We Don’t Need a Ferrari

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

When a retailer seriously embarks on an effort to completely reshape how they plan the flow of goods from supplier to shelf, the discussion inevitably turns to what software they will need to do the job. (And ideally, this isn’t Step 1 of the process).

As the time approaches to evaluate software vendors, someone in company leadership is bound to utter the phrase “We don’t need a Ferrari”. After that, everyone in the room will nod their heads sagely in agreement. You can almost set your watch by it.

The message they’re trying to send is “We don’t need unnecessary sophistication and we don’t want to spend a ridiculous amount of money. We just need to get the basics right.”

I believe the intention is correct. You don’t want the design and implementation team to go off on a wild search for the most sophisticated system they can find – whether or not it’s proven or even necessary. But the advice may not be as useful as you think.

People who are tasked with transforming supply chain planning generally don’t need to be constrained or reined in. They need to be led. By the time you get to this point, you should already have assembled a team with strong convictions and a bias toward pragmatism – they won’t run around chasing shiny objects. Sometimes they will need leadership to be led by them.

Using well-meaning platitudes like “we don’t need a Ferrari” doesn’t really clarify anything and could potentially lead them down the road of picking a simplistic system over a simple one.

The team needs to understand time-tested and proven planning principles, what the true requirements of the organization are (including taking into account future strategic direction) and what results they are expected to deliver.

A system with a simple data structure, easy navigation and limited options that doesn’t adhere to solid planning principles and doesn’t meet the requirements will not deliver results.

Just because some functionality may be more complex or sophisticated than what you have today, that doesn’t make it “too fancy”, unless the mandate is to implement a new system and process that does the same thing you’ve always done (with the same results). Not all sophistication is unnecessary.

Just because people will need to acquire more skills – some of which may be difficult to learn – doesn’t mean that the system or process is “too complicated”.

No, you do not need a Ferrari – because nobody “needs” a Ferrari. 

But there is a wide range of options between a Ferrari and a tricycle. Your requirements need to dictate whether you need a Corolla, a minivan or a pickup truck.

Don’t choose a tricycle just because it’s the farthest option away from a Ferrari. A simplistic system that doesn’t meet requirements makes the implementation just as complicated as an over-engineered system that you don’t need.

A Rather Unassuming Approach

When we make assumptions, we contribute to the complexity rather than the simplicity of a problem, making it more difficult to solve. – Julie A., M.A. Ross and Judy Corcoran

Planning the retail supply chain not only requires, but is entirely predicated upon making assumptions about the future.

Why?

Because when a customer walks into a store, they already expect the products they want to be on the shelf in sufficient quantity to satisfy their demand – and they give no advance notice of their planned visit. So depending on the cumulative lead times from the ultimate source of supply to the store shelves, the decisions you make regarding product movements today must be made based on what you anticipate (i.e. assume) customers will want – how much, where and when – days, weeks or even months into the future.

So for retailers of any size, that could add up to millions of assumptions that need to be made each day just for the expected consumer demand element alone. And each assumption you make is a risk – if an assumption doesn’t hold, then the decisions you made based on that assumption will cost you in some way.

If the supply chain is disconnected, there are more assumptions to be made. So there are greater risks and higher costs in the form of customer service failures and/or inefficient use of labour and capital.

As an example, it’s not uncommon for a retailer to have different planning and replenishment systems for stores and distribution centres. And those systems usually have a “what do I need to request today?” focus – think min/max or reorder point.

The store replenishment problem is relatively straightforward:

  1. What is the current on hand in the store minus the display minimum (or “cycle stock” for want of a better term)?
  2. What do you anticipate (assume) you will sell between now and the next scheduled delivery day?
  3. If what you expect to sell exceeds the cycle stock, request the difference, rounded to the nearest ship pack

In this case, the only assumption you’re making is the expected sales, with a few “sub-assumptions” with regard to trend, seasonality, promotional activity, etc. going into that.

(NOTE: You are also assuming that your store on hand balance is accurate, which is a whole other lengthy discussion in and of itself.)

Okay, so far so good. Now we need to make sure that there will be sufficient stock in the distribution centre to satisfy the store requests. Because the supply chain is disconnected, the requests that the stores drop onto the DC today need to be picked within a day or two, so that means that the DC must anticipate (assume) what the stores will request in advance.

A common way to do this is to use historical store requests to forecast future DC withdrawals. In this case, you are making a number of additional assumptions:

  • That store inventories are largely balanced across all stores served by the DC and have been so historically
  • That any growth/decline in consumer sales will be accurately reflected in the DC withdrawals with a consistent lag
  • That there are no expected changes in store merchandising requirements that will increase or decrease their need for stock irrespective of sales

Going further back to the supplier, they have their own internal planning processes whereby they are trying to guess what each of their retailer customers are going to want from them in order to plan their inventories of finished goods. They are now several steps removed from the ultimate consumer of their products and have to apply their own additional set of assumptions.

It’s like a game of telephone where each successive person in the queue passes what they think they heard on to the next.

And if something doesn’t go according to plan, a whole bunch of people need to revisit their assumptions to figure out where the breakdown happened. At least they should, but that rarely happens. Everyone is too busy dealing with the fallout in “crisis mode” to actually figure out what went wrong.

The result?

  • In stock rates to the consumer in the 92-93% range
  • Excessive amounts of “buffer stock” to try to cover for all of the self-inflicted uncertainty (assumptions) in the process
  • Margin loss from taking markdowns on excess stock that’s in the wrong place at the wrong time

So how does an approach like Flowcasting – a fully integrated end-to-end planning process – sustain in-stocks in the high 90s while simultaneously (and significantly) reducing stock levels throughout the supply chain?

