THE OMNICHANNEL HOLIDAY CHALLENGE WITH STORE INVENTORY AND FORECASTING

Holiday shoppers have just a few more days to get their shopping done. Do they order online and get it shipped? Do they order online and then pick up in store? Or do they go into a store hoping to walk out with the items they want to purchase? Retailers have the challenge of meeting all of these needs, many of them using store inventories as distribution centers to handle online purchases, whether shipping it to the customer’s home or having it available so they can pick up the item in the store.

An online customer who is having their products shipped does not care which store or warehouse handles their purchase. A shopper in the store or on the way to a store does – they expect the item to be available on the shelf. Retailers are using different strategies to manage these needs. Some, such as Target, are holding inventories back from online purchasers in order to keep inventory on the shelf for their in-store shoppers. In Target’s example, an online shopper may try to go online and buy or reserve an item in store but are unable to do so. Other retailers, like Toys ‘R Us, have a “first-come, first-served” strategy. The big challenge is for retailers to determine, by product and by store, how to divvy up the store’s stock, and need to forecast in-store purchases to try to have the right amount of inventory on the shelves.

 

Tracking item-store inventories as real time as possible is the best way for these retailers to make these forecasts. Retailers without inventory systems who can keep up with purchases are having to keep extra available in the store and not online, in order to avoid the mistake of selling the same item to two customers around the same time.

Source: Chicago Tribune.com, Wall St. Journal

Suppliers Need Better Forecast and Demand Projections

A recent benchmarking survey of key consumer product metrics by Gartner and IDC Manufacturing Insights found SKU-level forecast error rates one month out had an average of 21.9%. For new products, the error rate grew to 48.3%. In that report, Gartner says consumer goods companies will continue to get better at using POS data and near-term demand signals to improve short term forecast accuracy and replenishment plans. Top performing companies showed forecasting error rates much lower than the averages, dropping to 11.7%, and 34.7% for new products.

Forecasts with high levels of error result in many supply chain issues, such as the wrong product mix – excess inventory of some products and not enough of others, higher overall inventory – excess cash tied up in inventory and poor cash flow, increased waste in production resources and customer service problems.

Gartner says performance leaders are not only working on their forecasting but also leveraging downstream data to better predict how much to replenish based on what is being consumed downstream.

Those leaders "are already working closely with their key retail accounts to manage vendor-managed inventory (VMI) replenishment based on retail warehouse inventory and movements, and some point of sale (POS) data reflecting consumer pull through the pipeline. They set inventory buffers based on product level, average daily demand, range of demand variation and supply reliability. These buffers are reviewed regularly to reflect recent patterns, rather than averaging two years of history to take out the highs and lows."

In addition, increased analysis of actual SKU-level demand to determine average daily usage, near-term demand patterns and range of variation will improve alignment of buffer inventory to cover demand volatility with less reactive disruption in the upstream supply chain than we see today.

As a result, overall inventory will be reduced because the mix is better aligned with what is selling. There will be less slow-moving and obsolete inventory for those products replenished based on downstream consumption. This will improve cash flow and cash conversion cycle time, and it will reduce write-offs.

Resource: Supply Chain Digest

Demand Driven Planning in 2013

The availability of retail point of sale data over the past several years has created the opportunity for vendors to gain a detailed understanding of consumer demand at the retail point of sale.  Actual consumer demand at the retail point of sale presents a more accurate and timely picture of how your SKU’s are selling than retailer forecast advice or even retail purchase orders.  So why don’t all vendors collect EDI 852 or retail POS data from their customers and use it for creating forecasts and managing sales?  There seem to be several myths holding vendors back….

Myth #1: Collecting and analyzing EDI 852 / retail POS data is expensive and complex.  In a few limited cases like Home Depot and Menards it is true that the simple process of collecting the data has some expense.  Home Depot EDI 852 for example must be collected using a VAN so there are data transmission charges.  Menards charges a vendor to purchase a RSA SecurID.   But most retailers make EDI 852 or retail POS data available for free and even when there is a fee the benefits exceed the expenses.   Extracting the data, matching to item catalog details and store details does require some expertise but there are many SaaS applications now like Accelerated Analytics which will outsource the technical requirements for an affordable monthly fee.  By monitoring the consumer demand and inventory on hand at a SKU / store level of detail a vendor can proactively work with the retail replenishment manager to avoid out of stocks.  Every sale you get that would have been lost due to an empty shelf is returning value and paying for the expense of collecting and using the EDI 852 data.  How many lost sales do you need to recover a monthly data management fee that is typically less than $2,000?   At a chain like Home Depot with roughly 1900 stores in the USA the answer is not very many.

