Supplier Scheduling | Demand Clarity

Respect the building blocks, master the fundamentals, and the potential is unlimited. – PJ Ladd

For the last 30 years or so, we’ve been working with retailers to integrate their planning and drive their entire supply chain from consumer demand at the shelf by:

Adopting demand planning as a core discipline
Using simple arithmetic (Flowcasting) to model all future product flows in the supply chain to meet the demand at the shelf
Sharing continuously updated replenishment schedules with suppliers so that lead times can be collapsed and suppliers can plan their finished goods inventory and production based on the retailer’s consumer demand driven needs

A retailer who adopts this model can expect a 5-7% in-stock improvement, while simultaneously increasing turns by 20-40%.

This is because – for the most part – retailers have all of the information they need internally to make this happen for every item at every location. They just to organize the data, processes and systems around the three core principles outlined above.

But what about retailers’ suppliers? Most are keenly aware that their supply chain activities should be tied directly to the shelf, but in order to be truly actionable and valuable to their business, the plans calculated from the shelf back must answer this question:

Specifically, what can a CPG supplier do if, say, only 2 or 3 of its retailer customers (representing maybe 20-30% of its total sales) are providing valid supplier schedules? It’s better to have this information than not, but that still leaves a gaping hole in their inventory and production planning.

Here’s a Bunch of Data – You Figure it Out

The good news is that retailers have become more and more willing to share key data with suppliers to help them fill in this gap. It started in earnest with Walmart’s Retail Link platform in the early 1990s and soon after many other large retailers started to follow suit. Suppliers were able to get their hands on gargantuan quantities of core retail data, but it was basically a one-way proposition and there was an (unrealistic) expectation that the information shared contained everything a supplier would need to unlock the secrets of retailer buying behaviour.

Suppliers are always salivating to get their hands on as much of their customers’ data as possible to try to piece the puzzle together. It started with POS sales and store inventories. Then some retailers started sharing their store sales forecasts, some replenishment policies, planogram information and promotional plans. But it is still a patchwork quilt with some retailers providing tons of data, others just sending POS history and many sharing nothing at all.

And while more and more retailers are reengineering their planning processes to provide schedules to suppliers, progress has been slow.

Modeling the Complete Supply Chain

Flowcasting provides the model for predicting all stock flows in the extended supply chain from consumer demand back to the supplier. It’s driven by simple, step-wise arithmetic (DRP logic) and explicitly and transparently works backward to answer the fundamental questions of retail/CPG supply chain planning.

The problem is large, but fairly simple. And to reliably get to the answer the CPG supplier is looking for, a lot of input data is required.

The good news is that retailers retain all of those inputs and seem willing to share more and more of it with suppliers. Even if those retailers are a bit late to the “adopt a planning mindset internally” party, at least they have all the puzzle pieces that their suppliers might be able to collect and assemble themselves. It can strengthen the retailer/supplier relationship significantly when a full multi-echelon plan can be developed with a common data set for making decisions – even if it is the supplier who is making it happen.

Scaling Up

This leads to another challenge for suppliers: How to get enough volume from their retail customer base represented in their plans in order to be able to stabilize the overall production schedule and significantly reduce costs?

For example, if a supplier’s top 10 customers represents 85% of their sales volume for most of their products then they would either need all 10 of those customers to adopt a planning process that gives them a replenishment schedule or to provide the data that would allow the supplier to construct it themselves.

While internal retailer adoption of industry leading planning processes provides the most value to both the retailer and the supplier, it could take a long time for the supplier to get to a critical mass where a large majority of their production volume is driven directly by consumer demand at the shelf. In the meantime, they’re left trying to fill the gaps with emails, spreadsheets, sticky notes and a whole lot of assumptions.

But even if an impatient supplier would rather just get all of the data themselves to get to the answer more quickly, it’s not quite that simple either. All of the input data discussed above would need to be collected, standardized and modeled for every one of those 10 retailers and maintained on a regular basis, with people watching over it to ensure that results are understood not just internally, but with each of those retail customers. This may be possible for some of the enormous global brands, but mid-tier suppliers would likely not have the resources to do this on their own.

Data Aggregators – The Missing Puzzle Piece?

For time immemorial, consolidation has been the primary driver of efficiency and cost effectiveness for moving goods in the global supply chain. It’s why product moves on container ships, rail cars and 53 foot trailers. It’s why retailers use distribution networks. It has enabled new distribution methods like cross-dock and pick-to-zero flowthrough.

Now I’m wondering if the same principle also applies to supply chain planning data.

One of the by-products of Walmart’s success with Retail Link in the 90s has been the rise of data aggregators who collect and disseminate valuable retailer data as a business.

