Artificial Intelligence (AI) is not magic. From my perspective, in the context of distributors, AI is a combination of software and data to solve, and prevent, problems. The “software” is usually a distributor’s ERP system, modified to include AI logic / formulas, and potentially interacting with CRM and e-commerce systems, and using “data.”

The data is a repository of information that can originate in the ERP database, from an AI request, perhaps from text messaging, emails, possibly enhanced via an Internet search and maybe other sources.

So what is AI? Software plus data. Simply put, AI is machine “learning.”


Here are two examples where AI could help improve accuracy and efficiency of your warehouse. Notice that AI combines data (“synthesizes”), makes a decision (“interprets”) and makes a recommendation (“projects”).

  1. Accuracy
    Distributors and wholesalers want “high” warehouse accuracy, defined here as 1 minus (the number of mistakes in a week divided by the number of lines picked and packed in that week). But ’'it’s almost impossible to achieve 100% accuracy because the cost would be very high and some preventative measures would not be practical. So distributors would like to know what level of accuracy they should try to achieve. AI might recommend a target accuracy level by using information found on the Internet or based upon a formula the company develops based upon historical data. Using picking as an example, for each item and pack size in the warehouse (e.g., ½” copper EL), software would search the Net for reported levels of accuracy. If enough data for an item and pack size is found, the software would calculate an average accuracy level and average variation from the average accuracy level. For a pre-selected product group or sub-group or vendor, if a suitable level is calculated and the variation is acceptable, AI would display the calculated average and any actual service level calculated by the ERP software, and the difference.
  2. Productivity
    Determining if a worker is as productive as desired requires standards (e.g., pieces/cartons per hour) for each job (e.g., picking) and shift. And it requires captured data for each worker, such as date, clock-times in and out, and cartons or pieces picked; all of which are captured via bar code scanning. Manually calculating those standards is time consuming, but AI might determine those standards in seconds. In this example, for each shift, AI- enabled software would search the Internetfor reported picking standards. If enough data for a shift is found, software would calculate an average picking rate and average variation from the average picking rate. If a suitable rate is calculated and the variation is acceptable, AI would display the calculated average rate—the standard. (Standards by type of item, e.g., line sets, are too difficult to obtain from the Net because too many warehouse-specific factors affect that level of standards). AI could also display, for each worker and shift within a pre-specified date range, the actual pick rate, and the difference from the standard (amount and percent). A manager would decide whether or not to store an AI-calculated rate, by shift; or store them all.

To see that picking standards really are on the Internet go to, and in the search box key in “warehouse picking standards.” Scroll until you find “strategosinc” and click on it; then select “NS 529 standards.” The security of NS 529 Standards is unknown, so you click on “free download” at your own risk. You also take a risk if you enter your email address and key in the security code displayed in the lower left-hand corner. If you do proceed, retrieve the email and click on the link to download the information.

AI in manufacturing and supply chain are going to be very impactful in ways we don’t yet know, but it will happen quickly.

— Wade Tennant, Director of Marketing, Legend Valve


AI is always off for most distributors, so until AI becomes a reality here are a few tips for achieving high warehouse accuracy.

  • Organization: Store items picked the most often closest to the packing area, and even where items are stored by “family” or vendor line, store the fastest moving ones closer to the front of the section.
  • Receiving: If the unit of measure displayed in PO/put away data is not the same as that on the corresponding packing list, the receiver should note that discrepancy on the packing list or record it via a scanner.
  • Put Away: If there is no permanently-assigned storage location for a newly-received item, or an alternate location is used, the person doing put away must record the selected location ASAP.
  • Pull Down: The time to replenish picking locations from bulk/overflow is before daily picking begins, regardless of whether someone is using a printed pull-down list or displays data on a bar code scanner. Pulling down and picking at the same time leads to mistakes.
  • Picking: To minimize picking time, items must be picked in a sequence that minimizes walking time. Rushing to make up for long travel distances causes mistakes.
  • Packing/QC: To avoid repeating mistakes already made, an order checker should not be the same person who picked the order being checked.
  • Loading - To reduce mistakes, the smaller and lighter items and packed cartons of an order could be placed on rolling shelves that are used only for staging outbound orders. Each rolling shelf can be pushed into or near the appropriate truck.


