I’m From Missouri

 

“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

missouri

“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.

Respect the Fat Lady

It ain’t over ’til the fat lady sings. – Modern Proverb

opera

When is it too late to update a forecast?

Here’s a theoretical scenario. You’re a retailer who sells barbecue charcoal. The July 4th is approaching and a large spike in sales is predicted for that week.

Time marches on and now you’re at the beginning of the week in which the holiday is going to happen. For a large swath of the country, a large storm front is passing through and there’s no way in hell that people will be out barbecuing in their usual numbers.

Remember, the holiday is only a few days away now. Chances are that the stores have already received (or have en route) a large amount of charcoal based on the forecast that was in force when outbound shipments were being committed to the stores.

So, we’re already within the week of the forecasted event and most (if not all) of the product has already been shipped to support a sales forecast that is way too high. Nothing can be done at this point to change that outcome.

So changing the forecast to reflect the expected downturn in sales is basically pointless, right?

Au contraire.

When the entire supply chain is linked to the sales forecast at the store shelf, then the purpose of the forecast goes far beyond just replenishing the store.

The store sales forecast drives the store’s replenishment needs and the store replenishment needs drive the DC’s replenishment needs, and so on. All of this happens on a continuum that really has nothing to do with what’s already been committed and what hasn’t.

If your sales forecast for charcoal in the affected stores is 5,000 units over the next 5 days, but you know with a pretty high degree of certainty that you will only sell about 2,000 because of the weather, then why would you delay the process of realigning the entire supply chain to this new reality by several days just because you can’t affect the immediate outcome in the stores right now?

The point here is that while the supply chain is constrained, the sales forecast that drives it is not. It may not be possible for a forecast update to change orders that are already en route, but it is always possible to change the next planned order based on the new reality. In that way, you already have a plan in place that is starting to get you out of trouble before the impact of the problem has even fully materialized. In other words, bad news early is better than bad news late.

If you have information that you think will materially impact sales, then the only time it’s too late to update the forecast is after it’s already happened.

The Point of No Return

 

Events in the past may be roughly divided into those which probably never happened and those which do not matter. – William Ralph Inge (1860-1954)

best-archery-return-policy

Tedious. Banal. Tiresome.

These are all worthy adjectives to describe this topic.

So why am I even discussing it?

Because, for some reason I’m unable to explain, the question of how to deal with saleable merchandise returns in the sales forecasting process often seems to take on the same gravity as a discussion of Roe v. Wade or the existence of intelligent extraterrestrial life.

Point of sale data, imperfect as it is, is really the only information we have to build an historical proxy of customer demand. However, the POS data contains both sales and merchandise returns, so the existential question becomes: Do we build our history using gross sales or net sales?

The main argument on the ‘gross sales’ side of the debate is that a return is an unpredictable inventory event, not a true indicator of ‘negative demand’.

On the ‘net sales’ side, the main argument is that constructing a forecast using gross sales data overstates demand and will ultimately lead to excess inventory.

So which is correct?

Gross sales and here’s why: Demand has two dimensions – quantity and time.

Once a day has gone into the past, whatever happened, happened. Although most retailers have transaction ID numbers on receipts that allow for returns to be associated with the original purchase, we must assume that the customer intended to keep the item on the day it was purchased.

The fact that there was negative demand a few days (or weeks) later doesn’t change the fact that there was positive demand on the day of the original purchase.

Whenever I’m at a client who starts thinking about this too hard, I like to use the following example:

Suppose that you know with 100% certainty that you will sell 10 units of Product X on a particular day. Further suppose that you know with 100% certainty that 4 units of Product X will be returned in a saleable state on that same day.

You don’t know exactly when the sales will happen throughout the day, nor do you know exactly when the returns will happen. At the beginning of that day, what is the minimum number of units of Product X you would want to have on the shelf?

If your answer is 10 units, then that means you want to plan with gross sales.

If your answer is less than 10 units, then that means you’re not very serious about customer service.

 

Ipso Facto

 

If you don’t have the best of everything, make the best of everything you have. – Erk Russell

fullshelf

Store in stock.

It’s one of the most critical measures of supply chain health in retail for which there is data readily available.

Unfortunately, your customers don’t care.

Think about it – the store in stock percentage represents the number of times the system inventory record for an item/store is above some minimum quantity (could be zero, could be the shelf facings, take your pick).

