A schedule defends from chaos and whim. – Amy Dillart
Retail Systems Research recently surveyed 133 US retailers of various sizes ($250M+) and across 5 verticals on the subject of tariffs. The authors were trying to understand how retailers view the tariffs in the short and long term and how they are responding to them.
Some of the results were highly predictable – most are feeling the squeeze of trying not to alienate price conscious customers at a time when their cost of goods are increasing because of forces out of their control – but there were also some somewhat shocking surprises. A significant majority believe that in the long term, “the benefits from the current tariff policy will ultimately outweigh the short-term drawbacks to our business”. Those expected benefits, they believe, will flow from increased onshoring of manufacturing in the United States.
But there seems to be some inconsistency (cognitive dissonance?) at play, as the authors note:
“Most retailers excluding those selling fast moving consumer goods believe that robots will perform US-based manufacturing tasks. Yet they also believe that renewed manufacturing in the US will create a significant new job market. It is unclear to us how to reconcile those two opinion sets. It isclear retailers are all feeling quite positive about near-sourcing and ultimately benefits will outweigh the risks.
Again, it’s hard to reconcile these seemingly divergent opinions (remembering that most retailers don’t think they can survive even a year of tariff-driven retailing). It is emblematic of our country as a whole today: torn between the need for more self-sufficiency, yet unsure how to get there from here.”
I guess that’s chaos for you.
It’s a fascinating study and also a glimpse into the future for US consumers on how retailers are planning to respond to the tariffs.
They conclude with 7 recommendations for retailers that could be applied to chaos in any form (pandemics, natural disasters, financial crises, etc.). Six of those 7 recommendations are coded directly into the DNA of Flowcasting and – in our view – should be a “way of life” for retailers even in times of relative “calm” (or maybe the correct term is “normal background chaos” that always exists in retail, even if they’re not talking about it on CNN).
Acknowledge The Chaos
2025 has proven to be anything but normal, as an environment of wildly fluctuating tariffs has completely upended the relative stability retailers – and their supply partners – were expecting at the beginning of the year. The hard truth is that our industry is now trying to plan – what to buy,what to sell, how much demand there will be, how much supply will be needed – in an environment when planning is nearly impossible. Acknowledging this chaos is now the first step on the journey, as all indicators point to this being the “new normal” for the foreseeable future.
In Flowcasting speak, we call this “documenting your assumptions”. When you don’t know exactly what will happen in the future (and who really does?), you need to gain consensus on your best guess and then anchor your plans on that. As time unfolds, the assumptions you made may not hold water (more on that below), but at any point in time, everyone knows the thought process that was driving the plans.
Embrace ‘Sense And Respond’
The plan may be out the window, but that doesn’t mean a new plan shouldn’t take its place. We advise retailers to use the data available and new analytics to sense and respond to changes in market conditions as quickly as they possibly can. Just as it was during the pandemic, this is a time to get creative. Retailers have shown – time and again – that when backed into a corner, they are more than capable of adapting.
Throwing plans out the window and creating a new one to take its place is what Flowcasting is really all about. For every item at every location every day. While retailers writ large have shown that “they are more than capable of adapting”, those with fully a fully connected planning process from the store shelf back to the supplier can adapt far more quickly, accurately and efficiently.
Forecast Continuously And Adjust
Forecasts need to be updated in-season as the selling period unfolds to make short-term adjustments. This capability is empowered by new data (customer, product, competitor, and market) and the analytical tools that can turn those data into insights.
This recommendation goes hand-in-hand with “sense and respond”. With Flowcasting, forecasts are updated daily for every item at every selling location with current sales. As far as “turning those data into insights” goes, the continuously updated demand forecasts drive a fully integrated end-to-end model of the supply chain that updates the product flow plans from the store shelf right back to the supplier, also on a daily basis. What could be better than that?
Recognize That Scenario Planning / Predictive Modeling Is Vital
Few forward-thinkers would deny that the world will become more unpredictable with time. Weather events, social norms, wild swings in the geo-political world – all happen faster and less predictably than at any time in the recent past. Retail Winners view their ability to use predictive modeling techniques as key to helping them to react much more quickly to supply chain disruptions and sudden shifts in demand.
Digital transformation and the technologies it employs makes it possible for retailers to predict all kinds of unforeseeable patterns before they occur – to model future states – and react to them faster once they do. This has moved from being merely a winning behavior to becoming essential to survival.
With a realistic model of the supply chain constructed in a single system, scenario modeling is very quick and easy to do (i.e. “plug in some new assumptions and see what happens”). Not only can different demand scenarios be modeled to determine the impact on sales, costs and service, but you can also game out various supply side strategies that can be deployed in response. Once you have a complete plan that looks like it will work, you can commit the changes and start executing to it across the board. If it starts to look like it’s not going to work, you can change it.
