From The Editor | July 26, 2016

How Does Data Quality Influence Food Manufacturing Supply Chain Operations?

Source: Food Online
Sam Lewis

By Sam Lewis, associate editor
Follow Me On Twitter @SamIAmOnFood

How Does Data Quality Influence Food Manufacturing Supply Chain Operations?

As food manufacturers’ supply chains continue to expand, so does the importance of recording, managing, and using good data in daily operations. Here, Patrick Taba, supply chain product specialist with Storck USA, answers my questions about how bad data can affect a food manufacturer’s daily operation, why accurate measurements and data are essential, and how food companies can begin implementing a successful  Data Quality (DQ) program.

Food Online: What negative effects can bad data have on a food manufacturer’s daily operations?

Taba: The effects of bad data can impact several aspects of a food manufacturer’s daily operations. There are many stakeholders within the supply chain that rely on our data to be accurate, especially in regard to the physical measurements — dimensions, weight, etc. — of food products. If a food manufacturer understates the gross weight of a product, this may cause the company to unknowingly exceed any weight capacity thresholds that may exist. Shipping companies may reject the load because it is overweight or the container may incur a fine if the shipment is inspected at a weigh station and discovered to be over the threshold. These delays and fines can ultimately lead to lost sales.

Administrative costs may also increase as order entries or invoicing may constantly have to be revised to correct any mistakes due to incorrect data. Bad data can also affect a final customer or end user, as they may have expected a product to have certain attributes based on what a website or catalogue had stated. If the products do not meet customer expectations, it may lead to multiple returns or even lost opportunities for future sales.

Food Online: Conversely, what advantages can be gained from obtaining and utilizing good data?

Taba: There is much to be gained when a food manufacturer maintains a high level of data integrity. A company that can accurately state its product data can also confidently optimize how the product flows from end-to-end of the supply chain. For example, if the dimensions of a case are accurately defined, a company can maximize the quantity of this case for a pallet configuration, which, in turn, would allow for increased container utilization during shipment. Accurate weight measurements would ensure that container loads would not be overweight.

Ordering and invoicing would be much more efficient without the need to make any error corrections and products would be available in the marketplace in a minimal amount of time. Customers may also benefit by receiving exactly what they were expecting which builds trust and can also create repeat sales.

Food Online: Can you summarize the effects of a measurement that is even slightly inaccurate? How does this impact transportation costs? How does it impact warehouse costs?

Taba: There are several attribute measurements that we deal with on a daily basis which may include everything from inches, to pounds, to volume. It is very easy to resort to rounding measurements to the closest whole number, or even utilizing measuring tools that do not have the precision required for an absolutely accurate measurement. The importance of data accuracy can be expressed by illustrating the impact of being off by just a quarter of an inch. A case that may have its height overstated by just a quarter of an inch may not sound like much of an error, but its impact can be quite significant.

Let’s say we’ve overstated the case height at five and one-quarter inches, and it is currently on a pallet that has 20 cases per layer. With an industry standard pallet height threshold of 56 inches (which includes a six-inch wooden pallet underneath), we would only be able to go nine layers high (for a full pallet quantity of 180 cases). Now let’s say that the actual case height is only five inches. This would mean we could have optimized the pallet to go 10 layers high instead of only nine, which would add an additional 20 cases per pallet (for a full pallet quantity of 200 cases).

As we optimize the pallet quantity, it impacts transportation costs because we could fit more cases on a container which would reduce the amount of containers needed to ship. It would also impact warehouse costs because we could store more cases by utilizing less pallet locations (as many warehouses charge storage fees per pallet location).

Food Online: What resources are available to help companies understand data quality and make a case for starting an initiative within their own organization?

Taba: We work with GS1 US through their National Data Quality program and their website has a variety of resources available for those getting started with their data quality programs. I recently worked with a group of industry leaders dedicated to creating ROI calculators, which help data managers justify the resources needed to address data quality issues within their organization.

There are three versions of the ROI calculators — one focused on transportation costs and case dimensions, one focused on warehouse costs and case dimensions, and a third focused on transportation costs and case weight. They each offer step-by-step worksheets which reflect the guidance given to companies participating in the GS1 US National Data Quality Program but on a smaller scale. Using one of two key pieces of data, companies can illustrate the cost of inaccuracies, and the benefit to be gained by resolving them.

About Patrick Taba
Patrick TabaPatrick Taba is a Supply Chain Product Specialist with Storck USA. He has 15 years of experience working for a variety of consumer packaged goods companies in both marketing and supply chain capacities. His current role includes new item set-up, master data management, and data synchronization. He is also an active member of the GS1 US Data Quality Discussion group.