What customers truly care about is on shelf availability. In other words, when they’re standing there in the aisle, is the product present on the shelf for them to buy it?

What if the system inventory record for an item says there is 5 on hand, but the store is physically out of stock?

What if the system record matches what’s physically in the four walls of the store, but the product is stuck in the back room (or some other customer inaccessible location)?

What if the system record matches the physical quantity in the store and the product is displayed in multiple locations on the sales floor, half of which are empty?

All of those scenarios represent in-stock successes, but on shelf availability failures.

Inevitably, item level RFID tagging is going to be as ubiquitous as item level barcoding is today. Problem is that nobody’s really talking about it anymore, so it’s going a lot slower than we would like. Even when it does come to pass, there will be significant capital investment required at store level to get to the point where stock can be precisely counted and located in real time.

At some point, it will become possible to truly measure on-shelf availability – but it’s going to take years.

Do we really want to wait that long?

If the physical count in the store more closely matches the system record and if the supply chain (including the back of the store) is aligned to flow product directly to the shelf as quickly as possible, then in stock will more closely resemble on shelf availability.

The good news is that there are things retailers can be doing today to make this happen before ‘self-counting shelves’ are a reality.

Make On Hand Accuracy a Store KPI

Management of the on hand balance at the store is often viewed as a necessary evil and it can seem overwhelming. It’s common practice for retailers to count store stock annually and pat themselves on the back for achieving a low shrink percentage measured in dollars.

The problem is that this measure is for accountants, not customers. To the customer, the physical quantity of each item in each store on each day is what’s important. Practices that degrade the accuracy of item level counts should be reviewed and corrected, such as:

  • - Scanning items under a ‘dummy’ product number if the bar code is missing from the tag.
  • - Blind receiving shipments from the DC to get product into the store faster (unless the DCs are consistently demonstrating very high levels of picking accuracy).
  • - ‘Pencil whipping’ the on hand balance, rather than thoroughly investigating and searching when the system record is significantly different from what’s immediately visible.

Only by instituting an on hand accuracy measurement program (and using the results to identify and fix flawed processes) can you have confidence that store on hand system records match what’s actually in the store.

Eliminate the Back Room

I’m not suggesting something as drastic as knocking down walls, but the back room should only exist to hold product that doesn’t physically fit on the shelf at the time of receipt. An easy way to eliminate the back room is to make changes in the supply chain that support that goal:

  • - Minimum shipment quantities from the DC should be aligned to the planogram of the smallest store that it services. If the shelf in the smallest store only holds 6 units of an item, then you’re guaranteeing backroom stock if the DC ships in cases of 12. Maybe the DC should be shipping that item in onesies. Will that increase DC handling costs? Probably. But just think of how much labour is being consumed across dozens (hundreds? thousands?) of stores each day rummaging through the back room to find product to keep the shelves full.
  • - Ship more frequently to the store, thereby reducing the shipment quantities (assuming ship packs have been ‘right sized’). See my argument above about considering the cost of store labour by not providing them with shelf-ready shipments.
  • - Appropriately staff the stores such that a truck can be received and the product put onto the sales floor within 2 shifts. That way, there should never be any question as to where the product is in the store – if it’s in the on hand, it’s out on the floor.

The Supply Chain, Merchandising and Distribution home office operations have to do their part here. They have all of the data they need to set up the stores for success in this regard – they just need to be co-ordinated.

Institute Plain Old Good Shopkeeping

In a retail store, there are generally two ways of doing things – the easy way or the right way. As with most things, taking the ‘easy’ shortcut now tends to make your life more difficult down the road, while expending a little extra effort to do what we know is right pays handsome future dividends:

  • - Don’t just jam product wherever there’s available overhead space to get it off your checklist. At least try to find a spot near the product’s home. If there isn’t a spot, then make a spot. Much better to reorganize when the opportunity presents itself, rather than going on a scavenger hunt when a customer is tapping her foot waiting for you to find the product.
  • - When using backroom storage is unavoidable, keep it organized. There’s nothing wrong with using masking tape and black markers as a stock locator system if you don’t have anything more sophisticated at your disposal.
  • - Walk the aisles at least once a day. When product has been put in the wrong spot, it will stick out like a sore thumb to an experienced retail associate. Put it back in the home (or at least collect it into a central area where the restocking crew can deal with it when their shift begins).

