A farmer and his son had a horse who helped the family earn a living. One day the horse ran away and the farmer’s neighbors said, “Your horse has run away, what terrible luck!” “Maybe,” replied the farmer.

Sometime later, the horse returned with a group of wild mares. The neighbors then said, “Your horse has returned with several other horses, what great luck!” “Maybe,” replied the farmer.

Later that week, the farmer’s son was trying to break one of the mares and she threw him to the ground, breaking his leg. The neighbors said, “Your son has broken his leg, what terrible luck!” “Maybe,” replied the farmer.

A couple of weeks later, the army marched through town recruiting young men. They did not take his son as he was recovering from his injury. The neighbors said, “Your boy was spared, what great luck!”

“Maybe,” the farmer replied.

The point of the story is that you never really know if something is bad or good because you don’t know how it’s going to affect the next step in your life.

I think about this when it comes to the future of demand planning as it relates to Artificial Intelligence (AI) and Machine Learning. How is it going to affect the next step in our career or even our supply chain?

Will AI change demand planning for the better? “Maybe”

AI-Generated Insight Isn’t Enough

About a year ago Daniel Fitzpatrick wrote an article titled Beware the Pitfalls of AI in Demand Planning, published on this website.

He mentions a few concerns but the one that caught my eye was “Forecasts as Proxies for Success”. In the article he says “Forecast accuracy is only a proxy for improved business performance. Without an effective supply chain to support more accurate forecasting, much of the value that an advanced algorithm might add may be lost. An excessive focus on improving forecast accuracy may draw attention and resources away from other constraints that are actually causing larger problems.”

“An accurate forecast does not reduce demand volatility.”

Let’s think about this for a minute. So even with advanced algorithms and heavy investment in AI, an accurate forecast does not reduce demand volatility. To tackle that, you are going to need to understand the root cause, some of which can be prevented and some are completely out of our control. Let’s separate the two.

On one hand, you have the preventative. Communication is at the top of the list. It’s not going to help when your sales team keeps market or customer knowledge to themselves. How about promotions and if they aren’t planned out correctly? They could drive bad consumer behavior, a self-inflicted wound of huge swings in demand. A third is inventory levels. Whether it’s through safety stock or customer inventory-level agreements, with the right strategy, both can help lead to smoother, more forecastable demand.

“Machine Learning Algorithms can only take you so far.”

On other hand, you have the uncontrollable. There’s weather, labor market and wages, raw materials costs and availability, and industrial production specifically as it relates to commodities. The list goes on, a ripple effect that affects all of our supply chains. The majority of my career has been in the CPG space and as much as I would like to claim to say I have seen it all, I am sure you could come up with a story that tops mine. The point is, AI and Machine Learning Algorithms can only take you so far.

“AI improves forecasts based on real-time, internal and external data”

Demand Planning isn’t simple and we can all envision putting ourselves on the imaginary process line of evolution ranging from the basic to the advanced. But unless you are on the top of the food chain, AI and machine learning can likely help. Machine learning can take you to the next level; it enables improved forecasts based on real-time data using internal and external data sources and can turn the uncontrollable into the measurable.

External Data Is Our Friend

External data is our friend and modern machine learning algorithms combined with our supply chain networks can likely outperform processes that are managed solely by Demand Planners. Think about new products. What if AI helped users identify products with similar characteristics ultimately leading to better predictions? Demand Planners can be turned into Super Demand Planners.

Recently I have been getting the impression that there is going to be a shift in our world of Demand Planning. Maybe it’s about finding efficiencies in processes, maintaining lean personnel, or maybe it’s about preserving inventory levels and increasing cash flow. Either way, change is coming and likely in the name of AI.

So, is Artificial Intelligence (AI) to the Rescue for Demand Planning? Maybe.

Maybe we can expand our set of tools and work smarter, not harder. Maybe there is always going to be the uncontrollable and too much data isn’t always a good thing.

When it comes to the term “S&OP”, there is some uncertainty around how it all started. Some say it started with Oliver Wight in the early 1980’s, others say it was Richard Ling and Walter  Goddard in their book Orchestrating Success: Improve Control of the Business with Sales & Operations Planning. I’m not here to debate that because for me, it all started in 2007.

I remember the first S&OP presentation that I saw. It was as basic as you can imagine with Excel graphs copied and pasted into PowerPoint presentations. I am sure some of you are thinking to yourselves “we still do that”. You’re not alone. S&OP is a journey, not a destination and with every journey, it takes time.

Over the next few years, improvements were slow but gradual. We stayed with Excel and PowerPoint for quite some time. Different metrics came and went and unfortunately, so did some Demand Planners. The most important thing stayed constant, though – support from leadership. When you have buy-in from leadership and they truly understand the value behind S&OP, then resources such as personnel and systems start falling into place.

Remember, change is good. Change means stopping doing what’s not working, keeping doing what is working, and always making improvements.

With the support of leadership, we continued to upgrade our forecasting tools, we integrated our systems with both our promotion planning tool and a short term demand sensing tool. Things were starting to click, but we still needed improvement on the presentation side.

In 2019, Microsoft invited a few members from my company, mostly IT people, to their corporate offices in New York City. The point of the meeting was to introduce some of their tools that we weren’t taking advantage of: Yammer, Teams, and Power BI. Fortunately for me, someone couldn’t attend, so the invitation was passed along. So on a cold rainy day in the Fall, I took a short train ride with a coworker, spent the day in the city, and was immediately enamored with Power BI.

If you’re not familiar with Power BI, some say it’s closer to Excel than Microsoft Access but I like to think it’s the best of both worlds. Not only can it handle large amounts of data, like Access, using it’s query editor, but it’s a great tool that has interactive data visualization options that can help tell your S&OP story.

Power Bi was introduced into our S&OP process shortly after the trip.

When this happened, it brought energy back into the process. You could tell there was more excitement than there was in recent years. We were still showing similar data, but we were showing it using our shiny new toy.

We weren’t done yet.

We needed to keep up the momentum so it was time to partner with our friends from IT. We wanted to make the shift from static data to interactive data. We wanted our S&OP meetings to be able to answer questions on the fly. “What was case fill last year?”, “What caused that drop in forecast accuracy last month?”, “How does our inventory this quarter compare to inventory last quarter?”. The objective was to be able to answer these questions at any time.

Luckily, we had the right support. So, by partnering with IT, we were able to directly connect Power BI with data coming from our transactional system and just like that, magic. We had an interactive S&OP presentation. Don’t get me wrong; this took a lot of time and energy.

We now had one Power BI document that could be filtered on a specific Planner’s brands. Not only that, but we could filter our visualizations to show different time periods and we could begin to answer the questions that we previously had to follow up on.

One of the biggest improvements was time. There was no longer the need to make a dozen different PowerPoint presentations with Planners doing the same repetitive work. Instead they could use time more wisely; looking into forecast accuracy misses, explaining gaps to other forecasts, and laying out assumptions. We were working smarter, not harder.

And that is currently where we stand.

Over the last 15 years, I like to think we came a long way. It wouldn’t have been possible without the support and dedication from those involved.

The journey isn’t over. We are further along today than we were yesterday so let’s start thinking about tomorrow.

How To Present Forecasts Properly

Spreadsheets Are Obsolete In The Age of Big Data —What Is Replacing Them?

The Intersection Of Forecasting, Machine Learning & Business Intelligence