Trust That Pricing Solutions Can Help – Even In Pricing Chaos
Consumers continually cite price as a primary value attribute, and retailers continually tell us price is something they need to get right. New technologies can bring advancements to pricing right now – not in some distant future. RSR’s take is that retailers simply cannot afford to overlook this opportunity. Price optimization solutions have improved to the point that changes as granular as SKU/location can be recommended daily based on dynamic demand and inventory availability.
Whether retailers want to be that granular and fluid is another matter, but (especially with the continued adoption of electronic shelf label, or ESL, technology in stores), retailers could be better able to maintain profitability even in the face of wildly fluctuating cost-of-goods, by taking advantageof what new pricing solutions have to offer.
While this recommendation is not in the direct purview of Flowcasting (pricing is taken as an input and the model does what it’s told), current and planned changes to pricing can be fed in at any level (right down to item/store/day if desired), forecasts can be updated at any level and the impact to the financials can be rolled up from there.
Maintain Flexibility At All Costs
The COVID-19 pandemic taught a great many lessons to retailers. The paramount need-to-know what inventory you have – and where – was just the beginning. How quickly a brand can change its course of action to deliver the right products to where they are needed very quickly is a lessonin the importance of flexibility that cannot be forgotten without creating risks to business survival.
Flowcasting allows not just the modeling of your supply chain as it exists today, but future dated modeling of planned changes to your demand picture, network flows, stocking policies, etc. right down to item/location/day level. And once you’ve modeled it, the system automatically executes the product movements at the right time.
Keep Every Eye Possible On The Supply Chain
The ability to monitor the supply chain in real time and react as necessary, using digital twin technologies to evaluate disruption scenarios and prepare supply chain to minimize financial impact, and the ability to enable “real time inventory management/visibility” are all top-of-mind capabilities for retailers.
Unlike standard computer assisted ordering approaches that only evaluate whether or not it’s time to request more stock on specific ordering days, Flowcasting recalculates demand and supply plans for every item at every location every day. With a complete model of the entire supply chain in a single system, it’s the ultimate digital twin that always has its eyes open.
It would be terrific if we could precisely predict the future, but that’s not possible even in times of relative stability. To the extent that you can come closer to this goal by collecting more information and improving your assumptions, by all means have at it.
But experience has taught us that the real secret to achieving maximum customer service with minimum inventory is not so much accurate forecasting, but continuous replanning.
I am a man of fixed and unbending principles, the first of which is to be flexible at all times. – Everett Mckinley Dirkson
As a retailer, if you can accurately forecast the impact of your promotions – down to item/store level – within a narrow range, then everything will be fine.
Umm, okay, that sounds great but what if – hypothetically speaking – you’re not always able to do that? Then what?
Flowcasting has been fairly accurately described as a demand driven supply chain planning approach, with “demand” in this context referring solely to pure demand from consumers at the shelf.
In order for Flowcasting to work properly, the forecast of future demand at each item/store must be representative of what you expect to sell in every planned week in the future. While the starting point for the forecast can be derived mathematically by detecting patterns in history, it needs to be augmented when you know something about future consumer demand that will be different from the past (sometimes referred to as “demand shaping”). In this case, you know that you’re going to advertise a price drop to your customers in Week 9:
In the example above for an item at a store, we expect to sell 64 units on the promotion. This store needs to maintain 20 units of minimum stock at all times to keep the shelf display looking presentable. Flowcasting logic ensures that the Projected On Hand will never fall below that Minimum Stock in any future week, so as a result, the high expected promotional demand in Week 9 triggers 66 units to arrive at the beginning of that week (all requirements rounded up to shippable packs of 6 units). With a 1 week lead time, that 66 units is seen by the servicing DC as a shipment that will be made to that store in Week 8.
We’re only looking at a single store here and there are a variety of ways to have the promo uplift applied (top-down based on proportional contribution to past total sales, promo sales or baseline forecast, elasticity curves, machine learning based approaches, etc.). The key point here is that demand must be appropriately shaped and represent what you actually expect to sell.
What we have here is a really good plan… If you’re confident in your promotional forecast and if you’re just going to put a promo tag on the home location. For a lot of products (e.g. those that you’ve promoted frequently at the same price with no additional merchandising support), this might be just fine and dandy.
But what if you need product to be in the store earlier or in greater quantities than required just to support expected sales? This could be to support the set up of off-shelf displays or cover for upside forecast risk (particularly if shipments to the store are relatively infrequent and there may not be enough time to do a “mid-course correction” once the promotion starts).