I suppose you could wait until self-counting shelves come along instead, but guess what? You’ll still need to do everything described above to have the physical product properly presented to the consumer anyhow.

Why not start working on it now?

Facts and Principles

The truth is more important than the facts. – Frank Lloyd Wright (1869-1959)

facts_truth

‘Our decision making needs to be fact based!’

Not many people would argue with that statement. But I will.

While I wouldn’t recommend making decisions devoid of all fact, we need to be careful not to assume that facts, figures and analysis are the only requirements to make good decisions. More importantly, we must never use facts as a cop-out to allow ourselves to make decisions that we know are bad. As obvious as this sounds, doing the wrong thing for the sake of political expediency and ‘keeping the peace’ happens all too frequently in business today.

As a case in point, many economic studies have used facts and figures to argue that a major catalyst to economic growth in the United States in the 1800s was the widespread use of slave labour in agriculture. Some have even gone so far to suggest that America would not be the economic superpower it is today without the slave trade.

In a presidential election year, there is much hand wringing about the state of the U.S. economy and there has never been an election in which this hasn’t been a key voting issue. So here’s my question: If ‘the facts’ show that slave labour was historically a key contributor to economic growth, why isn’t anyone suggesting a return to slavery as part of their platform?

The first problem is that facts are rarely, if ever, complete. The second problem is that humans have a tendency to dismiss facts that don’t support their preconceptions.

The fact is (no pun intended) that the really big and important decisions can often be made on principle (as in the slavery example) without having to bother doing a full blown cost benefit analysis to tell you the answer.

Data analysis is great, but it must be used to support and measure decisions made on principle, not to make the decisions themselves. As an example, we are often lambasted for our long standing criticism of pre-distributed cross dock as a retail distribution channel. After all, it reduces picking volume and frees up pick slots in the DC, decreases ‘touches’ in the supply chain and takes advantage of the existing outbound network to get product to the stores. What could be wrong with that?

While those are certainly facts about cross-dock, so are these:

  • It shifts the burden of picking store orders from a facility that was designed for that purpose (the retail DC) to a facility that was not (the supplier’s DC), lessening efficiency and increasing cost.
  • It requires stores to lock in orders further in advance, resulting in decreased agility when demand changes and higher inventories in the stores.
  • It reduces transport cube utilization, as pallets must be built with only the handful of products that are shipped by the supplier, not the thousands of products that are shipped by the retail DC.

So how do we use these conflicting facts (along with dozens of others that I didn’t mention) to determine whether or not cross-docking is a wise distribution strategy?

You don’t.

Retail is about customer service. Customers can walk into any store at any time to get any product. Their expectation is that the product they want will be there on the shelf when they show up to get it.

Postponement (i.e. committing to decisions at the last possible moment) is a timeless supply chain principle that maximizes service while minimizing costs.

By its nature, the cross-dock channel increases commit times at the point where the customer is demanding the product without notice and builds inventory at the point in the supply chain where it is fully costed and can’t easily be redirected.

That’s not to say that there is never a scenario whereby cross-docking doesn’t make sense, but violation of a core supply chain principle should at least give you pause before pursuing it in a big way.

No facts required.

Measuring Forecast Performance

Never compare your inside with somebody else’s outside – Hugh Macleod

I’m aware that this topic has been covered ad nauseum, but first a brief word on the subject of benchmarking your forecast accuracy against competitors or industry peers:

Don’t.

Does any company in the world have the exact same product mix that you do? The same market presence? The same merchandising and promotional strategies?

If your answer to all three of the above questions is ‘yes’, then you have a lot more to worry about than your forecast accuracy.

For the rest of you, you’re probably wondering to yourself: “How do I know if we’re doing a good job of forecasting?”

Should you measure MAPE? MAD/Mean? Weighted MAPE? Symmetric MAPE? Comparison to a naïve method? Should you be using different methods depending on volume?

Yes! Wait, no! Okay, maybe…

The problem here is that if you’re looking for some arithmetic equation to definitively tell you whether or not your forecasting process is working, you’re tilting at windmills.

It’s easy to measure on time performance: Either the shipment arrived on time or it didn’t. In cases where it didn’t, you can pinpoint where the failure occurred.

It’s easy to measure inventory record accuracy: Either the physical count matches the computer record or it doesn’t. In cases where it doesn’t, the number of variables that can contribute to the error is limited.

