Ever pack the car for a trip and find the perfect placement for every bag? Or maybe you have a favorite suitcase with a pocket for everything? Maybe you’re like me and just sit on the suitcase till you can get the zipper all the way around.

Either way, it’s no surprise to anyone that preparation, planning, and a little bit of organization can make a trip that less stressful and provide more opportunities for living in the moment. Whether you’re packing a suitcase, a trunk, or a camping backpack efficiency can mean the difference between grabbing your camera to capture the perfect moment or missing an opportunity. You might think this is a small price to pay but what happens when the same concept is applied to business? Or to an organization managing millions of dollars?

In that scenario, maybe those opportunities aren’t capturing the perfect moment. Maybe its gaining wallet share as a customer takes on a big project. Maybe it’s winning a bid that establishes your company as the industry’s gold standard. What happens when these opportunities are missed and how can you prepare your team to recognize opportunities such as these? It all starts with having your data ready to go.

Jonathan Bein, a Managing Partner at Distribution Strategy Group, discusses with Benjamin Cohen, Founder of Proton.ai, 4 use cases for AI in distribution that can prepare companies for these unexpected opportunities. Jonathan provides great insight into how optimization can be a bit more complicated than industry knowledge and good judgement,

“ You may remember the term factorial. So if I want to put 3 items in the trunk, and I want to do it perfectly and use the least amount of space possible, there’s 6 different ways I can do it with 3 items. When I get to 4 items, there’s twenty-four ways I can do it. When I get to 5, there’s 120. When I get to 10, there’s 3.6 million”

So as the number of items increases, the complexity of optimization increases exponentially. Just from adding 7 items to the trunk the number of options went from 6 to 3.6 million. And that only accounts for 10 items. What does this mean for a company with hundreds or thousands of items? And this only accounts for 1 variable, being space. What if one of those items was a cooler? You might want to keep that accessible. What if something is fragile and shouldn’t go on the bottom? No one has time or the attention span to analyze 3.6 million different options. But that doesn’t mean that optimization insights are beyond the scope of a modern business.

Using AI, companies can gain insights powered by custom algorithms. These algorithms can analyze the enormous amount of data hosted in product catalogs, inventory tables, and data lakes to discover patterns in customer behavior, material selection, and more. Say a request for quote (RFQ) has just come in from a high value customer. The RFQ has 1,000 line items each with unique quantities, units of measure, and contract discounts. Maybe some of those items are individually measured to order, like carpet that is ordered and cut to a customized size. To create the quote, a Customer Service Representative must find all 1,000 items and calculate the cost to best service this high value customer. Imagine the stress if not all these items are easily identified. What if the Rep accidently quotes the wrong material and the customer gets and inaccurately expensive quote? Who wouldn’t blame them for working with a competitor?

Further adding to this stress is the common practice of submitting multiple RFQs to multiple distributors. Now, in addition to accuracy, speed is imperative. Especially considering 90% of customers will accept the first bid received. Using AI, an algorithm can analyze the individual items in the order to identify and calculate the requested materials. Using algorithms to determine relevance, AI can instantly provide recommendations to the Service Rep who can quickly choose the matching item and complete the quote in a matter of minutes. Not only does this allow the Rep to spend more time focused on the customer but the AI tool can learn additional patterns from reinforcement learning. Further improving its performance in the future.

Using these algorithms, machine learning can optimize an organization’s data to better prepare for unexpected opportunities. Whether interested in improving customer service, employee experience, or ROI, machine learning and the use of Artificial Intelligence is a way to exponentially improve your business processes through optimizing data.

Visit us at DataXstream IA to learn more about how you can optimize your data and pack your trunk for unexpected opportunities. As SAP Gold Partners, DataXstream is committed to providing future proof solutions on emerging technologies for SAP users in sales and distribution. Our mission is to help our customers be more successful at leveraging their SAP ERP infrastructure and realize a better return on investment. We strive each day and with each customer to be a world-class provider of enterprise solutions for sales and distribution, using proven and emerging technologies architected and deployed by seasoned professionals of the highest degree of integrity and skill.