This need is sometimes referred to as “push/pull” or “decoupling” and it can be a real challenge, especially when your supply chain is… well… decoupled.
Flowcasting is uniquely capable of solving this problem quickly, precisely and well in advance so that everyone (store operations personnel, support office planners, buyers and suppliers) can see what’s going to happen.
Because Flowcasting connects the entire supply chain from the consumer to the supplier – it doesn’t support “decoupling” – it completely invalidates it.
For example, suppose that the item we planned earlier will be supported by an off-location display of 30 units in addition to the 20 units required as a minimum on the shelf. Furthermore, the stores need to have sufficient stock a week ahead of time to organize their merchandising teams to set up the display.
In Flowcasting, this is executed as a simple, future dated temporary change to the minimum stock:
Instead of stock arriving just in time to support sales, a large shipment to support the additional display will arrive the prior week, while the additional stock to support the sales uplift will arrive later.
Okay, but what if you’ve never promoted this item at this price point before? The forecast is your best unbiased guess at what’s going to happen, but you would rather have additional stock at the store than risk running out.
Here, we’ve set our minimum stock during the promotion week to ensure that we’re covered if we sell double what we expect.
What if this store can get multiple shipments during the promotional week? You can instead apply a safety stock uplift to the distribution centre plan so that the stock is positioned there to quickly refill stores that are selling through it more quickly, while not overloading the stores that aren’t.
Or you can split the difference by adding some of the additional safety stock to the stores and some to the DCs. Or… you get the idea. All nodes are planned and all nodes are connected, so the effect of changing the shape of the supply plan is precise and the impact on all nodes is transparent.
And by planning in this fashion (shaping the supply plan separately and independently from shaping the demand), there is an additional advantage over pushing stock out via allocation: continuous replanning. The planned shipments and arrivals will be recalculated every day as sales and inventory movements are realized between now and when the promotion starts. And everybody sees how the plan is shifting over time at every location, right back to the supplier.
While there are other methods for shaping the flow plan (temporarily bypassing nodes with planned network flow changes, days of supply/safety time, etc.), simply having separate levers for demand shaping and supply plan shaping is a very effective way to plan not just promotions, but any other scenario where “decoupling and pushing” would be used outside of a Flowcasting context:
Cannibalization and halo effects on items that compete with or complement a promoted item
Planning for the initial pipeline and shelf filling, followed by ongoing replenishment for a new item
Pre-building stock ahead of a seasonal or holiday peak
There is not any memory with less satisfaction than the memory of some temptation we resisted. – James Branch Cabell (1879-1958)
What are my current stock levels? What’s the status of my inbound orders? How were the weekend sales for my products?
A great deal of effort has been spent over the last 2 decades to provide this information to planners and decision makers in near real time. But how useful is it, really?
We like to call this the “salt, sugar and fat” of supply chain planning. It’s extremely satisfying to get answers to these questions in the moment, but the satiation wears off quickly and you find yourself asking the same questions a few days later.
These types of supply chain visibility metrics are merely a glimpse in the rearview mirror. The myriad activities that give rise to a particular inventory level, a change to an order status or a weekend sales result have already happened and have been happening for days, weeks or even months before the question was even asked.
It’s like sitting at the gate and your airline announces a departure delay. You would rather have that information than not, but if that’s all the information you get, you have no control over the outcome. All you know is that you won’t be getting to your destination on time.
Now, suppose that you’re a savvy traveller. Hours before you even leave for the airport, you check the tail number for the inbound flight. Then you check the origin city of that flight and a massive storm is rolling through right around the time it’s supposed to depart, virtually guaranteeing a significant delay.
What are you going to do? Try to get booked on a different airline whose inbound aircraft is not coming from the city that’s about to get pummelled? Extend your hotel stay for another night because there’s no way you’ll be getting out at a reasonable time? Rent a car and just make it a road trip instead? Or just suck it up and leave on your scheduled flight, even though you know you’re going to be significantly delayed.
Any of those options may be acceptable, depending on your needs and constraints (e.g. cost, how urgently you need to get to your destination, whether or not the distance is reasonably driveable). But you only have one option available if you didn’t see the problem coming and only learned about it when you were sitting at the gate.
The point here is that knowing where things currently sit is certainly useful, but nowhere near as useful as being able to anticipate what things will be like in the future. Constantly checking in on up-to-the-minute information about the very recent past may give you a sense of control, but in reality, you’re just sitting in the back seat bingeing on cheeseburgers and donuts.
In a supply chain context, focusing too much on “real time current” information can lead to false conclusions and bad decisions (or non-decisions).