In both of the above cases (and most other supply chain performance metrics), near-perfection is an achievable goal if you have the resources and motivation to attack the underlying problems. You can always rank your performance in terms of ‘closeness to 100%’.

Demand forecast accuracy is an entirely different animal. Demand is a function of human behaviour (which is often, but not always rational), weather, the actions of your competitors and completely unforeseen events whose impact on demand only makes sense through hindsight.

So is measuring forecast accuracy pointless?

Of course not, so long as you acknowledge that the goal is continuous improvement, not ‘closeness to 100%’ or ‘at least as good as our competitors’. And, for God’s sake, don’t rank and reward (or punish) your demand planners based solely on how accurate their forecasts are!

Always remember that a forecast is the result of a process and that people’s performance and accountability should be measured on things that they can directly control.

Also, reasonableness is what you’re ultimately striving for, not some arbitrary accuracy measurement. As a case in point, item/store level demand can be extremely low for the majority of items in any retail enterprise. If a forecast is 1 unit for a week and you sell 3, that’s a 67% error rate – but was it really a bad forecast?

A much better way to think of forecast performance is in terms of tolerance. For products that sell 10-20 units per year at a store, a MAPE of 70% might be quite tolerable. But for items that sell 100-200 units per week a MAPE of 30% might be unacceptable.

Start by just setting a sliding scale based on volume, using whatever level of error you’re currently achieving for each volume level as a benchmark ‘tolerance’. It doesn’t matter so much where you set the tolerances – it only matters that the tolerances you set are grounded in reasonableness.

Your overall forecast performance is a simple ratio: Number of Forecasts Outside Tolerance / Number of Forecasts Produced * 100%.

Whenever your error rate exceeds tolerance (for that item’s volume level), you need to figure out what caused the error to be abnormally high and, more importantly, if any change to the process could have prevented that error from occurring.

Perhaps your promotional forecasts are always biased to the high side. Does everyone involved in the process understand that the goal is to rationally predict the demand, not provide an aspirational target?

Perhaps demand at a particular store is skyrocketing for a group of items because a nearby competitor closed up shop. Do you have a process whereby the people in the field can communicate this information to the demand planning group?

Perhaps sales of a seasonal line is in the doldrums because spring is breaking late in a large swath of the country. Have your seasonal demand planners been watching the Weather Channel?

Not every out of tolerance forecast result has an explanation. And not every out of tolerance forecast with an explanation has a remedy.

But some do.

Working your errors in this fashion is where demand insight comes from. Over time, your forecasts out of tolerance will drop and your understanding of demand drivers will increase. Then you can tighten the tolerances a little and start the cycle again.

Flowcasting the…

What’s in a name? That which we call a rose by any other name would smell as sweet. – William Shakespeare, Romeo and Juliet

It’s hard to believe that nearly 10 years have gone by since Flowcasting the Retail Supply Chain was first published. Mike, Andre and I had the manuscript nearly complete before we turned our attention to figuring out the title. I figured it would end up being something boring like ‘Retail Resource Planning’.

Then, Andre got us both on a conference call to tell us that he came up with a title for the book: ‘Flowcasting’. For reasons I can no longer remember (or maybe perhaps because it wasn’t my idea), I immediately disliked it. We went back and forth on it for awhile and as time went on, the name ‘Flowcasting’ grew on me.

Then I came up with the idea that we should be more descriptive about the process. It’s a new concept for managing the retail supply chain, right? So, we should call the book ‘Flowcasting the Retail Supply Chain’!

Now I wish I had just listened to Andre and left well enough alone.

Over the years, we’ve written and talked at length about the types of major changes a process like Flowcasting brings about in the supply chain areas for retailers and their trading partners:

  • Managing all supply chain resources (inventory, labour, capacities and spend) using a single set of numbers based on a forecast of consumer demand at the shelf
  • Collapsing lead times between retailers and their trading partners by unlocking the power of the Supplier Schedule
  • Improving supply chain operational performance by providing 52 weeks of future visibility from the store shelf back to the factory and giving manufacturers the opportunity to shift their operations from ‘make to stock’ to ‘make to order’
  • And so on and so forth

To be sure, the supply chain operational and planning changes are significant – but that’s just the beginning. To make Flowcasting successful, the mindset changes in other areas of the retail organization can be just as revolutionary.