Myth #2:  My buyer won’t accept replenishment recommendations.  We hear this all the time – “I realize I could probably increase my in stock rate using EDI 852 / POS data but my retail customer uses automated replenishment or has a fixed open to buy plan so my recommendations fall on deaf ears”.  Several things are at work with this myth.  First, most vendors are operating on an assumption that if they talked to their buyer, they would discover is inaccurate.  I’ve talked to buyers at many retailers and I get a consistent answer – if the vendor can quantify the problem and provide an accurate order recommendation I will take it into consideration.  Second, the vendor has to demonstrate a competency in using the data for basic tasks like sales monitoring before they try to recommend orders.  I’ve seen countless examples of a vendor providing sales reporting and value to a buyer who then gains confidence the vendor can get the demand forecast right.  Finally, you have to start off slow.  Start with your highest turn products at your A volume stores and calculate the lost dollars sold for an 8 week period.  Then go to your buyer with a summary of your findings and actions to improve in stock and quantify the sales opportunity for both of you.  Make conservative recommendations to increase the WOS by one week so you gain back some sales but avoid loading the store with inventory and dropping your GMROI.  They have the same goal as you – to sell more product!

Every vendor that sells a product through a retail store should invest into analyzing retail point of sale data and using it for creating detailed action plans.  The data acquisition and reporting costs are very low when you consider them as a percentage of your retail sales and the upside benefits of increased sales, better assortment planning, and optimal inventory on hand are huge by comparison.  Let’s make 2013 the year that all vendors make the investment.  

Even Simple Forecasting Can be High Value

Forecasting is part mathematics and part art, and due to this,  it can be extremely complex, but even simple forecasting can be very valuable.  Many vendors get too tied in a knot over the complexity of item and store level forecasting and then nothing gets done.  We encourage all vendors to start with the basics and increase the sophistication of your model over time.  Forecasting will provide you with critical information necessary to avoid stock outs and maximize your retail sales.  And if you do it right, you can gain a critical advantage over your competition and demonstrate to your buyers that your company is working hardest to be a good partner.

There is a simple process for forecasting.

1.   Sum the units sold for the most recent 5 weeks.

2.   Sum the units sold for the same period last year.

3.   Calculate the percentage change in units sold.

4.   Sum the units sold from the current week forward 16 weeks last year.

5.   Adjust the 16 week total to current year demand by multiplying times the calculated percentage change.

6.   Divide the adjusted 16 week units sold by 16 to get an average weekly forecasted units sold.

7.   Calculate a weeks of supply on hand by dividing the current on hand by the weekly forecasted units sold.

The forecast can be customized to your business by adjusting the base for the foundation (5 weeks above) to be longer or shorter depending on how heavily seasonality or promotional activity affects your sales.  E.g. shorten the period if your business is highly seasonal and lengthen the period if your business is primarily replenishment with little variability.  You can also adjust the current on hand value by adding any in transit inventory so you don’t overstock. 

Now that you have a good idea of the forecasted weeks of supply, you can use this value to make inventory production decisions.  Each vendor’s lead time to produce and land product at a store will vary, so you will need to determine your target weeks of supply and take appropriate action.

Retail Replenishment - How tuned in are you?

I spent about 9 hours yesterday analyzing sales, order and forecast data for Walmart, Home Depot and Lowe's vendors, and I am somewhat surprised by my observations.  It's pretty clear that there are some min/max rules in place, as I can see patterns to the order quantities based on the OH inventory and the order case pack quantities.  However, what surprises me is that I also see a large number of what I would guess are "manual overrides." That is UPC/stores which clearly need inventory and fall under the minimum OH of other stores, but which do not have an open order, and UPC/stores that are clearly overstocked (e.g. high WOS), and yet have an open order.  It makes sense that there would be automated replenishment rules in place and then some lead-way for the buyer/replenishment manager to make judgment calls, so that leads me to my question....