You look at your current DC and store stock levels and everything looks nice and healthy, so you breathe a sigh of relief and move on to the next item. But a promotion is scheduled in 2 weeks that’s going to virtually wipe you out. And your lead time from the supplier is 4 weeks. This is an example of something that is a big problem, but it doesn’t look like a problem in the current data. The cost is lost sales that could have been avoided.
You move on to another item and you see that 30% of your stores are out of stock. So, you panic. You spend the morning trying to figure out how can this be? What happened? And you have a bunch of higher-ups (who are looking at the same “here and now” data that you are) asking the same questions. Meanwhile, an order was just triggered with the supplier that covers the shortfalls and is due to arrive in a few days. Within a week or so, all of the stores will be back in stock. This is an example of something that looks like a big problem in the current data, but really isn’t much of a problem at all. The cost is stress and lost productivity trying to solve a problem that has already been automatically solved.
Subsisting on a diet consisting mainly of salt, sugar and fat is not good for one’s long term health. So, how do you kick the habit?
Like the savvy air traveller, you need to give yourself a window into the future to know all of your options and make the best decisions in advance.
Properly cooked, an end-to-end planning process that is designed to always maintain a valid simulation of reality is a very tasty and nutritious vegetable.
All exact science is dominated by the idea of approximation. – Bertrand Russell (1872-1970)
Okay, so that title might seem a bit “clickbait-y” and even a little dumb without some context, so bear with me here.
Before we get started, this piece is not about optimizing inventory investment by paring back inventory (and risking out of stocks) on long tail items “for the greater good” or any other such nonsense.
If you’re in the retail business in 2025, then you’re competing with Amazon at least to some degree. Those long tail items are probably as important to your long term success as a business than the so-called “bread and butter” fast sellers.
If holding stock on those long tail items gives you heartburn, then you’re better off increasing the selling price to offset the carrying cost than trimming your inventory. Customers will pay a premium if they know you’re the only game in town (or at least the most reliable game in town) to get those hard-to-find items.
Now, back to the topic at hand.
If you read the title of this piece and thought to yourself “What a dumb question!”, I’d wager that you were probably conflating the terms “in-stock” and “on shelf availability”.
What’s the difference? Customers don’t care that you have available stock somewhere within the 4 walls of the store. They want it on the shelf.
While it’s true that stock can’t be on the shelf if it’s not in the store in the first place, there are times when putting additional inventory in the store to boost your in-stock metric can actually harm on shelf availability – and sales.
Consider a simple example for a particular item where the shelf capacity for the item is 10 units. The shelf is completely full and the store is currently holding an additional 20 units in inventory in the back room or some other overflow location.
A couple of weeks go by. The 10 units of shelf stock has sold and the selling location is now empty. For a few days, either nobody notices the hole, the stock has been misplaced or is in a difficult to access overflow location within the store.
What will the replenishment system do? Probably nothing, because there is still plenty of stock in the store to sell. How does the in-stock report look for this item? Fantastic! But you’re losing sales.
In other words, boosting inventory levels in the store will definitely improve your in-stock metric, but if it’s done to excess, inventory can actually harm sales.
One problem we have is that in-stock is relatively easy to reliably measure, while on shelf availability is not. So we are forced to use in-stock as our proxy measure for customer service, when that’s not always the case.
So getting back to the original question: Are you really sure you want high in-stock?
Necessity never made a good bargain. – Benjamin Franklin (1706-1790)
When a retailer seriously embarks on an effort to completely reshape how they plan the flow of goods from supplier to shelf, the discussion inevitably turns to what software they will need to do the job. (And ideally, this isn’t Step 1 of the process).
As the time approaches to evaluate software vendors, someone in company leadership is bound to utter the phrase “We don’t need a Ferrari”. After that, everyone in the room will nod their heads sagely in agreement. You can almost set your watch by it.
The message they’re trying to send is “We don’t need unnecessary sophistication and we don’t want to spend a ridiculous amount of money. We just need to get the basics right.”
I believe the intention is correct. You don’t want the design and implementation team to go off on a wild search for the most sophisticated system they can find – whether or not it’s proven or even necessary. But the advice may not be as useful as you think.
People who are tasked with transforming supply chain planning generally don’t need to be constrained or reined in. They need to be led. By the time you get to this point, you should already have assembled a team with strong convictions and a bias toward pragmatism – they won’t run around chasing shiny objects. Sometimes they will need leadership to be led by them.
Using well-meaning platitudes like “we don’t need a Ferrari” doesn’t really clarify anything and could potentially lead them down the road of picking a simplistic system over a simple one.
The team needs to understand time-tested and proven planning principles, what the true requirements of the organization are (including taking into account future strategic direction) and what results they are expected to deliver.