It’s not magic. By connecting the supply chain with long term supply projections and keeping those projections up to date, the number of assumptions you need to make are drastically reduced:

  • You already know the inventory for every item at every location, so you can model the long term need for each individually and roll them up rather than assuming averages.
  • The planning approach automatically models the impact of any changes in consumer demand by netting against available stock in store and applying the necessary constraints and rounding rules using simple calculations – there’s no need to guess how a changing demand picture will affect upstream supply.
  • Inventory level decisions (e.g. changes to display quantities and off locations) can be discretely modeled separately from demand and incorporated directly into the store projections in a time-phased manner. You don’t need to make a “same as last year” assumption if you already know that won’t be the case.

However, it’s not perfect and things can still go wrong. You still need to have long term forecasts about consumer demand, which means assumptions still need to be made. But when bad things happen, the information travels quickly and transparently up and down the supply chain assumption-free after that. Everyone knows exactly how they are affected by any botched assumptions about consumer demand in near real time and can start course correcting much sooner. “Bad news early is better than bad news late.”

It’s like playing telephone, except that the first player doesn’t whisper to the next – he uses a megaphone to ensure that everyone hears the same phrase at the same time.

A Forecast By Any Other Name

What’s in a name? That which we call a rose
By any other name would smell as sweet. – William Shakespeare (1564-1616), Romeo and Juliet, Act 2 Scene 2

Scenario 1: A store associate walks down the aisles. She sees 6 units of an item on the shelf and determines that more is needed on the next shipment, so she orders another case pack of 12 units.

Scenario 2: In the overnight batch run, a centralized store min/max system averages the last 6 weeks of sales for every item at every store. This average selling rate is used to set a replenishment policy – a replenishment request is triggered when the stock level reaches 2 weeks’ worth of on hand (based on the 6 week average) and the amount ordered is enough to get up to 4 weeks’ worth of on hand, rounded to the nearest pack size.

Scenario 3: In the overnight batch run, a centralized store reorder point system calculates a total sales forecast over the next 2 shipping cycles. It uses 2 years’ worth of sales history so that it can capture a trend and weekly selling pattern for each item/store being replenished and calculate a proper safety stock based on demand variability. On designated ordering days, the replenishment system evaluates the current stock position against the total of expected sales plus safety stock over the next two ordering cycles and triggers replenishment requests as necessary to ensure that safety stock will not be breached between successive replenishment days.

Scenario 4: In the overnight batch run, a centralized supply chain planning system calculates a sales forecast (with expected trend and weekly selling pattern) for the next 52 weeks. Using this forecast, merchandising minimums, store receiving calendars and the current stock position, it calculates when future arrivals of stock are needed at the store to ensure that the merchandising minimums won’t be breached over the next 52 weeks. Using the transit lead time, it determines when each of those planned arrivals will need to be shipped from the supplying distribution centre over the next 52 weeks. The rolled up store shipment projections become the outbound plans for each item/DC, which then performs the same logic to calculate when future inbound arrivals are needed and their corresponding ship dates. Finally the projected inbound shipments to the DC are communicated to suppliers so that they can properly plan their finished goods inventory, production and raw material procurement. For both stores and DCs, the plans are turned into firm replenishment requests at the ordering lead time.

With that out of the way, let’s do some audience participation. I have a question for you: Which of the above replenishment methods are forecast based? (You can pause here to scroll up to read each scenario again before deciding, or you can just look down to the very next line for the answer).

The answer is… they are ALL forecast based.

Don’t believe me?

In Scenario 1, how did the store associate know that a visual stock position of 6 units meant they were “getting low”? And why did she order a single case of 12 in response? Why didn’t she wait until there were 3 units? Or 1 unit? And why did she order 12? Why not 120?

For Scenario 2, you’re probably saying to yourself: “Averaging the past 6 weeks’ worth of sales is looking backward – that’s NOT forecasting!” Au contraire. By deciding to base your FUTURE replenishment on the basis of the last 6 weeks’ worth of sales, an assumption is being made that upcoming sales will be similar to past sales. That assumption IS the forecast. I’m not saying it’s a good assumption or that it will be a good forecast. I’m just saying that the method is forecast based.

Using the terms “trend” and “selling pattern” in Scenarios 3 and 4 probably spoiled the surprise for those ones.

So why did I go through such pains to make this point?

Quite simply, to counter the (foolish and naive) narrative that “forecasts are always wrong, so you shouldn’t bother forecasting at all”. 

The simple fact is that unless you are in a position where you don’t need to replenish stock until AFTER your customer has already committed to buying it, any stock replenishment method you use must by definition be forecast based. I have yet to run across a retailer in the last 28+ years that has that luxury.

A forecast that happens in someone’s head, isn’t recorded anywhere and only manifests itself physically as a replenishment request is still a forecast.

An assumption that next week will be like the average of the last few weeks is still a forecast.

As the march continues and retailers gradually transition from Scenarios 1, 2 or 3 to Scenario 4, the forecasting process will become more formalized and measurable. And it can be a lot of work to maintain them (along with the replenishment plans that are driven by them). 

But the overall effort pays off handsomely. Retailers in Scenarios 1, 2 and 3 have experienced in stock rates of 92-93% with wild swings in inventory levels and chronic stock imbalances. This has been documented time and again for 30 years.

Only by formalizing your forecasting your forecasting process, using those forecasts to drive long term plans and sharing those plans up and down the supply chain can you achieve 97-98% in stock while simultaneously reducing inventory investment, with reduced overall effort.