With the above being said, AI is a rapidly growing field with the potential to revolutionize many industries, and warehouse operations are no exception. In recent years, AI has been making its way into the world of logistics and supply chain management, bringing with it a wide range of benefits that can help companies to optimize their operations, increase efficiency, reduce costs and improve customer satisfaction.

Here are some ways some PHCP manufacturers are already seeing AI and automation solve pain points for manufacturers and distributors.

Wade Tennant, director of marketing at Legend Valve says AI can help with accuracy in order processing, reducing wear and tear on employees, freeing up time to work on more creative tasks and project work, reducing time during the onboarding process and it can help streamline hiring staff in some cases.

James (Phong) Kue, director of manufacturing engineering at InSinkErator agrees, adding some other tasks where AI is helping manufacturers.

“Some key pain points AI has resolved for us include: improving safety and reducing ergonomically straining tasks/process, improving product consistency and improving our manufacturing production rate,” he says. “Enhanced automation has allowed us to utilize robots for jobs that involve repetitive motion and could be physically straining if done incorrectly. An example is loading and unloading heavy machine parts and components during the production and sub assembly process.”

While AI and automation are helping in many ways, as with any new technology, there are concerns and hesitations to address.

Kue says common concerns are over-complication of processes and the sustainability of the new technology. “These concerns each require careful planning, communication and execution to successfully manage implementation of new technologies,” he adds. “Our decisions to integrate technology have ultimately benefited our business and are aligned with our goal of being the premier manufacturer of Residential and Commercial Food Waste Disposals.”

Tennant predicts that adoption of AI throughout the PHCP-PVF supply chain will be slower than some industries, but overall it will continue to grow in the industry. His advice? Take action sooner rather than later.

“Make sure to do your due-diligence and work with the right partner/vendor which will assist in alleviating the pain points you have,” he says. “Don’t wait — take action now. Price points have reduced such that ROIs are more easily achievable than previously. Take small steps to get momentum with automation and build from that...waiting a year to develop the perfect solution means you’re taking too long and the game will have changed by the time you’re ready to implement.”

Another common concern when talking about AI is whether or not it is a threat to employment and the workforce.

Both Tennant and Kue agree than in the immediate future, AI will only enhance employee capabilities, not replace them.

“We believe the proper roll out and positioning of systems and technology enhance our employees’ capabilities. In fact the enhancement means growth and thus adding additional employees,” Tennant explains. “AI has the potential to remove tasks and free up time for more valuable work from employees; think of AI as a smart assistant who can get some complex things done very quickly but still needs oversight, editing and fact checking.”

Kue agrees, pointing out that using AI can free up valuable employee time. “We view our team members as one of our most valuable resources. AI allows us to employ our labor resources in the most valuable manner while using technology to execute specific and often repetitive tasks in our operations,” he says. “Long term, there exists the opportunity to elevate the level of the work our team members execute.”

Although it seems like AI and automation technology have grown light years just in the past decade, we are still in the early adoption stage. Tennant predicts that AI will adopt in specific tasks much quicker than others.

“AI is going to continue to grow over the next three to five years, but still early in the adoption cycle. They are likely to grow quicker in specific areas of the business, like the front office,” he says. “Overall, AI in manufacturing and supply chain are going to be very impactful in ways we don’t yet know, but it will happen quickly.”

Kue adds that while AI is helpful, it has introduced complexities. “We anticipate these technologies to continue to advance and be made more intuitive to manage and control. While these systems have solved many manufacturing problems, they have also introduced complexities. The need to reduce complexity and still accomplish productivity safely and with high quality is a problem that someone need to solve, which is a huge opportunity,” he says. “The resources and talent pool cannot continue to support complex technologies that can only be maintained by complex thinkers. We believe that in 3-5 years, the most successful Manufacturers will be the ones that can overcome the shortage of these Controls and Engineering Resources through creative ways.”