In today's discussion on small manufacturer technology adoption, Sarah and Lindsay focus on practical steps. While envisioning the destination is straightforward, understanding the starting point and necessary actions is crucial. Small companies typically aim for improved visibility, cost reduction through task automation, and faster decision-making via data accessibility. The initial implementation often begins with a proof-of-concept. Overlooked considerations include legacy network issues and data field cleanliness. Addressing these challenges involves connecting, communicating, and gathering data. Convincing stakeholders to support these efforts is essential, as data extraction can be costly and time-consuming. Benchmarks, not estimates, should guide the process.
In today's discussion on small manufacturer technology adoption, Sarah and Lindsay focus on practical steps. While envisioning the destination is straightforward, understanding the starting point and necessary actions is crucial. Small companies typically aim for improved visibility, cost reduction through task automation, and faster decision-making via data accessibility. The initial implementation often begins with a proof-of-concept. Overlooked considerations include legacy network issues and data field cleanliness. Addressing these challenges involves connecting, communicating, and gathering data. Convincing stakeholders to support these efforts is essential, as data extraction can be costly and time-consuming. Benchmarks, not estimates, should guide the process.
Happy Monday! Welcome to our weekly news show where Lindsay and I come together and talk about a topic that we feel is relevant to small and midsize manufacturers. I am joining you from Austin today; the weather is incredible. It was in the 70s, AKA less than triple digits, which is amazing, and I can go outside, so my tan may be looking pretty sharp here in the next few weeks.
So, Lindsay and I decided that a topic that I feel is really challenging and important to people who are, in particular, working for small manufacturers is around technology adoption. So, lots that we're going to have Lindsay dive into today as experience with this from, you know, ERP to MRP to all the different types of bolt-on solutions that are in the market, figuring out what technology do I even need, why do I need it, and then how do I get buy-in and manage the implementation and getting budget for it, which is sometimes very, very challenging.
So, Lindsay, let's have you start off with kind of the three areas that the desired direction kind of falls into, and then we'll walk through some of the different steps that you recommend people take as they're figuring out their technology roadmap and adoption. Thank you.
Yeah, the three areas are an easy part of the conversation, Sarah, because this is a conversation that gets complicated and convoluted very quickly based on each situation. So, the three areas: understand exactly where we're going. And not just as a general statement, you know, probably have an actual problem statement document that everyone nods and agrees accurately captures the intent.
And then decide what it's going to look like in terms of the outcome. Is the outcome going to be improved machine monitoring on the production line? Is it going to be a dashboard that people can look at and summarize what's going on rather than have to default to managing by walking around? Is it going to be a single pane of glass that either pulls together disparate remote sites or even just different databases? Right here's what my ERP says, here's what my MES says, here's where I'm at with today's orders. Or am I going to go for the full enchilada and try and do Trend review, ongoing real-time Trend review that says, you know, here's my 90-day baseline, here's my 5-day baseline, here's my goal, and here's where I am. That requires some work. That requires more AI-involved work.
And then exactly what am I going to need to get there, and that's where we start to get complicated. Am I going to need equipment? Am I going to need an edge computer? Am I going to need sensors? Or am I going to need, you know, what data fields am I going to need and where are they going to come from?
So, as soon as we start to see that complexion, the complexity of the obvious first step is, "Okay, let's take a strategic timeout and say, 'Alright, we need to approach this with the right demeanor, the right disposition, and the right discipline. We can't just go boldly go, you know, we can't just cut Lindsay loose to go off and start buying stuff and starting to have it go together," in deference to what Nick Verus calls the "curated gloss." We've got to be real careful about that.
Folks talk about the industry 4.0 Factory of the Future, and there's a lot of repetition in social media that says this is an easy path to go down, or this is a path that many people have gone down, and that's not really the case.
Certainly, the Steve Covey mandate, right? We start with the end in mind. However, not all visions are equal. So, what I'm thinking is the heavy haulers, they have incredible visions, you know, paradigm shifts, but they also have literally billions of dollars to invest and accept that it may not work out. They embrace or accept the associated risk and uncertainty.
In our small manufacturer world, we're in a very different space. We're a follower; we may even be a technology lagger, and that might be a very healthy thing. You know, if we want to have frugal control of an operations budget, then we need to spend wisely. So, you know, it may sound cruel to label us as a laggard, but we can't expect to realize the panacea of AI with a zero budget, and maybe not even with a million-dollar budget. So, we've got to be, you know, perhaps our small manufacturer intent is less of a strategic vision and more of a general direction, and that's just fine.