The Merchandising Organization

Historically, the buyers in a retail organization were (and in most cases still are) just that: ‘people who buy stuff’. Their primary accountability is growing sales in their categories, and the conventional wisdom is that the best way to increase sales is to have a lot of inventory. If you think you’re going to sell 10,000 units on an ad, then you buy 20,000.

Flowcasting turns this notion on its head. Because it is always accounting for all inventories in the supply chain and replanning every day based on the sales forecast, ‘Buyers’ must learn to become ‘Category Managers’ who focus on their key accountability: generating demand and providing input to the sales forecast. The change management implications of this change cannot be overstated, as this can be viewed as taking away their control of a key input to their overall success.

Similarly, buyers are accountable for maximizing gross margin on their lines. When negotiating case packs and ordering minimums, they may be inclined to choose the option that gets them the lowest overall cost per unit. But this can be very costly to the business overall if these constraints make it impossible to flow product to the store shelf, particularly for lower sales volume stores. High gross margin doesn’t necessarily equate to high profitability for the business as a whole if folks in the merchandising organization haven’t been given the tools and accountability to think holistically about the supply chain.

Store Operations

Two decades ago, the retail supply chain was distribution centres and trucks. A few years ago, the thinking began to evolve to include the retail store as part of the supply chain. Now, we are finally starting to think of the supply chain as linking the factory to the customer’s hands.

Most retailers measure store ‘in stock’, but what’s meaningful to the customer is on shelf availability (meaning that if a store has 10 units in inventory, but it’s stuck in the back room somewhere, it’s an ‘in stock success’, but a failure to the customer).

One of the biggest questions retailers face is ‘How much autonomy do we give to the store?’ While it’s true that each individual store is closer to the market they serve than the folks at home office, does this mean that giving every store manager a stock ordering screen to use at their discretion will automatically increase customer service? In my experience, the only things it increases with regularity is inventory and confusion.

Even though Flowcasting would generally be a centrally managed process for most retailers, it’s driven from individual store level sales patterns and constraints that may not be known to a store manager. The entire process works much better when people are accountable and measured only for the things that are within their control. For a retail store, that means two things:

  • Receive the product quickly and get it straight to the shelf
  • Keep the inventory records accurate

If stores are focusing their energy in these areas, it doesn’t guarantee perfect on shelf availability for the customer, but it makes it easier to trace back where the process is failing and make corrections when there are fewer fingers in the pie.

Human Resources

Flowcasting isn’t just a different calculation for coming up with an order recommendation. It’s a fundamentally different way of operating a retail business. As such, it requires a different skill set and mindset to manage it.

While it might be tempting to fill an open replenishment position with someone with replenishment experience at another organization, you could be trying to fit a square peg in a round hole if her prior experience is with traditional reorder point or push methods. In fact, you may have to invest more time with ‘untraining’ than you do with training.

To be successful with Flowcasting, a person needs to be organized and possess decent problem solving skills. Other than that, no specific ‘experience’ is required – in point of fact, it could actually prove to be a detriment.

First

Normally these newsletters focus on wisdom we’ve gleaned with respect to supply chain planning, specifically as it relates to Flowcasting.  This month’s is going to be a little different.  It’s time for a celebration and to recognize a first in retail!

We’d like to congratulate our client, Princess Auto Limited (PAL), on their successful rollout and implementation of the Flowcasting process and philosophy.

For those of you who don’t know PAL, they are a growing Canadian hard goods retailer with stores from coast to coast, supported by a multi-tier distribution network that flows product from all over the world into their consumer’s hands.

I remember getting a call from one of the Vice Presidents, Tammy and she said, “Hey I read the Flowcasting book you guys wrote and I really like the idea and concept”.  So, I replied, “Great Tammy, I really like it too!”.  And so it began.

Just recently they completed the initial implementation and are completely managing the flow of product using the Flowcasting approach and solution.

As originators of the concept, co-authors of the book and retail focused implementers, we could not be more proud of their efforts and achievements.  Here’s what they have accomplished:

They are managing the entire supply chain from a forecast of consumer demand, by item, by store (and web store).  The forecasting process and solution is elegant, simple and intuitive and is not fraught with unnecessary complication.

The consumer demand forecast accounts for a number of different selling situations and demand patterns, including:

  1. Regular selling products that sell all year round
  2. Seasonal and highly seasonal products
  3. Products on promotions
  4. Slow sellers and very slow sellers
  5. One time buy products that are purchased and sold during a very short time period or when the inventory is available

As an organization when it comes to demand planning they have shifted their thinking and everyone, including the Demand Planners and the Merchandisers, is speaking the same language – “what we think we will sell”.

