About Jeff Harrop

Jeff Harrop

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

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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.

Princess Auto’s Flowcasting journey featured in Canadian Retailer magazine

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Our client Princess Auto Ltd. is the subject of a feature article in the inaugural Supply Chain issue of Canadian Retailer magazine (published by the Retail Council of Canada). Click here to learn about how they are using the Flowcasting planning process to significantly improve in-stocks and profits while unleashing a new omnichannel fulfilment model. You can also download a PDF copy here.

 

Virtual Reality for the Retail Supply Chain

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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

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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

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“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.