You know, we don't have the big company organizational change management organization. We have a few well-intended folks and hopefully maybe a couple of outsiders that can drop in, but, you know, our general direction is going to be, are we going to reduce cost? Are we going to improve consistency in manufacturing? Are we going to automate a mundane, repetitive task? And that's one of my faves. That's near and dear to my heart. I think there's a lot of manual work done on some procurement and purchasing teams that takes a lot of time and resources away from those individuals being more strategic. So, there's a balance between, can we find affordable software that actually will work and we can implement and get up and running pretty quickly? That's an investment in time and what will that do for us long-term by giving our buyers the ability to be more strategic.
Exactly, exactly. And the clearly a latent need, clearly, you'll find many people to agree with you, Sarah. How do we make it happen? What's stopping small manufacturers from doing the things they know they should be doing? So, one step is to kind of simplify this and take the general direction and come up with a first delivery milestone. So, something that's less of a stretch, something that's going to give the team low-hanging fruit, an early win, a proof of concept, if you will. So, whereas we have a rough path to our general direction, we have a more detailed path, of course, to our proof of concept, our first milestone.
But to get there, we need to know where we're starting from. And that's now we're starting to touch on the stuff that gets overlooked in, you know, by Nick Verus's quote about curated gloss reporting. You look at the conceptual infographics on LinkedIn; they're very clever. The mappings of the different boxes, the different hierarchies of the IT stack, the data flows. But there's an assumption there about we're all starting from a common place, and that's not always the case. Certainly, we need to know. It's not just, you know, what data fields do we need and what data fields are available and what data fields need to be created, for example. It's, you know, how do we connect? How do we communicate? How do we gather the data?
And the problem is the data fields might be dirty, or the connection might not be there. You know, one of the favorite red flags when you walk through a manufacturing site is, you know, why aren't the scanners being used? Why aren't the scanners being used in the warehouse? Well, the pickers don't like them because they say they don't work sometimes. Okay, where in the warehouse do they not work? Well, there's dead spots. Okay, so we've got ourselves a network problem. You know, we can't generate reliable data; we can't, if we put the people-process-system filter on the challenge, the people aren't going to support it if scanners don't work. So, the point is we may have to go backward and revisit our legacy network in the factory and mitigate, you know, subcat 5 cabling, do an active scan of the facility, and then identify dead spots, put in enhanced wireless connectivity just to get the data, just to get the connection to the data.
So, you know, it may well be that from that connection standpoint, we're actually behind. You know, we've got to take a step back before we can get started. And I would also argue or interject that I think figuring out what data fields you actually need is really important, right? There's no point in having data that's readily available if it's not useful for you. So, as you're going through this process about data, right, data is important, we need to have it, we need to have clean, good data, but we also need to figure out what data fields we actually need and then what we're going to do with that data, right, and where's the data going to come from? You don't want one of the challenges even with cloud hosting is the fact that we can have the same data in different parts of the cloud, but, you know, one gets updated, one field gets updated from the ERP each time it regenerates, another one gets updated, you know, every few hours from the manufacturing execution system, and another one gets updated by customer orders, you know. So, we, where's the data going to come from? What fields do we need? Where's it going to come from? How are we going to pull it together to collate it?
So, this is where we start to get complicated, and, you know, depending on the bandwidth of our IT team, you know, how much do they want to jump in and make it all happen? How much do we want to include in the scope of the proof of concept? You know, I can have a fun little proof of concept that says, making stuff up now, that says, "Okay, here's 10 changes to 20 purchase orders. Send them out to three vendors and show me and how show me how efficiently they can get me information back and I can plug that into my system." But, you know, how do and that's part of it, but that isn't the whole process flow. So, I think part of the solution, obviously, is mapping out the entire process flow, and that's probably a good segue because mapping out the process flow is a wonderful way to communicate complexity. You know, a frustrated supply chain or buyer teammates saying, "My job's complicated," doesn't resonate as well as a conference room wall covered in color Post-it notes that people walk in and say, "Oh my goodness, we got ourselves a challenge here."
And then just because we know the data, you know, one of my favorites for purchase order process automation is the open order report. I mean, what for goodness' sake could be more fundamental? Is it clean? Doesn't need scrubbed? How are we going to get it scrubbed? You know, and that isn't a casual comment. You look at there's a reason that Susan Walsh and her classification guru team are so widely recognized and lauded as, you know, best in the data industry, up against so many titans and larger organizations because it's so fundamental. If you've got dirty data, this isn't going to work. If the open order report has stuff that people supply chain practitioners fob off and say, "Oh, don't worry about that. I just put that in there to remind me," or, "No, the supplier doesn't have a copy. There's no visibility of that," or, "Oh yeah, that's an old order. There's something going on in the system that the receipt didn't get done properly, just ignore it."
