Last Mile Delivery: Really Folks?

 

One way to boost our will power and focus is to manage our distractions instead of letting them manage us. – Daniel Goleman

shiny_object

Okay, first a confession out of the gate. The title, quote and image above might lead you to believe that I’m judging last mile delivery (and the broader omni-channel retailing discussion that goes along with it) as a ‘shiny object’ distraction.

I know that’s not entirely true. But I believe it is at least partially true.

To be sure, retail is changing and it’s changing rapidly. Customers want more choices in terms of how they make purchases and how they get those purchases to their homes – and they aren’t super keen on paying a lot more for these choices.

Retailers who put their heads in the sand and don’t actively address these challenges will (and in some cases already do) find themselves in serious peril.

Where is last mile delivery headed? It’s still evolving – but getting into those details is not the point of this discussion. I’m going to stay in my lane. At the risk of oversimplifying things, a sale is a sale and the supply chain planning challenge is to have the product available where the sale will be fulfilled.

The beef I have is that all of the discussion about last mile delivery seems to be making the blanket assumption that retailers have everything aced right up to the last mile.

As if to prove my point, I received an unsolicited email today (God only knows how many supply chain related online publications have my email address at this point) asking for my participation in a survey with the title: “Can we solve the last mile?” The opening two sentences read as follows:

“The last mile is bearing the brunt of the eCommerce boom. Yet, it represents a great source of angst and expense for retailers and last mile providers alike.”

After that is a ‘sneak preview’ of survey topics that focus solely on last mile problems – the implication (likely unintended) is that the challenges in the last mile are completely independent of all the activities that precede them.

Retail out-of-stocks have been a major problem since they started measuring it (8% on average and double that during promotions). The most prevalent cause cited by all of the major studies is inventory management and replenishment practices at store level. Not surprisingly, the lack of attention on solving for these causes means that they haven’t yet magically vanished. Perhaps someday, if we keep wishing really hard…

It’s pretty clear that ‘non Amazon retailers’ will need to make use of their bricks and mortar store network to enable whatever last mile delivery options they intend to pursue. How will they be successful in that regard with such abysmal out-of-stock performance and no idea what the accuracy of their electronic on hand records are (if they even have them at all)?

The day is coming when customers will expect to see store on hand balances on your web page before they submit a ‘click and collect’ order – what happens when the website says you have 3 in stock, but there isn’t any to be found when the customer goes in to collect?

Finally, we can’t lose sight of the fact that the ‘omni’ in ‘omnichannel’ is a latin prefix meaning ‘all’ or ‘every’. One of those ‘every’ channels is customers walking into a store, getting a cart, selecting products and paying for them at the checkout – kickin’ it old school to the tune of 91.5% of total retail sales.

Yes, e-commerce is growing like crazy, but it’s going to be awhile yet before online selling is truly dominant in retail as a whole.

And if (when) that day comes?

Again, I’m not suggesting that working out the last mile won’t be critically important. I’m just saying that retailers still have some work to do in getting basics right (like being in stock and knowing how much is on hand) in order to make it all work.

Virtual Reality for the Retail Supply Chain

SimulatorFA18

Whenever we discuss Flowcasting, we always describe it as ‘a valid simulation of reality inside a system’. This term originated with our longtime colleague Darryl Landvater and it is the most concise and accurate way to describe what Flowcasting really is that we’ve heard.

In fact, we use that term so much that I think we sometimes assume its meaning is self evident.

It’s not.

Over the last 11 years since Flowcasting the Retail Supply Chain was first published, I’ve noticed that, more and more, the terms ‘Flowcasting’ and ‘valid simulation of reality’ have been treated somewhat like ink blots, with some folks (intentionally or otherwise) using them to mean whatever they want them to mean.

To set the record straight, a true ‘valid simulation of reality’ for the retail supply chain has some very specific characteristics, all of which must be present. To the extent that they are not, the value of the plan suffers – as do the results.

