Getting the foundation right: A better future for product data verification
Adrian Kelly
"Data question" can be an all-too-familiar subject line in emails for Ag retailers, distributors, and manufacturers this time of year.
Missing data. Repacks, and the ensuing data confusion. Incorrect product names or specifications (like size or amount). Last-minute scrambles to clean up the numbers at the end of a rebate season. Transaction data that builds off inaccurate product data — so then you end up with something that’s doubly wrong.
Meanwhile, the people tasked with fixing all these errors are stressed out. They’re not sure where a piece of data started to go wrong, or how far back the inaccuracy goes. There are no easy communication methods to verify data or correct an error.
Unfortunately, the Ag input supply chain has no shortage of these situations. It’s one of the industry’s biggest problems.
But… deep breath. These headaches — longstanding as they are — don’t have to be the future.
Solutions are available to take us into a better, more accurate era of Ag input product data.
The status quo: what’s wrong with product data verification in the Ag input industry?
There’s no shortage of data tied back to individual products. SKU, package list, pallet configuration, unit of measure, the list goes on…
So you can imagine in its current state, the process of moving product data up and down the supply chain is plagued by a variety of problems. Here are the core four issues:
Siloed business processes
At the manufacturer level, there are often just way too many systems in place to maintain order. For example, product data might migrate back and forth among sales, marketing, regulatory, supply chain management, or more. Each of those departments has their own processes and standards for data handling.
Each time data bounces from one system to another, there’s an increased risk of mistakes. Plus, the likelihood is high that at various stages throughout that journey, someone is extracting data (via CSV, Excel file, etc), then passing that version onward. It gets messy fast.
Take this scenario for example: If a product has over 150 attributes and that data is moving through 5 different department systems across 15 different individuals, there is suddenly over 11,000 data touches that could lead to errors.
Branching of product information
Let’s say you’re a supplier that sells a bulk tank of ‘chemical X’. You know how many of these tanks you’ve sold and to which distributors. Simple, right?
That is, until the end of the rebate season arrives and ‘repackage surprises’ start creeping into your reconciliation process.
Suddenly you’ve got data floating upstream about how many 50-gallon drums of that product have been sold this season. “We don’t even sell a 50-gallon drum,” you say, scratching your head.
Well, now you do. That bulk tank that went to the distributor was repackaged as a 330-gallon tote. From there, a retailer repacked it again to sell as 50-gallon drums to their grower customers.
It’s not uncommon for product to be repackaged up to three times by the time it hits the farmgate. And that’s not even counting the unapproved “stealth repacks” that add another level of headache.
Now, you’re staring at confusing, splintered product data, and rebate payments just got a lot more convoluted. The manual work it takes to reconfigure the numbers is a time suck — or a money suck, if you hire a third-party company to come in and fix it.
Information systems disadvantage for smaller suppliers
Measured by sales dollars, the handful of largest suppliers in the country do make up the majority of the Ag input market. Those organizations typically have access to more resources, more robust systems, etc to ensure higher quality (and frequently updated) data.
But there are still plenty of smaller manufacturers in the market who don’t have access to the same resources (financial or human). Not to mention the thousands of smaller distributors and retailers in the supply chain.
As a smaller manufacturer, your products might not move at the same volume as larger agribusinesses. Unfortunately, that can lead to getting less attention into (and from) the channel and increasing the probability of inaccurate product data.
But regardless if you’re a large or small player in the industry, the information system gap is real, and it hurts data health from top to bottom of the Ag input supply chain.
Reliance on individual crusaders
Quality data in the Ag input supply chain is not systemized. Too often, data health is almost entirely dependent on individual data crusaders.
When you don’t have someone at your organization invested in accurate data and regular upkeep, your data suffers.
Even when you do have someone like this on staff, it’s a short-term solution. What happens if that person retires or quits? Your commitment to data health is locked into their brain — and you have to start from square one when they leave.
This issue becomes apparent in many of the industry’s legacy product information databases. It’s not unusual to encounter outdated product data that is time-stamped just before ‘Sally’ or ‘Bob,’ the 30-year veteran data crusaders, departed, taking all their extensive knowledge with them.
Data health crusaders don’t need superhero capes. But they do need tools and software in place that help them advance the cause of clean product data.
What’s the cost of continuing down this path?
In one word? Chaos.
When data gets splintered, when numbers are inputted incorrectly, or when employees fail to frequently update product data, you get massive confusion.
No one knows what’s correct, and it takes time (and sometimes money) to untangle the knots.
Wherever you’re positioned along this supply chain, bad data will eventually come back to bite you. For manufacturers, this looks like paying high hourly outsourcing fees to rebate processing companies who fix inaccurate retailer transaction data. On average, manufacturers pay anywhere from $500,000 to $2 million per year on internal labor and outsourcing costs for this specific problem. For some of the largest suppliers, that number can stretch to $5 million.
But retailers and distributors suffer too. According to our research, when third-party processing companies get hired by manufacturers to “clean” product data, retailer and distributors spend over $200,000 per branch in labor costs to answer questions or track down information for them.
Manufacturers might shoulder the financial burden of fixing inaccurate data along the supply chain when the end of rebate season arrives. But everyone suffers a high opportunity cost.
What a better future state could look like
The good news is, just because this is the way things have always been done doesn’t mean it’s the way it has to continue.
The industry needs better communication among all the disparate parties — and platforms to actually support that connection. It needs robust software and digital tools that can automate tasks and reduce administrative burden. And it needs a central “middle ground” where everyone can go to see what’s right — rather than digging through multiple systems or outdated spreadsheets in their inbox.
Technology that empowers smoother business systems and more accurate product data can reduce the noise that makes the value chain so jumbled today.
Smartwyre’s product verification platform was built around these exact problems
Smartwyre’s item master / supplier verification management platform acts as connective tissue between the organization that needs data verification and the organization that has that information within the value chain. Here’s an example of how it works:
A distributor or retailer flags that a piece of information is missing.
A request travels to the supplier that has that data.
That organization routes the request to the individual within the company who has the answer.
That person updates the data in a common, industry-wide format.
Corrected data travels back to the retailer or distributor.
Verification is simple and fast — which fixes short-term confusion. But this process also improves data health in the long run.
Operating within today’s status quo, it’s easy for someone to just input an incorrect piece of data into their system. Often, it’s all they have — and no easy way to verify or correct it.
But those little mistakes pile up quickly. Bad product data snowballs throughout a season, or even into the next year. And the next. And the next. Getting data quickly corrected can prevent a lot of headaches.
When various stakeholders can send simple validation requests, data gets healthier — and everybody wins. Retailers and distributors get standardized product information they can rely on. Suppliers can avoid the costs of hiring fixers to chase down the correct data.
Meanwhile, everyone reduces their admin load, especially at the end of the year. And that’s a win we can all get behind.
To learn more about Smartwyre’s supplier verification management tool, Engage, reach out to our team today.
