Ipso Facto

Posted on October 5, 2016 by Jeff Harrop

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

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

Posted on August 3, 2016 by Jeff Harrop

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

‘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

Posted on June 1, 2016 by Jeff Harrop

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

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

Don’t.

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

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

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

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

Yes! Wait, no! Okay, maybe…

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

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

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

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

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

So is measuring forecast accuracy pointless?

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

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

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

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

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

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

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

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

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

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

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

But some do.

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

Flowcasting the…

Posted on April 6, 2016 by Jeff Harrop

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

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

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

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

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

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

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

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

The Merchandising Organization

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

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

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

Store Operations

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

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

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

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

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

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

Human Resources

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

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

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

First

Posted on March 2, 2016 by Mike Doherty

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

To summarize:

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

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

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

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

Congratulations, and well done!!

Overly Sophistimicated

Posted on February 3, 2016 by Jeff Harrop

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

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

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

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

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

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

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

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

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

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

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

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

Bad Habits, Part 2

Posted on December 2, 2015 by Jeff Harrop

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bad Habits, Part 1

Posted on October 7, 2015 by Jeff Harrop

Bad habits are like a comfortable bed, easy to get into, but hard to get out of – Anonymous

Friedrich Nietsche said “Most bad habits are tools to help us through life.”

This is especially true in the world of retail supply chain planning. When retailers embark upon implementing time-phased planning, it is tempting – from a change management standpoint – to tell people “this isn’t much different than what we do today” or “we’re essentially doing the same thing in a different way”.

While this is partially true at the 10,000 foot level, the things that are different are fundamentally different.

This month, we’ll explore a bad planning habit that is particularly insidious, because it is often treated as best practice in a non-Flowcasting world: Using the forecast as a “joystick” to control product flow.

There comes a time in every retailer’s life when product must flow further in advance of customer demand than desired, such as for capacity blowing seasonal peaks, planogram resets or promotional display setups.

In a reorder point world, there is little means to plan these types of things in advance. Replenishment rules such as safety stock apply “right now”, making it exceedingly difficult to do a lot of advance preparation in the system. This leaves 2 options:

Plan all of your safety stock updates in advance and use an Outlook reminder to update the values at just the right time to trigger orders when you want them to arrive.
Spend hours creating manual orders/transfer requests with the future ship dates you want – and pray that things don’t change a lot in the meantime that will require you to change those dates/quantities as it gets closer to execution time.

Neither very good options to be sure.

But wait!

There is a third option. By putting an artificial spike in the forecast at around the time you want product to arrive at various locations, you can ‘trick’ the system into flowing product when you want, but also take advantage of changing inventory levels right up until the lead time fence, thereby letting the ordering happen automatically.

To some extent, this makes sense in a reorder point world. Often, it can be the only efficient and automatic way to control flow. And it doesn’t do too much damage, because the forecast really only exists to trigger an order for an item at a location.

So what would happen if this approach was applied in a Flowcasting world? Not much: just unstable plans, incorrect capacity and financial projections and general chaos within operations and the vendor community.

That’s because, with Flowcasting, the sales forecast doesn’t merely drive ordering for an item at a location. It drives replenishment across all locations in the supply chain simultaneously. It drives capacity planning for the DCs and transportation department. It drives labour planning at the stores. It drives revenue, purchasing and inventory projections within the merchandising teams and finance.

Simply put, the forecast must always be representative of how much you expect to sell, where you expect to sell it and when you expect to sell it. And with the visibility afforded by a reasonable sales forecast, many options become available for controlling the flow of goods as necessary.

In other words, once you make the leap to Flowcasting, the bad habit of using your forecast as a ‘joystick’ to control replenishment is no longer a ‘tool that helps you through life’ – rather, it is one of the quickest possible ways to make your life a living hell.

The Biggest Mistake You’ll Ever Make

Posted on August 5, 2015 by Jeff Harrop

You must learn from the mistakes of others. You can’t possibly make them all yourself. – Sam Levenson

‘How does it work today?’

