What Demand Planners Really Need

Posted on June 7, 2023 by Jeff Harrop

Necessity never made a good bargain. – Benjamin Franklin (1706-1790)

If you ask someone who thinks they know what a retail demand planner needs from a forecasting system, the response will likely be a list of features and gadgets that they believe will make forecasts “more accurate”. On the surface, this makes some sense – a more accurate forecast has greater planning value than a less accurate one.

Based on this perceived need, the hunt is on to buy a shiny new forecasting system for the demand planners to use. After some evaluations, the list is narrowed down to a couple of front runners. You send them your historical sales data and challenge them to a “bakeoff” – whoever produces the most “accurate” weekly forecast over a few cycles wins (or at least significantly improves their odds of winning).

And what do you learn from this process? You learn how good a bunch of nerds working for the pre-sales team are at fine tuning the inner workings of their system to produce the desired result they’re looking for – a new customer for their solution. How many person hours did they spend trying to win the sale? What exactly did they do to the models? Is any of what they did even remotely close to what a real demand planner can (and should) do on a daily basis to manage a large number of forecasts? You probably won’t learn any of this until the implementation team arrives after you sign on the dotted line.

What you will probably also learn is that each of the front runners produces a more accurate forecast for about 50% of the forecasts – likely with no clear reason as to why one did better than the other for a particular item in a particular location in a particular week. After rolling up all of the results, you find that one software provider’s accuracy is 0.89% higher overall than the other for the sample set used.

That’s when someone creates a fancy spreadsheet to “prove” that this extra 0.89% of “accuracy” actually equates to millions of dollars of additional benefits when you multiply it through all items at all locations and do a 10 year net present value on it. It’s all complete nonsense of course, but because it’s based on a tiny kernel of “truth” from the evaluation, it’s given outsized weight.

Fast forward to 3 years later. All of the real business challenges rear their ugly heads during the implementation and are solved with some compromises. Actual demand planners can’t seem to get the same “accuracy” results that were touted in the bakeoff. They don’t really understand all of the inner workings and don’t have the time that the pre-sales team had to fine tune everything in the same way. All of the press releases say that they now use Software X for demand planning, but in reality, most of the real work is being done in Excel spreadsheets, which the demand planners actually know how to use.

Now what if you asked demand planners directly what they actually need from a forecasting system? It’s really only 2 things: Comprehension and control.

Comprehension

When a demand planner is reviewing a system calculated forecast, they want to be able to say one thing: “Given the same inputs as the model, I would have come up with the same forecast on my own.”

That doesn’t mean that they agree with the forecast, it just means that they understand what the model was “thinking” to come up with the result. They don’t need code level understanding of the algorithms in order to do this, just knowledge of how the model interprets data and how it can be influenced.

Before they move a dial or switch to alter the model, they want to be able to reliably predict the outcome of their actions.

Control

So long as the behaviour of the model can be understood, a demand planner will want to work with it to get the output they want, rather than just give up and work against it with manual overrides they calculated in Excel.

Knowing what the model did and why it did it is important, but demand planners also need to know how to affect changes in the model to make it behave differently, but also predictably so that the system will produce forecasts that they agree with and for which they are willing to be held accountable.

Accuracy is a rearview mirror measure. Demand planners need to be able to live in the future, not the past. In order to support them, a forecasting system needs to be both understandable and directly controllable so that they can fully accept accountability for the outcome.

Probabilistic Forecasting – One Man’s (Somewhat Informed) Opinion

Posted on April 5, 2023 by Jeff Harrop

A reasonable probability is the only certainty. – E. W. Howe

My, how forecasting methods for supply chain planning have evolved over time:

Naive, flat line forecasts (e.g. moving averages) were once used to estimate demand for triggering orders.
Time series decomposition type mathematical models added more intelligence around detecting trends and seasonality to enable better long term forecasting.
Causal forecasting models allowed different time series to influence each other (e.g. the effect of future planned price changes on forecasted volumes)

All of these methods are deterministic, meaning that their output is a single value representing the “most likely outcome” for each future time period. Ironically, the “most likely outcome” almost never actually materializes.