A system with a simple data structure, easy navigation and limited options that doesn’t adhere to solid planning principles and doesn’t meet the requirements will not deliver results.
Just because some functionality may be more complex or sophisticated than what you have today, that doesn’t make it “too fancy”, unless the mandate is to implement a new system and process that does the same thing you’ve always done (with the same results). Not all sophistication is unnecessary.
Just because people will need to acquire more skills – some of which may be difficult to learn – doesn’t mean that the system or process is “too complicated”.
No, you do not need a Ferrari – because nobody “needs” a Ferrari.
But there is a wide range of options between a Ferrari and a tricycle. Your requirements need to dictate whether you need a Corolla, a minivan or a pickup truck.
Don’t choose a tricycle just because it’s the farthest option away from a Ferrari. A simplistic system that doesn’t meet requirements makes the implementation just as complicated as an over-engineered system that you don’t need.
When we make assumptions, we contribute to the complexity rather than the simplicity of a problem, making it more difficult to solve. – Julie A., M.A. Ross and Judy Corcoran
Planning the retail supply chain not only requires, but is entirely predicated upon making assumptions about the future.
Why?
Because when a customer walks into a store, they already expect the products they want to be on the shelf in sufficient quantity to satisfy their demand – and they give no advance notice of their planned visit. So depending on the cumulative lead times from the ultimate source of supply to the store shelves, the decisions you make regarding product movements today must be made based on what you anticipate (i.e. assume) customers will want – how much, where and when – days, weeks or even months into the future.
So for retailers of any size, that could add up to millions of assumptions that need to be made each day just for the expected consumer demand element alone. And each assumption you make is a risk – if an assumption doesn’t hold, then the decisions you made based on that assumption will cost you in some way.
If the supply chain is disconnected, there are more assumptions to be made. So there are greater risks and higher costs in the form of customer service failures and/or inefficient use of labour and capital.
As an example, it’s not uncommon for a retailer to have different planning and replenishment systems for stores and distribution centres. And those systems usually have a “what do I need to request today?” focus – think min/max or reorder point.
The store replenishment problem is relatively straightforward:
What is the current on hand in the store minus the display minimum (or “cycle stock” for want of a better term)?
What do you anticipate (assume) you will sell between now and the next scheduled delivery day?
If what you expect to sell exceeds the cycle stock, request the difference, rounded to the nearest ship pack
In this case, the only assumption you’re making is the expected sales, with a few “sub-assumptions” with regard to trend, seasonality, promotional activity, etc. going into that.
(NOTE: You are also assuming that your store on hand balance is accurate, which is a whole other lengthy discussion in and of itself.)
Okay, so far so good. Now we need to make sure that there will be sufficient stock in the distribution centre to satisfy the store requests. Because the supply chain is disconnected, the requests that the stores drop onto the DC today need to be picked within a day or two, so that means that the DC must anticipate (assume) what the stores will request in advance.
A common way to do this is to use historical store requests to forecast future DC withdrawals. In this case, you are making a number of additional assumptions:
That store inventories are largely balanced across all stores served by the DC and have been so historically
That any growth/decline in consumer sales will be accurately reflected in the DC withdrawals with a consistent lag
That there are no expected changes in store merchandising requirements that will increase or decrease their need for stock irrespective of sales
Going further back to the supplier, they have their own internal planning processes whereby they are trying to guess what each of their retailer customers are going to want from them in order to plan their inventories of finished goods. They are now several steps removed from the ultimate consumer of their products and have to apply their own additional set of assumptions.
It’s like a game of telephone where each successive person in the queue passes what they think they heard on to the next.
And if something doesn’t go according to plan, a whole bunch of people need to revisit their assumptions to figure out where the breakdown happened. At least they should, but that rarely happens. Everyone is too busy dealing with the fallout in “crisis mode” to actually figure out what went wrong.
The result?
In stock rates to the consumer in the 92-93% range
Excessive amounts of “buffer stock” to try to cover for all of the self-inflicted uncertainty (assumptions) in the process
Margin loss from taking markdowns on excess stock that’s in the wrong place at the wrong time
So how does an approach like Flowcasting – a fully integrated end-to-end planning process – sustain in-stocks in the high 90s while simultaneously (and significantly) reducing stock levels throughout the supply chain?
It’s not magic. By connecting the supply chain with long term supply projections and keeping those projections up to date, the number of assumptions you need to make are drastically reduced:
You already know the inventory for every item at every location, so you can model the long term need for each individually and roll them up rather than assuming averages.