They use the consumer demand forecast to calculate a series of integrated, time-phased plans (for a 52-week planning horizon) from the store to the supplier factory adhering, like heroes, to the mantra “never forecast what you can calculate”.

The projections of product purchases are shared with their merchandise vendors in the form of a supplier schedule so the vendors have visibility to see future requirements and plan accordingly.  The vendors are beginning to use these projections to plan raw materials and production and are adhering to the concept of “silence is approval” – that is, if they see something in their schedule that looks odd, they contact their respective Analyst – otherwise, they are expected to supply.

Product transfers (from Stores to Distribution Centres) and purchase orders (from Vendors to Distribution Centres) are cut, automatically, at the agreed upon lead time between any two locations.  Since all partners in the supply chain have visibility they are working to a single lead time between two nodes in the supply chain – even promotional requirements are automatically converted to an order at the same lead time as regular demand.  In fact, their thinking has evolved to the point where they understand that, in retail, there really is no difference between a “regular” order and a “promotional order”.

The unit projections at all levels are automatically translated to different languages of the business:

  1. In dollars for finance to aid in budgeting and gaining control of the business
  2. In cube and weight for distribution, transportation and retail operations to provide volume projections and automatically convert the projections to capacity requirements

In terms of planning they are using the Flowcasting process and solution to greatly simplify and improve a number of common retail planning scenarios.  These include:

  1. Product introductions
  2. Product discontinuations that phase out products, store by store, based on available inventory and the store specific consumer demand forecast
  3. New store openings to predict future dated sales and replenishment by product
  4. Promotions, including national and regional events
  5. Seasonal planning to ensure that residual seasonal carryover inventory is minimized
  6. Distribution Centre openings whereby future store requirements are mapped to the new DC in order to properly depict the ramp up demand on the new DC, while simultaneously showing the draw down demand on the older DC

To summarize:

They are managing their business to a single set of numbers and have created a dynamic model of the business – driven by the consumer!

I think you’d agree that’s pretty impressive and, in my opinion, they deserve a round of applause and perhaps even a standing ovation for their accomplishments.

Folks, this is a first in retail supply chain planning and a first for Flowcasting.  A retailer is managing their entire business using Flowcasting and is already beginning to see improvements in on-shelf availability, inventory performance and operational performance – not to mention starting to gain control of the business by connecting the business plan with the day-to-day operational plans.

To the folks at PAL and to the small, dedicated Flowcasting Team that we’ve worked with, I have only this to say….

Congratulations, and well done!!

Overly Sophistimicated

There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards or crystal balls. Collectively, these are known as ‘nutty methods’. Or you can put well researched facts into sophisticated computer models, more commonly known as ‘a complete waste of time.’ – Scott Adams

If you have your driver’s license, you can get into virtually any automobile in any country in the world and drive it. Not only that, but you can drive any car made between 1908 and today.

You want to make a left turn? Rotate the steering wheel counter-clockwise.
Right turn? Clockwise.
Speed up? Press your foot down on the accelerator pedal.
Slow down? Remove your foot from the accelerator pedal.
Come to a stop? Press your foot down on the brake pedal.

Think all of the advances in automotive technology – from the Ford Model T in 1908 to the Tesla Model S in 2016… Over 100 years and countless technological leaps, yet the ‘user interface’ has remained the same (and universally applied) for all this time.

This is what makes the skill of driving easy to learn and transferable from one car to the next. And all of the complexities of road design, elevation and traffic can be solved by making the decisions on the part of the driver in any scenario very simple: speed up, slow down, stop or turn. Heck, even the lunar rover used the same user interface to deal with extraterrestrial terrain!

Not only that, but because the interface is simple and control on the part of the driver is absolute, there is built in accountability for the result. If the car is travelling faster than the speed limit, it’s because the driver made it so, manufacturing defects (most often caused by ‘over sophistimication’) notwithstanding.

While supply chain forecasting software hasn’t been around since the early 1900s, it’s been around long enough that it doesn’t seem unreasonable to expect some level of uniformity in the user interface by now.

Yet, while a semi-experienced driver can walk up to an Avis counter and be off cruising in a car model that they’ve never driven before within minutes, it would take weeks (if not months) for an experienced forecaster to become proficient in a software tool that they’ve never used before.