This brings us to probabilistic forecasting. In addition to calculating a mean (or median) value for each future time period (can be interpreted as the most likely outcome), probabilistic methods also calculate a distinct confidence interval for each individual future forecast period. In essence, instead of having an individual point for each time period into the future, you instead have a cloud of “good forecasts” for various types of scenario modeling and decision making.

But how do you apply this in supply chain management where all of the physical activities driven by the forecast are discrete and deterministic? You can’t submit a purchase order line to a supplier that reads “there’s a 95% chance we’ll need 1 case, a 66% chance we’ll need 2 cases and a 33% chance we’ll need 3 cases”. They need to know exactly how many cases they need to pick, full stop.

The probabilistic forecasting approach can address many “self evident truths” about forecasting that have plagued supply chain planners for decades by better informing the discrete decisions in the supply chain:

That not only is demand variable, but variability in demand is also variable over time. Think about a product that is seasonal or highly promotional in nature. The amount of safety stock you need to cover demand variability for a garden hose is far greater in the summer than it is in the winter. By knowing how not just demand but demand variability changes over time, you can properly set discrete safety stock levels at different times of the season.
That uncertainty is inherent in every prediction. Measuring forecasts using the standard “every forecast is wrong, but by how much” method provides little useful information and causes us to chase ghosts. By incorporating a calculated expectation of uncertainty into forecast measurements, we can instead make meaningful determinations about whether or not a “miss” calculated by traditional means was within an expected range and not really a miss at all. The definition of accuracy changes from an arbitrary percentage to a clear judgment call, forecast by forecast, because the inherent and unavoidable uncertainty is treated as part of the signal (which it actually is), allowing us to focus on the true noise.
That rollups of granular unit forecasts by item/location to higher levels for capacity and financial planning can be misleading and costly. The ability to also roll up the specific uncertainty by item/location/day allows management to make much more informed decisions about risk before committing resources and capital.

Now here’s the “somewhat informed” part. In order to gain widespread adoption, proponents of probabilistic methods really do need to help us old dogs learn their new tricks. It’s my experience that demand planners can be highly effective without knowing every single rule and formula driving their forecast outputs. If they use off the shelf software packages, the algorithms are proprietary and they aren’t able to get that far down into the details anyhow.

What’s important is that – when looking at all of the information available to the model – a demand planner can look at the output and understand what it was “thinking”, even if they may disagree with it. All models make the general assumption that patterns of the past will continue into the future. Knowing that, a demand planner can quickly address cases where that assumption won’t hold true (i.e. they know something about why the future will be different from the past that the model does not) and take action.

As the pool of early adopters of probabilistic methods grows, I’m looking forward to seeing heaps of case studies and real world examples covering a wide range of business scenarios from the perspective of a retail demand planner – without having to go back to school for 6 more years to earn a PhD in statistics. Some of us are just too old for that shit.

I see great promise, but for the time being, I remain only somewhat informed.

Your Sales Plan is NOT a Forecast!

Posted on August 4, 2021 by Jeff Harrop

Man is the only animal that laughs and weeps, for he is the only animal that is struck with the difference between what things are and what they ought to be. – William Hazlitt (1778-1830)

A Ferrari has a steering wheel. A fire truck also has a steering wheel.

A Ferrari has a clutch, brake and accelerator. A fire truck also has a clutch, brake and accelerator.

Most Ferraris are red. Most fire trucks are also red.

A new Ferrari costs several hundred thousand dollars. A new fire truck also costs several hundred thousand dollars.

Ergo, Ferrari = Fire Truck.

That was an absurd leap to make, I know, but no more absurd than using the terms “sales plan” and “sales forecast” interchangeably in a retail setting. Yes, they are each intended to represent a consensus view of future sales, but that’s pretty much where the similarity ends. They differ significantly with regard to purpose, level of detail and frequency of update.

Purpose

The purpose of the sales plan is to set future goals for the business that are grounded in strategy and (hopefully) realism. Its job is to quantify and articulate the “Why” and with a bit of a light touch on the “What” and the “How”. It’s about predicting what we’re trying to make happen.

The purpose of the operational sales forecast is to subjectively predict future customer behaviour based on observed customer demand to date, augmented with information about known upcoming occurrences – such as near term weather events, planned promotions and assortment changes – that may make customers behave differently. It’s all about the “What” and the “How” and its purpose is to foresee what we think is going to happen based on all available information at any one time.