The planning approach automatically models the impact of any changes in consumer demand by netting against available stock in store and applying the necessary constraints and rounding rules using simple calculations – there’s no need to guess how a changing demand picture will affect upstream supply.
Inventory level decisions (e.g. changes to display quantities and off locations) can be discretely modeled separately from demand and incorporated directly into the store projections in a time-phased manner. You don’t need to make a “same as last year” assumption if you already know that won’t be the case.
However, it’s not perfect and things can still go wrong. You still need to have long term forecasts about consumer demand, which means assumptions still need to be made. But when bad things happen, the information travels quickly and transparently up and down the supply chain assumption-free after that. Everyone knows exactly how they are affected by any botched assumptions about consumer demand in near real time and can start course correcting much sooner. “Bad news early is better than bad news late.”
It’s like playing telephone, except that the first player doesn’t whisper to the next – he uses a megaphone to ensure that everyone hears the same phrase at the same time.
The best offense is a good defense, but a bad defense is offensive. – Gene Wolfe
“We want our people helping customers, not doing back office tasks.”
This seems to be the prevailing wisdom in retail these days, particularly since Amazon became a significant threat to brick and mortar retailers.
This view is backed up by many articles that have made their way to my inbox and LinkedIn feed recently – retailers need to be doting on their customers, going above-and-beyond, providing an experience, etc., etc…
As I read these pieces and hear the arguments, the words all make sense to me, but they tend to make me feel a little out of touch. As a customer, I generally know what I want. If I need product knowledge or advice to choose between options, I turn to Google, not retail sales associates.
In other words, when I shop in person, I just want to be left alone. My idea of a spectacular shopping experience is walking into a store, finding everything on my list and leaving in record time, ideally without any human interaction aside from some pleasantries with the cashier.
After doing a bit of research on this, I began to feel a bit less isolated. Here’s an excerpt from Stop Trying to Delight Your Customers, published in Harvard Business Review in 2010:
“According to conventional wisdom, customers are more loyal to firms that go above and beyond. But our research shows that exceeding their expectations during service interactions (for example, by offering a refund, a free product, or a free service such as expedited shipping) makes customers only marginally more loyal than simply meeting their needs.”
And another from Your In-Store Customers Want More Privacy, also from HBR in 2016:
“Shoppers want a certain level of privacy in a store — and they want to have control over that privacy. In other words, people generally prefer being left alone, but also want to be able to get help if and when they need it.”
These references are a bit old, so I conducted my own (informal and definitely not scientific) survey in the Consumer Goods and Retail Professionals LinkedIn group. Here are the results:
So, the respondents who wanted to be left alone outnumbered those who wanted interaction by a factor of 6. And while LinkedIn’s rudimentary polling feature doesn’t accommodate deep dives on the responses, I would hazard to guess that a majority of the “It depends” respondents probably only want to talk to someone if they can’t find what they’re looking for – that is, the experience has already turned negative and the customer is hoping they can find someone to – hopefully – make it slightly less negative.
Even if I’m wrong about that, answering “It depends” doesn’t indicate that those folks are exactly yearning for staff interaction as an integral part of their shopping experience.
To be sure, there are certainly cases where interaction with knowledgeable staff is a necessary part of the customer experience – e.g. if you’re looking for a luxury watch or need help designing a custom home theatre for your basement – but what percentage of your total retail transactions does that represent?
It’s time for brick and mortar retailers to drop their somewhat defeatist attitude – “We can’t compete with online sellers on convenience, so we’ll compete on service.”
Firstly, for most customers, convenience IS the service they’re looking for.
Secondly, yes you can compete on convenience – by getting better at those mundane “back office tasks” (like stock accuracy and speed to shelf) that puts stock at customers’ fingertips.
There are a lot of low maintenance customers like me out there who just want to interact with your cash register. We are better served by finding the products we want in your aisles, more so than your staff (no matter how friendly or helpful they may be).
Just make it easier for me to do that and I won’t be a bother, I promise.
Moving a retailer from a firefighting mindset to a planning mindset is no small task and requires a lot of emphasis on education, change and butchering sacred cows.
It also invariably requires a technology investment in a new planning system. In theory, the technology piece of this is pretty straightforward, particularly if you choose off-the-shelf planning software that adheres to a few key fundamental principles and meets your core requirements. You bolt it on top of your ERP, master data flows in and you run the batch. Then forecasts, plans and orders flow out. Easy peasy.
To make all of this work, you need a dedicated team that includes:
A mixture of folks from the business who can drive change – grizzled veterans with a lot of credibility across the functional areas (especially Merchandising, Supply Chain and Store Operations) combined with some whiz kids who may be a bit wet behind the ears, but are eager to learn. Their job will be to design new processes, change existing processes within the business and be the “tip of the spear” for driving the change, both internally and with suppliers. You can optionally augment this team with consultants who specialize in this space and bring experience from prior projects.