It’s the first question any self respecting project leader or consultant asks on day 1 of a new initiative. It’s critically important to know the ‘state of the nation’ before you embark on any sort of effort to make the changes needed to improve results.

After asking this question, you set about the task of learning the current practices and procedures and documenting everything to a fairly low level of detail (along with the most common variations on those practices that you’ll almost always find in companies of any size).

In most retail organizations, you’ll hear versions of the following:

For non-promoted periods, our purchasing is mostly automated with a short lead time, but we buy promotions manually with a longer lead time from the vendor.
Our stores ‘pull’ product in non-promoted weeks, but for big events like promotions and shelf resets, we ‘push’ the product out to them .
The system will recommend orders, but our analysts review all of the recommendations beforehand and have the ability to cancel or modify them before they go out to the suppliers.

After a few weeks, you’ve become a pseudo expert on the current state landscape and have everything documented. Now it’s time to turn this retailer from a reactionary firefighter into a consumer driven enterprise.

And that’s when you make the biggest mistake of your life.

After summarizing all the current state practices with bullet points, you save the document with the title ‘Future State Requirements’. You don’t know it at the time, but you’ve just signed yourself up for years of hell with no light at the end of the tunnel. If it’s any consolation, you’re definitely not alone.

Common practices, whether within your organization or throughout your industry as a whole, represent ‘the way everybody does it’. This does not equate with ‘that’s the only way it can be done.’

At the time, it seems like the path of least resistance: We’ll try to do this in such a way that we don’t disrupt people’s current way of doing things, so they will more easily adopt the future state. But if you’re moving from a ‘reactive firefighting’ current state to a ‘demand driven’ future state, then disruption cannot be avoided. You might as well embrace that fact early, rather than spending years of time and millions of over-budget dollars trying to fit a square peg in a round hole.

So how could such a catastrophe be avoided? All you really need to do is dig a little deeper on the ‘common practices’. The practices themselves shouldn’t be considered requirements, but their reason for existing should. For example, look at the three ‘common practices’ I mentioned above:

‘For non-promoted periods, our purchasing is mostly automated with a short lead time, but we buy promotions manually with a longer lead time from the vendor.’

Why does this practice exist? Because, generally speaking, the sales volume (and corresponding order volume from the vendor) for a promoted week can be several times higher than a non-promoted week.

So what’s the actual requirement? The vendor needs visibility to these requirements further in advance to make sure they’re prepared for it. Cutting promotional orders in advance is the currently accepted way of skinning that cat – but it’s by no means the only way.

‘Our stores ‘pull’ product in non-promoted weeks, but for big events like promotions and shelf resets, we ‘push’ the product out to them.’

Why does this practice exist? More often than not, it’s because the current processes and systems currently in place treat ‘regular planning’ and ‘promotion planning’ as distinct and unrelated processes.

What’s the actual requirement? To plan all consumer demand in an integrated fashion, such that the forecast for any item at any location represents what we truly think will sell, inclusive of all known future events such as promotions.

‘The system will recommend orders, but our analysts review all of the recommendations beforehand and have the ability to cancel or modify them before they go out to the suppliers.’

Why does this practice exist? Usually, it’s because the final order out to the supplier is the only ‘control lever’ available to the analyst.

What’s the actual requirement? To be able to exert control over the plan (items, locations, dates and quantities) that leads to order creation, allowing the actual ordering step to be an administrative ‘non event’.

So, once you’ve identified the true requirements, what’s next? A lot of hard work. Just from the 3 examples above, you can see that there are several change management challenges ahead. But at least it’s hard work that you can see and plan for ahead of time.

Woe to those who choose what they think is the easy path at the outset only to suffer the death of a thousand surprises after it’s far too late.

In Praise of Non-Collaboration

Posted on June 3, 2015 by Jeff Harrop

According to dictionary.com, the definition of ‘collaborate’ is: ‘to work, one with another; cooperate’.