Before diving into the nitty gritty details, consider this: Virtually all retailers have a data warehouse that captures daily sales summaries for every product in every location, all upstream product movements, every on hand balance and certain attributes of every product and every location. This data is usually archived over several years and the elemental level of information is kept intact so that rollups, reports and analysis of that data can be trusted and flexibly done.

Think of a valid simulation of reality for the retail supply chain as a data warehouse – with a complete set of the exact same data elements at the same granular level of detail – except that all of the dates are in the future instead of the past.

However, it must also be said that ‘valid’ does not mean ‘perfect’. Unlike the historical data warehouse that remains fixed after each day goes into the books, the future simulation can and will change over time based on what happened yesterday and new assumptions about the future. Updating the simulation daily at all levels is the key to ensuring it remains valid.

Now let’s get into some of the specifics. A valid simulation of reality has 4 dimensions:

  • Information about the physical world as of this moment
  • Forecasts of expected demand over the next 52 weeks for each individual product at each individual point of consumption (could be a retail store or a virtual store)
  • A simulation of future product movements driven by the forecasts and future planned changes to the physical world
  • Rollups of the elemental data to support aggregate planning in the future

While it may seem that meeting all of these requirements is onerous, it is actually quite simple compared to trying to do things several different ways to account for variations in how products sell or are replenished.

Current information about the physical world includes things like:

  • Master information about products (e.g. cube, weight, pricing, case packs, introduction and discontinuation dates) and locations (stores, DCs, supplier ship points)
  • Relatively accurate on hand balances
  • Planogram details such as store assortment, facings and depth
  • Source to destination relationships with lead-times that are representative of physical activity and travel times

This information is ‘table stakes’ for getting to a valid simulation of the future and is readily available for most retailers. High levels of accuracy for these items (even store on hands) is achievable – so long as the processes that create this information have some discipline – because they are directly observable in the here and now.

Forecasts of demand over the next 52 weeks must:

  • Include every item at every selling point
  • Model demand realistically for slow selling items (i.e. integer values as opposed to small decimals that will model an inventory ‘sawtooth’ that won’t actually happen)
  • Include all known positive and negative future influences for each individual item at each individual selling location (e.g. promotions, assortment changes, trends)
  • Allow for ‘uncertainty on the high side’ for promotions to be modeled independently from the true sales expectation so as not to bias the forecast, especially for promotions
  • Account for periods where inventory is planned to be unavailable at store level (e.g. sales forecast for a discontinued item should continue while the item is in stock and drop to zero when it’s projected to run out in the future)

The rule here is simple: if the item at a location is selling (no matter how slowly or for how long) it must have a sales forecast and that sales forecast must be an unbiased and reasonable representation of what future sales will look like.

A simulation of future product movements driven by the forecasts and future planned changes to the physical world means that:

  • Replenishment and ordering constraints are respected in the plan (e.g. rounding up to case packs if that’s how product ships, rounding at lane level if truckload minimums across all products on a lane are required)
  • Activity calendars are respected (e.g. an arrival of stock is not scheduled when a location is not open for receiving, a shipment is not scheduled during known future shutdown)
  • Carryover targets are respected for seasonal items (e.g. before the season even begins, the planning logic suppresses shipments at the end of the season so as to intentionally run out of stock at the stores and DCs)
  • Future changes to stocking requirements (e.g. changing the number of facings for an item or adding/subtracting it from the store assortment) are known in advance and the effect is visible in the plan on the future day when it will take effect
  • Future changes to network and sourcing relationships (e.g. changing a group of stores to be served by a different DC starting 2 months from now) are known in advance and the effect is visible in the plan on the future day when it will take effect
  • Future price changes (whether temporary or permanent) are known in advance and the effect is visible in the plan on the future day when it will take effect
  • Pre-distributions of promotional stock are scheduled in advance to allow display setup time ahead of the sale
  • Except in rare cases, the creation and release of orders or stock transfers at any location is a fully automated, administrative ‘non event’ that requires no human intervention

Here it can be tempting to take shortcuts that seem ‘easier':

  • ‘We don’t bother forecasting or planning slow moving items at store level. We just wait for a reorder point to trigger.’
  • ‘For items we only buy once from a vendor, we just manually buy it into the DC and push it all out to the stores.’
  • ‘We know our inventory isn’t very accurate for some items at store level, so we just get the store to order those items manually based on a visual review of available stock.’