No, we've got to per, one, we've got to clean all that stuff up, two, we've got to demonstrate it's clean by running a report that's accurate. Three, then we have to circle back with the team and say, "Okay, what do we have to, here's the, here's the guidance to make this stick?" Because there's no point in doing all this work if the change doesn't stick. So, we've got to go back and train and retrain and make sure that we have the change adoption. You know, if there's people in the organization that get upset when old furniture gets moved around or when they're told you have to use this door or when we're told, you know, we have to open your backpack or your pen runs out of ink and you need a new pen and one's not available. Yeah, these, that's life. But we've got to make sure we've hard-locked it that the employees and incumbent managers who have disproportionate influence and resist the initiative, you know, perhaps it's less stressful to keep on accepting the ways we've done business and the associated, you know, subpar, mediocre performance and the discomfort of using spreadsheets. You know, we have to get them on board, and that, in itself, requires attention. You can't just brush over it.
So, Lindsay, my big question, we talked about the proof of concept, right? Figuring out what do we actually need? How do we kind of start small and build? I feel one of the biggest challenges is actually getting budget for something that's not in the budget, right?
So, how does what is, what strategies or how do you recommend people go to their SE suite and ask for something in this case, technology budget that's not been there before and is not included in the upcoming budget? Yeah, a great couple of ways to wrap up the conversation, Sarah. Yeah, I think first off, no small manufacturer likes to spend money. Every small manufacturer wants to improve efficiency, and the small manufacturer team will feel a lot better about the supply chain team if they see them leading the conversation around, "We've got to be more efficient. We've got to be more effective. We've got to focus on adding value and less on using and abusing people that we're paying money to be supply chain professionals to do administrative work that can be automated."
So, finding someone, perhaps as you said, in the SE Suite that will support an efficient productivity improvement, being able to perhaps paint the vision that if the board's beating up the SE Suite or the leader about, "What are you doing about AI?" being able to say, "We're putting the fundamentals in place. We're putting the building blocks we need to support that as the cost comes down and as the complexity comes down." Because without that broad support, it's more likely that nothing will get done. So, you absolutely need to get improved efficiency.
And then the last thought I think is that around that cost, I don't really want a quote. I don't want an estimate. What I want is input from people who've been there, done that, and have the dirt under their nails, if you will, that they've been in the weeds, as I've been accused of, and that's the easy way to do that is to read the use cases and to challenge anyone who says, "You know, anyone who says they know a thing or two about technology transformation, about process automation, that's nice, but knowing that there's a hundred facets to it is not enough. We need someone, we need input from people who have been there, done that in the field." So, the folks that clean the data, you know, the Susan Walsh's that clean the data, the Jeff Winters that talk about every day about their conceptual approach to factory automation at Hatachi Solutions.
You know, here's one for you: people who are in transition in our industry are lonely. They feel they're on the outs. Great to tap in and say, "Hey, question for you. Here's what I'm doing. Does that think I'm going on the right path? What would be your top three considerations?" And maybe you solicit broad-range feedback from other resources to make sure we're on track and we're not going to waste money on an initiative that doesn't work.
So, I have to call out Larry's comment to us earlier in the show. I do enjoy complexity and simplifying where possible, but sometimes it does need the complexity it has evolved into. Oh yeah, no getting away from it. So yeah, and I think that's the beautiful thing, the Post-it notes across the wall, they speak to complexity. Talking about, "Okay, we're going to do an additional initiative. We're going to go to Factory 4.0. We're going to improve consistency with improved machine monitoring. Okay, how does that affect systems, process, people? How does that, you know, what data fields do we need? How do we connect to them? How do we gather the data? What are we going to satisfy ourselves with with edge computing and a local display, a dashboard? How much are we going to push to the cloud? How are we going to manage the cloud activity? How much is it going to cost?"
Everyone loves to push all the hyperscalers love you to push stuff to the cloud, right? Why? Because the more you P, the more volume you drive, the more revenue they get. So, we want to manage that portion, but at the same time, we really want to get up to the cloud where we can do our AI. Bring in the AI guys and do trend analysis. Alright, with that, we will see everyone next Monday at 10 a.m. Central. I have a feeling we're going to have some sort of technology theme throughout the month of October. Lots to talk about when it comes to selecting and adopting, and then the change management piece around it and tech at a small manufacturer. Look forward to it.