The technology topic of the year in distribution has been, and will continue to be, AI.
In reality, much of AI is really ML (machine learning) but it’s not as “sexy” to talk about data being built into algorithms, to mine data and then deliver information, insights and/or potentially predict (or recommend) actions. The difference between AI and ML, from a theorist’s viewpoint, is that AI “machines / systems” are sentient beings, but let’s stick with the current nomenclature of AI for now.
The key, however, is thinking how today’s AI could help your business.
It’s in the early stages but rapidly evolving. This is a challenge for many distributors and manufacturers who are not used to technology changes occurring rapidly … and implementing them rapidly. It requires a different skill set for business leaders and technology leaders.
One person who knows distribution and warehousing, which is the essence of distribution and a key component of manufacturing, is Dick Friedman. Dick is the principal of General Business Consultants and has been working with distributors in helping them optimize warehouse productivity for many years. Since the beginning of the year, maybe earlier, he’s been thinking of how AI will impact warehouse operations and how those who stock, pick, pack and deliver could benefit from AI … or should start thinking about. He shared his thoughts:
Artificial Intelligence (AI): Could it Help Your Warehouse?
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.”
Let’s talk AI in the Warehouse
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”).
- 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 its 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 an algorithmic formula the company develops based upon historical data and other acquired insights.
- Using picking as an example, for each item and pack size in the warehouse, software would search the Net, or another benchmark based upon a formulaic approacfor reported, or desired, accuracy levels. 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.
- 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 Internet, or compare versus company / industry / peer group standards, for 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.
To see that picking standards really are on the Internet you can go purchase the Warehouse Modernization & Layout Planning Guide which was developed by the US Navy (although) in 1985 for only $16 and/or download, for free, standard time data for pallet storage, binnable picking and rackable item picking. Yes, these may be dated, however, it shows that there may be resources available that can be integrated into models to help you improve your business … and NAED, or marketing groups, could commission similar research to assist their members.
Improving Operational Efficiency – What to Do Today.
AI is aways off for most distributors, so until AI becomes a reality here are a few tips for improving your 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.
Dick Friedman helps distributors prevent warehouse mistakes that lose sales and customers; and helps determine if warehouse automation would be practical AND cost justified. Contact him at 224 723 5143 or via www.GenBusCon.com for a FREE consultation or more information.
Thoughts
The topic of AI gets one thinking of the areas where it can impact the business. Microsoft, as evidenced by its investment in OpenAI and launch of ChatGPT, is thinking much about it. They are regularly launching new functionality into its Office offering (especially in Excel and PowerPoint), and is adding much to its CoPilot offering which will bring interesting functionality to CRM systems.
NAED held one of its Futures Group sessions last week on AI, which generated a spirited discussion (and if there is a link made available to attendees, I’ll post it here.)
During the session, Steven Levy from Infor shared some of the things they are working on and then offered a tool to help distributors enhance and enrich their item data via an experimental webservice. It appears that the offer is free and is open to distributors regardless of the ERP system you have (and if you have issues with the link to the LinkedIn posting, email me and I’ll connect you with him.)
At the end of the day, every distributor has the capability to use AI. Whether you have talented individuals within your technology group or will outsource development (and connect with the myriad of applications that are becoming available), it all starts with data. The data can be customer information, sales / ordering information, product data and can be augmented by other types of information (weather?, distance?) much is dependent upon having the information, in a good structure (and AD, at last week’s HR, Finance and Technology Summit reportedly had a good presentation on data structure conducted by someone from Amazon Web Services.)
While functionally data management is an IT / digital initiative, it really needs to be something that senior management needs to understand, envision and think what it wants as deliverables. Here’s a good article in CIO Journal from Deloitte titled “Data, Analytics, and Your AI Strategy, to add to the discussion.
Getting data “right” is key to leveraging systems (and AI) to generate business outcomes that benefit customers, associates, suppliers and your top and bottom line.
