GAINS On Video: MRO & Service Parts Planning Part Two: Using AI to Manage Complexity
In this episode of GAINS On, join GAINS Co-founder Bill Benton as he describes the complexities of MRO and Service Parts planning, and how GAINS uses AI tools to manage this complexity.
(00:00): Hi, my name’s Bill Benton, I’m the Co-founder of GAINSystems. Today I’ll be talking about MRO and service parts planning in this version of GAINS On.
(00:30): Each decision that GAINS doesn’t automate and presents to the Expert Planner is assessed from two points of view. One is, how much risk does this action pose to our ability to fulfill service and what risk does it pose financially? And so when you look at that, it’s looking at our overall goals and how much just one element would contribute to either reducing that risk or avoiding it, and ranks those things accordingly. So if you have low cost, low risk decision, those all get automated. We look at automation of different supply options, not like a traditional buy-from-a-vendor, receive, distribute, sell. In this case, there’s a lot of redistribution that’s available. You can use fill in alternative supply, you can repair, you can upgrade, you can use alternate parts. All these things are usually a manual decision, which adds a ton of complexity. GAINS will provide an optimal and feasible suggestion so that you need not review those all, although you always have the opportunity.
(01:37): What GAINS does is it records each change of the recommendations GAINS makes, and learns from that to determine what factors are correlated with one of those decisions. And here we have a crawl, walk, run approach because understanding why a decision was made, after say a negative event occurred, and trying to understand what you could have known beforehand or why the decision was made, we look at first saying, all right, what are the things you’re likely to reject, right? So that’s lower risk cuz you can always end approve it the next day, but that might eliminate 50% of the workload off the bat.
(02:36): Moving from preventive to predictive maintenance after a certain number of hours of use or miles driven, certain components of a machine are gonna be broken down and have a series of parts replaced, without regard to whether those parts might have some meaningful lifespan left. But predictive maintenance does actually measures the likely lifespan of those key parts to do two things: One is prevent scrapping parts that might be good until the next cycle. Secondly, determining those parts that might break before the prevent cycle and avoid an unscheduled maintenance event. In reality, not all parts are gonna have sensor data to do predictive maintenance, but what we can do is really tighten up those reliability estimates so that that estimate can enhance the model in a much more accurate fashion than today.
(03:30): Key performance indicators apply such as inventory turns, line fill rate, part availability, et cetera. But beyond that, there are special metrics that need to both drive the model and be measured and improved upon. First is something similar to First Pass Resolution Rate. They’re going to make the repair and they can make that repair upon diagnosis. So when they look at it, geez, I have the parts available, I’m gonna replace parts A, B, and C to not only diagnose what I need but to actually execute that, and that might dramatically reduce the cost of that. Similarly, when you think about shipping parts, so if you’re either a distributor or a retailer, splitting shipments is quite expensive. So if you have to send two parcels rather than one, that’s sort of the rough equivalent of a first pass resolution, right? Do I have everything I need, in the place I need, at the moment of a request to fulfill that. Turnaround time of repair or T&T is a huge metric and achieving that or reducing that has myriad benefits. Last is looking at outer ground or expedite. Some of these things are highly costly that either erode your budget to fulfill your service goals internally, or dramatically reduce your profit margin. So these are other types of measures of effectiveness that are not only measured by GAINS, but built into the model to help optimize excellence.
(05:13): A high number of SKUs and or SKU locations, that complexity is key. High cost of stockout and/or downtime, or very high amounts of working capital, or parts inventory investment. If either or both of those are the case, usually that’s because you’re trying to buffer a large amount of uncertainty and/or very high service expectations. And that usually means there’s, there’s room for improvement without cutting any muscle. So the second aspect we focus on is from an industry perspective, if you’re an OEM, you manufacture machines that are used out in the field and you have long-lived machinery and you need to provide spares over that entire lifespan, including the warranty period and end of life. If you’re a spare parts distributor and you’re selling spare parts, not necessarily to consumers or retail, but business to business, those are very good fits. And lastly is fleet operators that are servicing essential equipment. So that ranges from airlines to oil field companies and mining equipment operators. These are the two sort of areas where we think we can help. Appreciate your time.