Recently, I received an email from Supply Chain Digest asking me to participate in a survey called ‘The State of Retail and Vendor Supply Chain Relations 2015’ – perhaps you received it too. (As a consultant, I never participate in these surveys as I feel it could taint the results, but I’m always interested in what they’re trying to find out).

The Research Summary reads: ‘What is the state of retailer and vendor/supplier relations today? Is it getting better and more collaborative – or heading the other way?’ The implication is crystal clear: More Collaboration = Better.

It seems foolish to argue against that. But that’s just what I’m going to do.

Over the years, supply chain collaboration initiatives were conceived with the notion that ‘two heads are better than one’, especially when there’s uncertainty afoot that could cause havoc in the supply chain, such as for promotions or product launches.

And because trading partners’ planning processes weren’t integrated with a common set of numbers, collaboration was seen as the way to bridge the gap (i.e. ‘we have two sets of plans that are misaligned, so let’s talk our way through it until we agree’). But to what extent is collaboration necessary (or even advisable) when trading partners are fully integrated?

Collaboration is work. Integration is effortless.

All that said, I would tend to agree with the ‘two heads’ argument, so long as all available information is shared between the ‘heads’ This is rarely the case, however, and it’s most often the result of inability to share, rather than unwillingness to share.

Consider a common scenario whereby a retailer and a supplier collaborate on a sales forecast. In order for the demand picture to be complete, the following information must be known and disclosed during the collaboration:

Pricing and promotion strategy for the supplier’s products at the retailer’s stores
Pricing and promotion strategy for the supplier’s competitors’ products at the retailer’s stores
Pricing and promotion strategy for the supplier’s products at the retailer’s competitors’ stores

Point 1 is generally already known by both parties. Points 2 & 3 are at best, unethical (and at worst illegal) for either party to share with the other.

It’s sort of like having two people collaborate on what bet to place on a 5 card poker hand. Each person can only see 3 of the 5 cards with one common known card between them and they aren’t allowed to discuss the cards they can see with their collaboration partner. But the strength of the hand is determined by all 5 cards together.

As a general rule, the retailer faces the customer and they know the breadth of their offerings across all of their competing and complementary products. They may not know exactly what their competitors are doing that could impact their sales, but they pretty much know everything else. Given that the supplier is ethically prevented from providing the one missing piece of information, what value do they add to the collaboration process?

Another example is collaboration on network efficiency to alleviate capacity issues at retail DCs during peak periods. The problem here is that a capacity constraint occurs as a result of a number of straws breaking the camel’s back. Only the retailer has the visibility to all of the straws. How can a collaboration with any single supplier (straw) result in a plan to smooth out the flow for the entire building?

Note that I’m not suggesting that retailers and suppliers shouldn’t be talking to each other when circumstances require it. In the capacity constraint example, the retailer has all the information they need to detect the constraint and figure out the best way to circumvent it. At that point, discussions may need to happen with some suppliers to pull shipments ahead or maybe bypass nodes in the supply chain, but at that point, they’re really just working out execution details rather than ‘collaborating on the plan’.

When you think about it, the supply chain is really not a chain at all – it’s a web. With the exception of private label goods or exclusive supply arrangements, the notion that a retailer and a supplier can ‘act as a single entity in service to the consumer’ is not as easy as it sounds – even with advanced planning processes like Flowcasting. Each retailer offers products from many competing suppliers and each supplier provides their products to many competing retailers.

So if the idea of collaboration is somewhat flawed, then what is better?

Basically, retailers need to get their houses in order and build all of their planning activities around sales at the store shelf (using Flowcasting, of course), incorporating all information they know into the plan – inventories, shipping/receiving schedules, case pack sizes and the like.

Once constructed by the retailer, these plans can be shared directly with suppliers, allowing them to ‘read the retailer’s mind’ without having to second guess or do a lot of ‘collaborating’ back and forth. Discussions between business partners only need to occur when either party foresees difficulty in executing the plan.

Why collaborate when you can integrate?