While I’m not well versed on all of the variations, it boils down to a simple premise: Retailers provide their data to an aggregator and the aggregator cleanses/normalizes it and sells it by subscription to suppliers. There is usually an analytics engine that allows a supplier to “slice and dice” across retailers and markets, roll up and drill down and apply some rudimentary rules for detecting bottlenecks and opportunities.

While there is quite a bit of fragmentation in this space, it has the potential to be something close to a “one stop shop” for CPG suppliers to get some visibility to the 99% of the supply chain that they don’t control. What’s missing is a cohesive and organized way to use all of this centralized information to answer the question that’s on every supplier’s mind:

It’s by no means a slam dunk, but the puzzle pieces are there. And data aggregators are continually adding functionality to their offerings to spin the raw data they collect into gold for their subscribers.

Could one of those offerings one day be a Flowcasting model that makes maximum use of their treasure trove of retailer data?

An object in possession seldom retains the same charm that it had in pursuit. – Pliny the Younger (62AD – 114AD)

As with all other industries and disciplines, retail supply chain planning is about to be rocked to its core by new AI technologies. Or maybe it’s going to fail spectacularly. The answer is probably somewhere in the middle.

Well, maybe.

We’re still at an early stage where multiple experts (people with far more knowledge about AI than I do) can have diametrically opposed views of where the technology is headed and they’re all looking at the same information.

Personally, I (along with nearly everyone else) avoided buying Amazon stock in 1996 when they were just an online bookstore hemhorraging cash every quarter. My point is that I’m not a futurist or an expert in AI, so I won’t be weighing in on whether or how it will ultimately impact how supply chains are planned in the years to come.

But what I think we can do right now is try to determine what the potential size of the prize actually is in terms of business results to a typical retailer.

First, we need to set a baseline of what retailers can achieve without the use of AI technology. Continuous time-phased planning has been around in manufacturing and distribution for quite awhile, but it’s still a relatively new concept in retail. And here I’m not just talking about forecasting and replenishment, but truly modeling the current and future policies and constraints of the supply chain from the retail store to the manufacturing plant over a long term time horizon – we call it Flowcasting.

It’s been well documented for at least the last 30 years that retailers who don’t plan in an integrated fashion from the store shelf back to their supplier base achieve in-stock levels in the 92-93% range. Those same studies estimate that every 3% improvement in in-stock leads to a 1% increase in top line sales.

Through our work with numerous retailers over the last 3 decades, we have proven that in-stock can be sustainably increased from the 92% range into the 98% range while simultaneously improving inventory turns by 20-40% (depending on item velocity and minimum merchandising requirements in the stores). This can be done by following some proven principles and strategies from manufacturing and distribution (tailored to apply to a retail business), namely:

Create a forecast of true consumption for every item at every selling location
Calculate long term replenishment plans for every item at every stocking location and link plans up the chain (i.e. from stores to DCs to suppliers)
Keep the plans up-to-date with any known current or future external or internal impacts to demand or supply
Make sure that everyone (including suppliers) always has an up-to-date version of the plan so everyone is working to a single set of numbers

This is all standard, old school DRP/MRP stuff. No magic. No AI. And while technology (especially the ability to process large data volumes) is enabling these results, the shift in mindset on the part of the retailers is as important, if not more so:

If you know about something today that will happen in 3 months (e.g. a promotion, new product launch or store opening), get it into the plan today
Don’t keep the plans a secret – let everyone see the plans and assumptions that go into making them so that everyone can be aligned on how to execute them
Work by exception – let the plans tell you about potential problems that you need to start working on now before they become actual problems

This brings us back around to the actual size of the prize of AI in supply chain planning. The primary gap that needs to be closed in retail is the stubborn and pernicious 8% out-of-stock rate.

By applying process discipline, planning technology that enables it (without a single drop of AI) and connecting up-to-date plans up and down the supply chain, this gap can be reduced to 2%, while simultaneously making substantial improvements to inventory turns.

Even with highly advanced artificial intelligence that hasn’t even been developed yet, getting to 100% in-stock is a practical impossibility – with millions of item/locations in the retail supply chain, there is too much that can go wrong operationally outside the control of the planning function. So, at least as far as in-stock is concerned, the net incremental benefit above continuous planning is less than 2%.

Once the technology matures, there will undoubtedly be ancillary benefits of greater automation to planner productivity (hopefully not at the expense of accountability), but you don’t need sophisticated AI models to squeeze the vast majority of the juice from the lemon.

A lot can be achieved by just giving people the right processes, tools and a single plan that they can control and execute together.

Tag Archives: Supplier Scheduling

The Size of the Prize