What do you know about your key retail customer's replenishment rules?

• Simple min/max ordering?
• Based on OTB dollars?
• WOS trigger?
• At what level is demand calculated?  e.g. Category/Region, Category/State, Sub-category/State
• Under what situations will the buyer do a manual override?

How much do retail out of stocks cost?

A recent RIS article  titled, “How Much Are Out-of-Stocks Costing You? Much More Than You Might Think”, By Greg Buzek, provides more evidence that retail out of stocks are costing vendors huge lost sales.  Buzek quantifies the scope of the loss; “A retailer that invested in completely fixing its out-of-stock problem, would gain a solid competitive edge. The average retailer could increase same store sales 3.7%, by converting all perceived out-of-stocks into transactions. Specialty soft goods could have the biggest potential win: solving out-of-stocks would boost their same-store sales 7.1%, while department stores would see a 4.2% jump.”

The good news is we have seen dramatic improvements in in-stock performance by active store and item level analysis.  The methodology is pretty straightforward:

1. Determine the lead time from order to product arriving at a store.  Let’s say this averages 2 weeks.  This is your minimum on hand weeks supply to avoid a stock out.
2. Next calculate the average weekly sales velocity for each item, and each store.  Yes, you must know the average sales velocity for each peg or shelf position.
3. Calculate the weeks supply on hand for each item and store by dividing the current on hand inventory by the average sales velocity.
4. Filter the results to show only those items with less than the 2 weeks supply on hand.  These are the stores you need to make sure you place an order immediately to avoid a stock out.

This type of analysis is not hard to do, but if you don’t have the proper tools it can be very time consuming.  But, it’s well worth the effort.  If you can improve your in stock performance by even 2%, you stand to gain significant sales.

Next Article: Increasing Sales By Managing Out of Stock Inventory

Retail sales improvement requires careful forecasting

The WSJ reported retail sales Rose 3.3%, showing signs consumers are returning to stores.  This is a great sign for the retail market as it seems a turnaround may be in the works.  Macy’s posted a 3.4% increase, Saks reported 7% and Costco 8%.  As demand begins to increase, vendors need to keep a careful eye on the supply chain.  Retail buyers have been operating on low open to buy for over a year, so inventory levels may be below where they will need to be to satisfy demand.  Vendors using EDI 852 data for forecasting need to make some careful adjustments to their forecasting model to not be caught by surprise.  Here’s why.  Forecast models use historical demand as the foundation for current year predictions, but last January was a terrible month for retail sales, so a simple look at comp year demand will give a misleading result.  To correct for this, vendors should be considering not only last year’s demand, but also the prior year’s demand and the current period trend.  By combining these three numbers, vendors will have a more accurate model and hopefully not get caught by surprise.  But even with a good forecast, we expect sales to be unpredictable for the foreseeable future, so vendors must carefully watch demand and inventory levels by analyzing the EDI 852 data weekly or even daily and making push order recommendations to their buyers.

How to get a buyer to accept a forecast recommendation

Moving into 2010, most retail economists are predicting a continuation of the “do more with less” retail buying strategy. This will mean high fill rates, accurate forecasting, and proactive out of stock analysis will be critically important to maximizing sales.

I would like to begin a discussion on the best practices for getting a replenishment manager to act on a SKU forecast recommendation. Here are a couple thoughts to get the conversation going.

• Carefully analyze fill rates and ensure all orders are completed on a timely basis before approaching the buyer with a forecast recommendation. It’s basic ‘blocking and tackling’, but if your fill rate is below expectations, the buyer is not likely to accept a recommendation for more inventory.
• Whenever possible, use actual comp period historical sales demand as the basis for forecasting current year sales. Conservative forecasts built on actual sales history have higher credibility and less chance of error.
• Concentrate your analysis on A stores and position your recommendations to the buyer on 100 or 200 ‘test stores’ to start.
• Carefully document the ‘before’ and ‘after’ performance so when you go back recommending an expansion beyond the test stores, you have a positive business case.

Please chime in and add your thoughts and experiences.