In these episodes of GAINS On, join GAINS Co-founder Bill Benton as he describes lead time sensing and prediction, empowered by machine learning through GAINS. We know there’s a lot of noise around AI and ML, everything from chatbots to social media ads, but what we focus on here is pragmatic ML and AI that can be operationalized to be resilient across teams and time, and has built-in bidirectional plumbing. So we’re excited to bring this.
(00:05): Hi, my name’s Bill Benton. I’m the co-founder here at GAINSystems. I’m excited to talk to you today in this GAINS On segment regarding lead time sensing and prediction, empowered by machine learning through GAINS.
Lead time sensing or prediction is the estimation of the time from which somebody decides they need something to the time they receive it. Generally, these have been maintained poorly and infrequently sometimes set and then rarely touched. What we’ve learned through the pandemic and following is that these can be highly volatile. And if you were to look at any one parameter driving performance, this might be the single most important one.
(01:23): You set it and forget it, right? And then the user manually adjusts with each order based on their own intuition of what the current status is. So that’s bad. Less bad would be that you use some dynamic broad brush parameters like, from this production center, I’m gonna expect this, or this vendor, I’m gonna expect this. When in fact, there’s a lot of variance from those, but at least those get updated and if say, a routing from one continent to another through a port, and you can recognize that that’s happening faster or slower. Good is using real data, observed data to calculate what the trend in historical lead time has been at a part location, ideally, or if there’s insufficient data, slightly higher level like part family or vendor.
(02:21): So let’s assume you’re already at the good level and you are looking backwards. Well you really miss inflection points that way. So for example, as peak stress in the pandemic was observed, you would’ve assumed looking at history that the lead times would continue increasing. And once some of those capacity constraints were resolved, you actually saw lead times declining. And that was very hard to see upfront. That surge was not just caused by overestimating demand, but overestimating supply time. So you’re buying in advance when it wasn’t necessary. So what we’ve done is we’ve used machine learning methods and really focusing on what’s ready, readily available to predict what the next lead time will be. And that uses data ranging from simple things like how many delayed orders exist from this supply source at that moment, and how does that compare to how many existed before, and how did those things correlate with lead time? Also looks at location. At minimum it looks at over 500 different potential correlates to determine which ones predict the next lead time well, not just measure the last one.
So if you’re trying to buffer across a one-week lead time plus or minus four days, that’s a very different question than if you’re trying to buffer across a one quarter or a half a year long lead time. Merely getting that input correctly defined can reduce the amount of inventory you need significantly, or conversely, ensure that it’s sufficient to meet the needs. So if you’re understating lead time, that’s very likely to lead stockouts and material problems. And if you’re overstating it, you’re gonna be tying up working capital.
(04:22): Accurate, available to promise: If you’re manufacturing, people are asking you when things can be provided. It can help with long-term planning in terms of vendor collaboration or supplier collaboration so that that’s aligned. It can help prioritize work in terms of expediting and de-expediting. So there’s, there’s many factors at play, and again, if you were to think of one parameter that might touch on 15 or 20 key elements of how much work it is, how effective you are, and how accurately you can predict, lead time is one of those key levers.
Thank you for your time today. We know there’s a lot of noise around AI and ML, everything from chatbots to social media ads, but what we focus on here is pragmatic ML and AI that can be operationalized to be resilient across teams and time, and has built-in bidirectional plumbing. So we’re excited to bring this. We think it’s highly useful and easily implemented. Thank you.