In September, Walmart raised the requirements on its OTIF program. The retailer is now asking all suppliers to meet a 98% service level for both on time and in full, regardless of the product category and transportation method. If you don’t achieve these targets – delivering 98% of all items ordered and on the Must Arrive By Date 98% of the time – Walmart will charge you a penalty of 3% of the Cost of Goods Sold (COGS) of non-compliant cases.
The reasoning remains the same as what Walmart’s Senior VP of Flow and Supply Chain explained at last year’s Gartner Supply Chain Executive Conference. That was shortly after the retailer last raised requirements. Walmart wants to actively engage suppliers in the process of meeting consumer demand by improving product availability and inventory management. Ultimately, the goal is to prevent lost sales when shoppers can’t get what they want, when and how they want it.
In the same session, Gartner analyst Simon Bailey emphasized the “root cause loss tree” is a critical tool to improve performance. If you examine what caused OTIF misses in the past and can pinpoint the root causes, you can proactively address those areas in the future and avoid further penalties.
For example, are particular DCs associated with a majority of late shipments? Or a particular carrier? Are there specific problem products that lead to orders not filled in full? Is inaccurate demand forecasting the culprit?
Each of these possibilities would lead to a different response. And while all probably have some role to play, focusing on the biggest problem areas will keep you from wasting valuable time and resources.
Let’s look at the data you can use to conduct this type of root cause analysis and what it takes to reveal the insights you need.
Walmart provides a good looking OTIF scorecard, which shows your performance about two weeks after the fact. They recently updated it to break out On Time and In Full separately. On Time is further broken out by transaction type – prepaid or collect. In Full is broken out by four general product categories.
Walmart does recognize that sometimes they are the reason the requirements aren’t achieved. This is reflected in Accountability and the number of cases with Walmart accountability, vs. the supplier or on time/in full.
Beyond that, this report offers little insight into problem areas. It’s simply too high level. It aggregates performance over an entire week, which typically consists of multiple POs, each containing multiple products and multiple destinations.
“Too much of the current reporting washes out the details and hides problems.”
To conduct more granular analysis, you need the PO level data. This information is also available in RetailLink, though you have to look for it separately. It offers details for every order, including:
With this granularity, you can start identifying some patterns, like if there are particular SKUs or DCs that have been most problematic. Surfacing insights across all your POs helps pinpoint the top issues you need to solve and watch out for.
Having the data provided doesn’t mean it’s quick or easy though. First, of course you have to download the data from RetailLink and consolidate it in Excel or your tool of choice. Then, since the data is provided by PO, not by product, you need to break down each order further into all its line items. Only then can you recompute On Time and In Full for every SKU, track it over time and bring the most problematic products and locations to light.
What Walmart sees is only half the picture. You know everything about the destination, but what about the origin? And the route in between? Perhaps the problem isn’t with any particular products, but with where shipments are going out from or who is delivering them.
To identify these issues, you need internal data, such as:
Any one of these could be a problem – a warehouse located in an area where weather regularly delays shipments, a carrier whose trucks regularly arrive later than promised, a discrepancy between planned and actual shipment dates, orders received on a Friday, etc.
All this data should be available in your ERP system or from your 3PL. But again, having the data doesn’t mean it’s a quick or easy analysis. Because of the one-to-many nature of POs to shipments, you have to map the two together. Additional data coding or translation is typically needed too, such as turning a specific date into a day of the week or Walmart calendar week.
Then you can compute On Time and In Full, but you need to know which factors to do it for. Otherwise, you can spend hours doing analysis by carrier and find it yields no meaningful conclusions because there’s actually no problems with any of them.
The most actionable insights come from combining the two data sources. Say you’ve identified some products that are problems and some originating warehouses that are having trouble. Which one should you tackle first? You need to understand them relative to each other to prioritize correctly.
Furthermore, is there a relationship between the issues you’ve seen? Perhaps certain warehouses look like they’re having problems only because they’re the ones that ship the problem products. And if you can narrow down the problem even further to specific product-location combinations, your response can be even more precise and efficient.
Connecting the Walmart data with your ERP and 3PL data can really enrich your analysis, though it requires even more data manipulation. You’ll likely need to translate between different product names, units of measure, location IDs, etc. to truly integrate the data sets. Then consider all the product-location combinations for which you have to calculate metrics! The time consuming nature of all this work is where automation should come in, enabling your team to focus on addressing the root cause, not just searching for it.
However it’s done, connecting all these dots gives you incredible flexibility to do whatever analyses you want – even going beyond pinpointing the root cause of poor performance. For example, we work with a customer who compares Walmart’s reported arrival date/time with the arrival date/time they get from their 3PL or their own trucks. When they notice a discrepancy, they use it to contest Walmart’s fines.
Talk to us today to learn about the many types of analyses suppliers are conducting to improve customer service, reduce fines and increase efficiency. When you always have integrated, harmonized data ready for use, you can continually optimize your operations and stand out as a strong, proactive partner to customers.
The global confectioner mitigates waste, improves service levels and controls costs by connecting digital supply chain visibility with POS analytics.
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