A technical team who can understand the mission, develop data maps, built and test the integrations, design the batch schedules and course correct when things don’t work out exactly as planned. This team can optionally be supported by the software company and/or system integrators to do some of the heavy lifting on many of those tasks.
An implementation team from the software provider who can work with the business folks to train your team on the new system and aid with configuration, data mapping/structure and workflow design.
That sounds like a dream team, doesn’t it? But is it enough?
Not quite.
Remember earlier when I said that the technology piece of the puzzle is “easy peasy”? Well, that’s only a relative description when compared to the change effort. In absolute terms – and from bitter experience – the technology stuff is often NOT “easy peasy”. At all.
This is why you always need one more person to augment your dream team: The Legend.
Every retailer has at least one of them, but usually no more than a handful. It’s the person whose name always comes up when these types of questions are asked:
Where the hell are we supposed to find that data and who manages it?
I can see the number on the screen, but how the hell was it calculated?
Why the hell did we decide to set things up this way?
These people often (but not always) have grey hair, are closer to the bottom of the org structure than the top and generally toil away in anonymous obscurity until a really big problem needs to be solved – then they’re the ones called upon to solve it. Losing one of these people would be more risky and disruptive to the organization than if the CEO was taken away in handcuffs for insider trading.
The Legend could have virtually any job title in any functional area of the organization, but the actual job description can be summed up in one word: Everything.
The Legend is a critical resource for any initiative that requires master data or touches legacy systems in any way. So, basically all of them.
They know where all of the bodies are buried and often need to throw some cold water on project teams who may have the notion that things are “easy peasy”. They don’t do that to block the path or to be a buzzkill. They just don’t want people wasting their time or making unrealistic assumptions that will foul things up. Don’t worry, they’ll eagerly help you to steer clear of the rocks, because they know where all of the rocks are.
But getting their time to help will be difficult, because they are always being pulled in ten different directions, not to mention tasked with keeping the lights on when more mundane day-to-day issues arise.
They are the critical resources that must contribute to any major transformation, but they are also the people that the business can’t afford to lose to a long term project.
In spite of all the power they wield, they are generally not protective of their knowledge or interested in defending turf. If a promotion offer came along, they may seriously consider it, but they probably won’t be actively seeking one out.
They’d be glad to write up detailed documentation and/or transfer some of their expertise. Maybe you can get some time on their calendar to get that going – there’s probably a 1/2 hour block available 4 months from now.
For ethical and technological reasons, cloning is not really an option right now, so how do you get valuable time from people who have none?
In the interest of long term success and stability, put some of your initiatives on hold and free up some of their time to cross train some junior folks on the more mundane tasks.
Bring in some contractors to backfill some of their “lights on” work and produce documentation while they work on more important matters.
Recognize their value and set aside an important role for them when you get to the other side of your change endeavour.
And it never hurts to give them a hug every now and then.
If it is true that words have meanings, why don’t we throw away words and keep just the meanings? – Ludwig Wittgenstein via Anatol Holt
How do you describe your demand forecasts?
In retail, I’ve had numerous conversations that go something like this:
Me: “Do you really think you’re going to sell 10,000 units on that promotion? You’ve never sold higher than 8,000 at that price point.”
Retail Buyer/Demand Planner: “Yeah, it’s a bit optimistic.”
While less common, forecasts can also be made lower than one would expect (or “pessimistic”, if you will), particularly if an incentive structure exists where people are rewarded when actual sales blow the forecast out of the water.
While these “optimistic” or “pessimistic” numbers are entered into the forecasting system and potentially even drive the replenishment and supply planning functions, it would be a misnomer to call them forecasts.
Saying that a forecast is optimistic is an admission that you believe it’s biased to the high side for no good reason. You can call it a goal, a wish or a dream, but it is NOT a forecast.
In a similar way, a “pessimistic forecast” is a hedge, not a forecast.
Simply put, if you don’t truly believe the numbers you’re producing are a reflection of what’s actually going to happen, then you’re not forecasting. A true forecast is the prediction you’d make if you were obligated to bet $500 of your own money on the outcome.
That’s not to say that the most objective forecast produced by the most unbiased demand planner may not be way off. At its essence, demand planning applies assumptions in an attempt to predict human behaviour. That doesn’t always work out.
That’s also not to say that the words “optimistic” and “pessimistic” have no place in forecasting – so long as there are assumptions to back up the judgments.
For example, “I’m optimistic that we’ll beat last year’s sales, because we’ve sharpened our pricing and caused a key competitor to exit the market for this category.”