The difference, in my opinion, is that the automobile was designed from the start to be used by any person. Advanced degrees in chemistry, physics and engineering are needed to build a car, not operate it.

While no one expects that ‘any person’ can be a professional forecaster, it should not be necessary (nor is it economically feasible) for every person accountable for predicting demand to have a PhD in statistics to understand how to operate a forecasting system. The less understood the methods are for calculating forecasts, the easier it is for people on the front line of the process to avail themselves of accountability for the results. Police hand out speeding tickets to drivers, not passengers.

Obviously, not all cars are alike. They compete on features, gadgets, styling, horsepower and price. But whatever new gizmos car manufacturers dream up, they can’t escape the simple, intuitive user interface that has been in place for over 100 years.

While I’m sure it’s an enriching intellectual exercise to fill pages with clouds of Greek symbols in the quest to develop the most sophisticated forecasting algorithm, wouldn’t it be nice if managing a demand forecast was as easy as driving a car?

Bad Habits, Part 2

Managing on time performance (inbound or outbound) is a struggle for every retailer. Whenever there’s a delivery failure, somebody asks the inevitable question: “How do we prevent this from happening again?”

Sometimes the cause is known to be out of your control – a freak snowstorm leaves trucks stranded or a temporary congestion issue at a facility. For these types of unpredictable reasons, it’s simply not possible to achieve 100% on-time delivery all the time.

But what about cases where a supplying location is chronically at 80%? What if it’s dozens of locations with this problem (which is not uncommon in retail)?

What we’ve seen is that, faced with this problem, many retailers have developed the bad habit of arbitrarily increasing their planning lead times. In many cases, they will even develop processes that will analyze the demonstrated order-to-delivery times on historical orders and transfers, then automatically update the planning systems with increased lead times in an effort to boost on time delivery performance.

What often happens is that on time performance does improve (for a time), which tends to validate the process. Then results start to slip again, triggering another wave of analysis or an increase in the frequency of the automated logic to ‘stay on top of things’.

The problem with this approach is that it assumes two things:

  1. That lead times are something that ‘just happens’ over which nobody has any control.
  2. Where on time delivery performance is failing, it must be because people aren’t being given enough time to do perform their tasks.

The actual order-to-delivery cycle time in a supply chain is the result of processes. These processes must be routine, repeatable and, most importantly, designed to achieve the goal you’re measuring them against.

The routine and repeatable part likely isn’t the issue in most cases. The amount of time it takes to pick an order or drive a fixed distance doesn’t have a lot of variation (especially when lead times are rounded to the nearest day). Once you know what those standard times are, there really shouldn’t be any need to change them very frequently (unless the processes that generate the results change significantly).

Yes, there are rare, infrequent events that will cause lateness, but that should account for an on-time performance measure in the 80s.

More likely than not, the culprit is that the processes are not designed to achieve on time performance. For example, if there is a prioritization scheme in place that schedules picking and shipping based on any other criteria than the due date, the process is not designed for on time performance.

For example, it’s commonplace for retailer DCs to prioritize promotional shipments to stores with a shipping date of next week ahead of regular shipments that are due to be shipped today.

Suppliers may or may not be prioritizing shipments based on the size of the customer or the negotiated price.

In any of those cases the issue is that the ship date is not being respected, so no amount of additional lead time will solve the problem long term. All an increased lead time will do is lengthen the amount of time between the order date and the scheduled ship date, thereby decreasing the ability to adapt and react to changes. Not to mention the increase in safety stock requirements to cover a longer frozen window.

Like anything in the supply chain, the key to solving chronic issues with on time delivery is to find the root cause of the problem and take the necessary steps to address them. To the extent that the processes are within your organization’s control, that can be relatively straightforward (assuming the intestinal fortitude exists to prioritize all shipments based on a due date, promotional or not).

If the issue is with a supplier, there needs to be a common understanding of how their underlying processes work and what conditions are necessary to deliver consistently on time. This goes beyond using a scorecard to try to ‘shame them into submission’.  By adopting Flowcasting and sharing a time-phased schedule of their requirements, retailers can provide very valuable preparatory information to their suppliers that will give them visibility to shipping requirements weeks and months before the purchase order is ever cut.

Don’t fall into the bad habit of setting your lead times based on a data mining exercise. Look at them with a critical eye, identify where chronic failures are occurring and attack the root cause.