In order to have a valid simulation of reality that supports higher levels of planning beyond immediate replenishment (see next section below), you need a system and process that can model these things in a way that is representative of what is actually going to happen.

If an item/location is selling/sellable, then it must have a forecast for those sales.

There is actually no such thing as ‘push’ in retail (unless you are able to ‘push’ product into a customer’s cart against their will and get them to pay for it).

Rollups of the elemental data to support aggregate planning in the future means:

  • You can do proper capacity planning with a complete view of the future because a common process is being used at the elemental level, cube/weight data is accurate and so-called shortcuts are not being taken at the elemental level
  • S&OP is possible because the elemental plans are complete, have future pricing changes applied – instead of looking in the mirror with ‘budget vs actual’, senior leaders and decision makers can look through the windshield and compare ‘budget vs operational plan

While all of these elements that define ‘valid simulation of reality’ may seem intuitive and reasonable, it doesn’t stop some people from saying things like:

  • ‘A lot of items, especially slow movers, can’t be forecasted, so the whole idea kinda falls apart right there.’
  • ‘That’s a great theory, but it’s actually not possible to use a pull-based system for every item.’
  • ‘Just because of sheer volume, it’s impossible to manage every product at every location in this way.’

Again, as mentioned previously, a single process framework that can be used for all possible scenarios is actually much simpler to implement and maintain over the long run.

Plus, well… it’s already been done, which kinda deflates the whole ‘it’s impossible’ argument.

We Can All Agree

 

We rarely think people have good sense unless they agree with us. – Francois de la Rochefoucauld (1613-1680)

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My family has a history of heart problems.

Although my blood pressure and cholesterol are both fine, I’m 47 years old, carrying 15-20 extra pounds and I don’t get enough exercise, which compounds that risk.

Family History + Being Middle Aged + Being Overweight + Not Enough Cardio = Increased Risk of Heart Problems

It’s hardly a mystery. Everybody knows this. I agree.

I can do nothing about my family history or my age, but I’ve been about the same weight for the last several years and have not meaningfully or sustainably increased the amount of daily exercise I get on a daily basis.

Ask any smoker if they are aware of all of the various health risks from smoking. They too will agree that smoking is bad. But they still do it.

Clearly, there isn’t a binary choice (i.e. agree or disagree), rather different ‘levels’ of agreement:

  • I agree with what you’re saying.
  • I agree that something needs to change.
  • I agree to change my behaviour.

In business in general (and supply chain in particular), significant improvement in results can only be achieved with process-driven changes to people’s behaviour.

We can all agree that the quality of a retailer’s customer service is directly tied to the accuracy of their store-item level inventory records – especially in an omnichannel world where a customer can demand product from a website and expect to pick it up in their neighbourhood store a couple hours later. It’s not a stretch to further agree that processes, procedures and measurement systems need to be in place to improve and maintain store level on hand accuracy.

And yet many retailers (40% of grocery stores according to a recent study) don’t even use a system on hand balance and those that do are not attacking their accuracy problems.

We can all agree that retail supply chains should be consumer driven to be efficient and profitable. And yet most retailers are using the same ‘old school’ processes for promotions, new product introductions and seasonal sales – ‘buy a ton, push it out to the stores and pray that it sells’.

While ‘agreement in principle’ is certainly necessary, it is clearly far from sufficient. So what is the secret ingredient?

I’ve seen it many times throughout my career in retail. I visit one store and the aisles are uncluttered, the shelves are faced out beautifully and the back room is organized and tidy. Then I visit another store with the same retailer and it looks like it was recently hit by a cyclone – even though both stores have the same systems, processes and training manuals.

The difference is that you have to care.

Don’t get me wrong. I’m not saying that the store manager with the messy store has no passion. I’m just saying that he doesn’t have passion for retailing.