Or “I’m pessimistic about the sales outlook for our high price point luxury lines because the economy is in the tank and discretionary spending is going to be way down for the next 12 months.”
But what about the potential missed opportunities? What if we produce an unbiased, objective forecast and it ends up oversold to the point that we run out of stock and leave sales on the table?
Isn’t having a forecast that’s “a bit too high” much less risky in terms of being able to satisfy customer demand?
To be clear, a forecast is just one determinant of the level of stock needed to satisfy customer demand. The other is the uncertainty of the demand. If you apply the uncertainty to the supply side of the equation (i.e. safety stock), then that frees you up to forecast with objectivity.
Yes, the uncertainty needs to be quantified, but if you habitually describe your forecasts as being “optimistic” (which is a euphemism for “biased”), then you’re already doing that anyhow:
Optimistic Forecast = What we think customers might buy
Objective Forecast = What we think customers will buy
It pays to be obvious, especially if you have a reputation for subtlety. – Isaac Asimov (1920-1992)
The sun came up today.
I’ve been tracking it daily in a spreadsheet for months. Please reach out if you’re interested in seeing my data. My suspicion is that you won’t.
In (belated) honour of Groundhog Day, the topic du jour is in stock reporting.
You come into the office on Monday morning, log in to your reporting/BI dashboard and display your company’s overall in stock report for the last 21 days, up to and including yesterday. As you look at it, you’re thinking about all of the conversations about in-stock you’ve had over the last 3 weeks and anticipate what today’s conversations will be about:
For the sake of argument, we’ll assume that there’s no major issue with how you calculate the in stock measure. Everyone understands it and there’s broad agreement that it’s a good approximation of the organization’s ability to have stock in the right place at the right time. (This isn’t always the case, but that’s a topic for another day).
It certainly looks like a bit of a roller coaster ride from one day to the next. That’s where applying some principles of statistical process control can help:
By summarizing the results over the last 21 days using basic statistical measures, we can see that the average in stock performance has been 92% and we can expect it to normally fluctuate between 86% (lower control limit) and 97% (upper control limit) on any given day.
In other words, everything that happens between the green dashed lines above is just the normal variation in the process. When you publish an in stock result that’s between 86% and 97% for any given day, it’s like reporting that “the sun came up today”.
Out of the last 21 days, the only one that’s potentially worth talking about is Day 11. Something obviously happened there that took the process out of control. (Even the so-called “downward trend” that you were planning to talk about today is just 3 or 4 recent data points that are within the control limits).
I used the word “potentially” as a qualifier there, because statistical process control was originally developed to help manufacturers isolate the causes of defects, so that they could then apply fixes to the part of the process that’s failing in order to prevent future defects with the same cause. In most cases, the causes (and therefore the fixes) were completely within their control.
Now when you think of “the process” that ultimately results in product being in front of a customer at a retail store, there are a LOT of things that could have gone wrong and many of them are not in the retailer’s control. In the example above, it was a trucker strike that prevented some deliveries from getting to the stores that caused some of them to run out of stock. Everybody probably knew that in stock would suffer as soon as they heard about the strike. But there was really nothing anybody could have done about it and very little that can be done to prevent it from happening again.
So where does that leave us?
Common Cause Variation is not worth discussing, because that’s just indicative of the normal functioning of the process.
Special Cause Variation is often not worth discussing (in this context) unless you have complete control over the sub-process that failed and can implement a process change to fix it.
So what should we be looking at?
In terms of detecting true process problems that need to be discussed and addressed, you want to look at a few observations in a row that are falling outside the established control limits, investigate what changed in the process and decide if you want to do something to correct it or just accept the “new normal”. For example:
But you can also take a broader view and ask questions like:
How can we get our average in stock up from 92% to 96%?
How can we reduce the variation between the upper and lower limits to give our customers a more consistent experience?
By asking these questions, what you’re looking for are significant changes you can make to the process that will break current the upper control limit and set a new permanent standard for how the process operates day to day:
But be warned: The things you need to do to achieve this are not for the faint of heart. Things like:
Completely tearing apart how you plan stock flow from the supplier to the shelf and starting from scratch
Switching from cheap overseas suppliers to ones who are closer and more responsive
Refitting your distribution network to flow smaller quantities more frequently to the store
They all have costs and ancillary additional benefits to the operation beyond just improving the in stock measure, but this is the scale of change that’s needed to do it without just blowing your inventory holdings out of the water.
Reporting your in stock rates (or any other process output measure for that matter) regularly is a fine thing to do. Just make sure that you’re drawing the right conclusions about what the report is actually telling you.