Level of Detail

The sales plan is an aggregate weekly or monthly view of expected sales for a category of goods in dollars. Factored into the plan are category strategies and assumptions (“we’ll promote this category very heavily in the back half” or “we will expand the assortment by 20% to become more dominant”), but usually lacking in the specific details which will be worked out as the year unfolds.

The operational sales forecast is a detailed projection by item/location/week in units, which is how customers actually demand product. It incorporates all of the specific details that flow out of the sales plan whenever they become available.

Frequency of Update

The sales plan is generally drafted once toward the end of a fiscal year so as to get approval for the strategies that will be employed to drive toward the plan for the upcoming year.

The operational sales forecast is updated and rolled forward at least weekly so as to drive the supply chain to respond to what’s expected to happen based on everything that has happened to date up to and including yesterday.

“Reconciling” the Plan and the Forecast

Being more elemental, the operational forecast can be easily converted to dollars and rolled up to the same level at which the sales plan was drafted for easy comparison.

Whenever this is done, it’s not uncommon to see that the rolled up operational forecast does not match the sales plan for any future time period. Nor should it. And based on the differences between them discussed above, how could it?

This should not be panic inducing, rather a call to action:

“According to the sales plan that was drafted months ago, Category X should be booking $10 million in sales over the next 13 weeks.”

“According to the sales forecast that was most recently updated yesterday to include all of the details that are driving customer behaviour for the items in Category X, that ain’t gonna happen.”

Valuable information to have, is it not? Especially since the next 13 weeks are still out there in a future that has yet to transpire.

Clearly assumptions were made when the sales plan was drafted that are not coming to pass. Which assumptions were they and what can we do about them?

While a retailer can’t directly control customer behaviour (wouldn’t that be grand?), they have many weapons in their arsenal to influence it significantly: advertising, pricing, promotions, assortment, cross-selling – the list goes on.

The predicted gap between the plan and the forecast drives tactical action to close the gap:

Maybe it turns out that the tactics you employ will not close the gap completely. Maybe you’re okay with it because the category is expected to track ahead later in the year. Maybe another category will pick up the slack, making the overall plan whole. Or maybe you still don’t like what you’re seeing and need to sharpen your pencil again on your assumptions and tactics.

Good thing your sales plan is separate and distinct from your sales forecast so that you can know about those gaps in advance and actually do something about them.

I’m From Missouri

Posted on April 5, 2017 by Jeff Harrop

“I am from a state that raises corn and cotton and cockleburs and Democrats, and frothy eloquence neither convinces nor satisfies me. I am from Missouri. You have got to show me.” – William Duncan Vandiver, US Congressman, speech at 1899 naval banquet

“How are you going to incorporate Big Data into your supply chain planning processes?”

It’s a question we hear often (mostly from fellow consultants).

Our typical response is: “I’m not sure. What are you talking about?”

Them: “You know, accessing social media and weather data to detect demand trends and then incorporating the results into your sales forecasting process.”

Us: “Wow, that sounds pretty awesome. Can you put me in touch with a retailer who has actually done this successfully and is achieving benefit from it?”

Them: <crickets>

I’m not trying to be cheeky here. On the face of it, this seems to make some sense. We know that changes in the weather can affect demand for certain items. But sales happen on specific items at specific stores.

It seems to me that for weather data to be of value, we must be able to accurately predict temperature and precipitation far enough out into the future to be able to respond. Not only that, but these accurate predictions need to also be very geographically specific – markets 10 miles from each other can experience very different weather on different days.

Seems a bit of a stretch, but let’s suppose that’s possible. Now, you need to be able to quantify the impact those weather predictions will have on each specific item sold in each specific store in order for the upstream supply chain to respond.

Is that even possible? Maybe. But I’ve never seen it, nor have I even seen a plausible explanation as to how it could be achieved.

With regard to social media and browsing data, I have to say that I’m even more skeptical. I get that clicks that result in purchases are clear signals of demand, but if a discussion about a product is trending on Twitter or getting a high number of page views on your e-commerce site (without a corresponding purchase), how exactly do you update your forecasts for specific items in specific locations once you have visibility to this information?

If you were somehow able to track how many customers in a brick and mortar store pick up a product, read the label, then place it back on the shelf, would that change your future sales expectation?