It’s the same reason I’m a supply chain consultant and not a fitness instructor (at least for now). I agree in principle that I need to exercise and lose weight, but I care deeply about order, organization and process discipline in the retail supply chain.

So where does this passion come from and how can it be cultivated and spread throughout an organization?

God, I really wish I knew. I believe that everyone is born with passion, but not everybody is in a job they’re passionate about.

That said, I know that passion can be infectious enough that a very small group of uber-passionate people can change organizations – not necessarily by making everyone as passionate as they are, but by generating just enough force to overcome the organizational inertia.

And once the boulder starts rolling down the hillside, we can all agree that it’s very difficult to stop.

The E-Commerce Secret Weapon

All the secrets of the world worth knowing are hiding in plain sight. – Robin Sloan

TopSecret

The end is nigh! If you still have physical stores with inventory, staff and cash registers, you’re a dinosaur and Amazon is coming to kill you! The future is online!

Okay, the rhetoric hasn’t been quite that sensational, but it wasn’t so long ago that ‘experts’ were on the verge of predicting the demise of retail as we know it.

As people (eventually) came to their senses on this, a new reality began to emerge, hidden in plain sight. It turns out that the decades of investments retailers made in their physical store footprint may not have been a complete waste of money after all. In fact, it’s actually a key competitive advantage that may result in Amazon playing some ‘catch up’ of their own in the not too distant future.

To be sure, the ‘buy online, delivery to home’ channel pioneered by Amazon represented a significant shift in how people buy goods. If you didn’t mind a bit of a wait and some extra delivery costs, you could shop without ever having to leave the house.

Over time, new products and services were added to build density, reduce shipping costs to customers and decrease delivery times for many in stock items. This could only happen cost effectively by positioning inventory closer to customers… kinda like what ‘NARs’ (‘non Amazon retailers’ – trademark pending) have been doing for decades.

For customers, that means brick and mortar retailers with an online presence can offer far more shopping and delivery options than Amazon (at least for now).

Click and Collect (or Buy Online, Pick Up in Store)

One way to look at click and collect is that it’s ‘not quite as convenient as home delivery’. In reality, click and collect isn’t necessarily a ‘convenience compromise’ in the mind of every customer – many (including yours truly) consider it to be a different (and more cost effective) kind of convenience.

This option allows customers to reserve their stock in advance and have store staff traverse the aisles on their behalf. And when customers get to the store to pick up their online order, they have the additional option to grab a few last minute or forgotten items. Or maybe they just want curbside pickup so they can get the items loaded directly into their trunk without even having to park.

And for customers who truly view click and collect as a convenience compromise vis-a-vis home delivery, Walmart now allows them to trade in some of their convenience for savings by giving them a discount for choosing click and collect over home delivery.

Third Party Personal Shoppers

This is a relatively new phenomenon, but companies like Instacart have been partnering with retailers to take orders online, shop local stores and home deliver to customers, offering cool features like chatting so that the personal shoppers can make real time decisions with the customers for substitutions or to take advantage of in store promotions.

Home Delivery from Stores

Because retailers already have inventory geographically close to customers, they have the ability to take advantage of cheaper modes of transit (i.e. ground vs air) to deliver in a 2 day time window.

But they also have the opportunity to make delivery promises in hours rather than days in their more densely populated markets, through the use of local couriers or even their own store employees.

Will all of these delivery options (plus a few others that haven’t been dreamed up yet) be popular and/or profitable? Click and collect seems like a done deal – time will tell for the others.

The point here is that these options are only available to retailers who have a retail store network in place. Far from being outmoded or passe, the ‘brick and mortar’ store network is becoming a critical linchpin in meeting customers’ online shopping expectations.

You never would have imagined it a few years ago, but the popularity of online retailing has actually served to enhance the importance of the old fashioned brick and mortar retail store rather than to diminish it. And as such, planning the supply chain from the store level back using Flowcasting becomes even more critical to a retailer’s success than ever.

 

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

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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)

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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.