Clearly there’s a lot about Big Data that I don’t know.

But here’s something I do know. A retailer who recently implemented Flowcasting is currently achieving sustained daily in-stock levels between 97% and 98% (it was at 91% previously – right around the industry average). This is an ‘all in’ number, meaning that it encompasses all actively replenished products across all stores, including seasonal items and items on promotion.

With some continuous improvement efforts and maybe some operational changes, I have no doubt that they can get to be sustainably above 98% in stock. They are not currently using any weather or social media Big Data.

This I have seen.

Only a few

Posted on May 5, 2016 by Mike Doherty

Flowcasting has often been referred to as ‘the Holy Grail’ of demand driven supply chain planning (and rightly so). Driving the entire supply chain across multiple enterprises from sales at the store shelf right back to the factory.

So is Flowcasting a retail solution or a manufacturing solution? Many analysts, consultants and solution providers have been positioning Flowcasting as a solution for manufacturers.

They’re wrong.

While it’s true that some manufacturers have achieved success in using data from retailers to help improve and stabilize their production schedule, the simple fact is that manufacturers can’t achieve huge benefits from Flowcasting until they are planning a critical mass of retail stores and DCs where their products are sold and distributed.

For a large CPG manufacturer, this means collecting data and planning demand and supply across tens of thousands of stores across multiple retail organizations, all of which have their own ways of managing their internal processes.

At the end of the day, a manufacturer initiated Flowcasting implementation results in what amounts to a decision support/reporting system that isn’t directly integrated to the actual product movements that will occur from the factory to the store shelf.

Flowcasting is a retail solution that will greatly benefit manufacturers over time as more and more of their retailer customers adopt the concept.

Flowcasting is not a data collection and calculation exercise. It’s a planning philosophy that requires the folks on the front end of the supply chain (retailers) to change most of their business practices to be forward looking, such as:

Assortment planning and line review (including planogram resets)
Seasonal planning
Promotions planning
Network realignments (including store-to-DC network mappings and changes of source)

The retailer holds complete control over all of the above decisions. To the extent that they can change their processes to plan these activities in advance (and share those plans with manufacturers in a language they can understand), everyone in the supply chain benefits.

To the extent that folks on the back end of the supply chain (manufacturers) attempt to ‘work around’ retailer customers who are not thinking or operating in a time-phased manner, we are still left with a disconnected supply chain (perhaps with fancier tools).

You can’t push a rope, as they say.

The perception that Flowcasting is a manufacturing solution has led many people to conclude that Flowcasting is really applicable to only a few companies. If that perception were true, then the conclusion would be correct – but it’s not.

When we conceived of Flowcasting, we were really outlining the concept of totally integrating a retail supply chain – from point of consumption (consumers) to point of origin (the manufacturer’s manufacturers). I believe it’s why we called the book “Flowcasting the Retail Supply Chain”.

Now, the last time I checked there were more than only a few retailers.

Does Flowcasting apply to virtually every retailer? I believe it does. After all, don’t they sell products to consumers using physical stores, virtual stores, or a combination of both and supply those products via a network of distribution points? Couldn’t that be Flowcasted? Shouldn’t it be?

Our retail client in Winnipeg Canada is managing their entire business driven by a forecast of consumer demand, by item, by store (including web store), and translating those forecasts into the demand, supply, capacity and financial requirements for a 52 week planning horizon – including sharing purchase projections with their suppliers.

They have implemented and are doing what’s outlined in our book. They are Flowcasting.

Another misconception about Flowcasting is that all of the data must be in one place and being used by planners from both the retailer and manufacturer organizations. While this is an admirable (and likely achievable) goal, the Flowcasting planning process can be (and has been) achieved without it.

Flowcasting is about seamlessly integrating the entire retail supply chain from one forecast and working a common plan and a single set of numbers. Can and should a retailer manage their business this way? Without question and our retail client has proven it.

The point is that separate companies can be using the same numbers and executing the same plan without logging into the same system. We need to collectively get a grip on this and learn to determine the difference between what’s cake and what’s icing (and in this particular case, a few sprinkles on top of the icing).

To extend the thinking of Flowcasting even further, consider Flowcasting as a concept and a philosophy. A philosophy to drive the entire, integrated supply chain from a forecast at the point of consumption.

A couple of years ago, I had the pleasure to visit One Network Enterprises at their Dallas headquarters to talk about supply chain planning. Inevitably we got talking about Flowcasting.

During the conversations, Aaron Pittman and Richard Dean proclaimed to me that Flowcasting, as a concept, had widespread application. They insisted that the concept of driving a supply chain from point of consumption to point of origin applied to any industrial supply chain.

If you think about it, they are right.

Had we spoken to them before we wrote the book, undoubtedly we would have more aptly named it “Flowcasting the Supply Chain”.

So if you think the concept of Flowcasting applies to only a few companies…you’re right…Flowcasting does apply to only a few…

Only a few thousand!

Cross Purposes

Posted on February 4, 2015 by Jeff Harrop

A compromise is the art of dividing a cake in such a way that everyone believes he has the biggest piece. – Ludwig Erhard (1897 – 1977)

Last week, I had the chance to catch up with a good friend and colleague of many years. His name is Ian and he is the VP of Supply Chain at a mid-sized retailer (with vast prior experience at a large retailer).

After a couple of beers, he asked me point blank: “Forecasting and Replenishment. One job or two?”

Without hesitation, I responded “Two!” Then I proceeded to describe the differences in skill sets, business relationships and aptitudes between a Demand Planner and a Supply Planner.

“Okay”, he said. “Who does the Demand Planning group report to?”

That’s a very interesting question indeed.

The goal of the demand planning process is to create a sales forecast that is as accurate and unbiased as possible. In retail, the process of coming up with a forecast is typically a “joint effort” between the Category Manager and the Supply Chain Planner.

The Category Manager is measured primarily on sales. Therefore he/she has a tendency to make optimistic projections and bias the forecast upward, knowing that a higher forecast will buy more inventory, thereby (theoretically) reducing the likelihood of lost sales.

The Supply Chain Planner is measured primarily on inventory turns. Therefore he/she has a tendency to “keep the forecast lean” to avoid carryover inventory after a promotion or selling season.

Therein lies the rub. If you give control of the forecasting process to the Merchandising group, Supply Chain feels like you’re “putting the fox in charge of the henhouse”. If the forecasting process falls under Supply Chain, Merchandising feels like they have no control over one of the key inputs that drive their businesses.

So should the Demand Planning function reside within Merchandising or Supply Chain?

Neither.

Think about it. Neither group can be faulted for exhibiting behaviour on which they are rewarded. The problem is that they have competing objectives and the biases on either side can have a direct negative impact on the P&L.

That’s why the Demand Planning function needs to report to someone who has accountability for the entire P&L – either the CEO or, more practically, the CFO.

Remember the goal for the sales forecast: As accurate and as unbiased as possible. By having Demand Planners report into Finance, they can be effective mediators between the competing groups and would have “a seat at the table” when matters relating to the sales forecast are discussed (promotions, product launches, safety stock policies, etc.)

Their job would be to chair S&OP meetings with their Merchandising and Supply Chain counterparts and hear both sides of the story with an objective ear. Without either group having the direct ability to impose their biases on the forecast, they must instead make a convincing case to the Demand Planner to support their view.

As a consequence, the current “blurred lines” of accountability are made clear once and for all:

Merchandising: Stimulate demand and be accountable for sales and gross margin.

Demand Planning: Forecast demand and be accountable for forecast quality.

Supply Chain: Optimize supply and be accountable for inventory turns and availability.

Mentally Separating Demand and Supply

Posted on September 20, 2012 by Mike Doherty

In order for the replenishment planning process to be as flexible as possible going into the future, it is imperative that a clear distinction be made between demand planning and supply planning.

The goal of the demand planning process is to predict behaviour of customers. Customer demand cannot be directly controlled and must be taken as given in the supply chain. We do our best to predict this behaviour and schedule resources (i.e. supply) as efficiently as possible to support the wants of customers. When we have our demand planning hats on, the furthest thing from our minds should be considering how this demand will be satisfied. Demand is demand, and it is not subject to constraints. This is what keeps the demand planning process as pure as possible.

Once you have a handle on your demand, proper supply decisions can be made to ensure that demand is satisfied. That’s the mental separation — demand is not within our scope of control and must be taken as given, while supply decisions can be made and remade in order to best satisfy changing demand patterns.