Expert Panel: Predicting the Future of IT in Supply Chain Management
Tod Grams | Business Development Partner
This article was originally posted on InsITe Business Solution’s Blog.
Join expert guests Pete Schroeter, Tod Grams, and Dave Poggi as they discuss the future of information technology in supply chain management in this informal manufacturer’s panel. These industry veterans delve into trends in manufacturing today, transformative technologies, and the future of data management as it relates to supply chain management.
-What are some of the key innovation trends you are seeing in manufacturing today?
-What technologies have been and will be relevant to help drive these innovations?
-Given the recent economic challenges, in what ways are manufacturers pivoting?
-What technologies have been the most impactful in aiding the pivot and future success?
Meet the Panelists
Principal – Schroeter & Associates
Pete Schroeter is Principal and sole member of Schroeter & Associates, an independent manufacturing and supply chain consulting practice started in 2002. He is a 35+ year veteran in the food, chemical, pharmaceutical, and packaging industries, supporting companies by improving their cost, inventory, and service performance.
Business Development – Disher
Tod Grams, Business Developer for IoT, Digital Tech Solutions and Electronics development is a skilled leader with a demonstrated history of working in Electronics, IoT, IIoT, Automation and Digital Tech Solutions. Skilled in hardware and software development for digital tech solutions, Tod has been with Disher for the last 6 years, prior to which he spent 21 years as a developer.
CEO & Principal Consultant – Software InsITe LLC
Dave Poggi is the CEO and co-founder of Software InsITe, a technology strategy, consulting, and development company focused on manufacturing and distribution. Dave improves his clients’ business processes and builds applications and integrations that improve margins. Technically inclined, Dave’s skills focus on Microsoft’s stack, including Dynamics Business Central, Azure, A.I., SQL, .NET development, PowerApps, Power Automate, and Power BI.
Panel Host/Technology Expert – InsITe Business Solutions
A regular content contributor for InsITe Business Solutions, Andy Syrewicze has experience managing IT solutions for a diverse array of enterprise clients. He provides CIO-Level Strategic planning and operations services, as well as helping enterprise clients align Cloud and Office 365 services with business needs. With almost 2 decades of IT experience, Andy is heavily involved in the wider IT community in a number of different ways, including, podcasts, webinars, blogging and public speaking.
1.What are some of the key innovation trends you are seeing in manufacturing today?
Pete: Manufacturing is a big, massive area, so I think it’s helpful to break it into a couple of pieces: Process Manufacturing and Discrete Manufacturing.
A good way to look at the differentiation between the two is that in Process industries, you make things in batches and put them in packages. A process product could be a bottle of ibuprofen, 100 count, where you make a bunch of tablets and you put hundred counts in bottles. Or it could be chemical surfactant and you put it in a 195,000 pound railcar.
On the Discrete side, you make things in niches. You know you’re going to make 1000 seatbelts today.
And so the manufacturing strategy, how you setup and configure varies between the two. Some of the innovation that we’re having the discussion on can vary between the two as well. So in process industries, there’s manufacturing strategy issues. What do you do with high volume items versus low value items? How do you set up your floor? How do you go after things like quick changeovers?
And that is a product of two things. One is physical things: How do I make it go from configuration A to configuration B? But how do I get the information to bear to know whether I should make that change over – or not? What’s demand tracking at? What’s the order stream look like? What’s the shipping stream look like etc etc?
On the discrete side, you have different strategies, such as cellular manufacturing, modularized, manufacturing. Maybe you can figure that you’re in a “product family”, and then moving from one item in that family to another item in that family minimizes your your wait time, your queue time to get from here to there.
So a lot of innovation, both on the physical side as well as information, coming to bear to make those sorts of sorts of changes. I like to say “Geez, if we can understand how to make decisions with real time data, that cuts a lot of poor metrics down to size.” Often inventory and improvements in service improvements.
So innovation is a confluence of physical setup and configuration and information to bear, and I think we’ve got some questions on information later in the dialogue and I’ll talk about transaction data versus master data as we get to that point.
Andy: Sounds good. Tod, Dave, any thoughts?
Tod: Pete brings up a great point and that’s relative to the trends that you’re seeing with data. I know we’ll get into data a little bit later, but those innovation trends really start with the company and in the “why” you know.
But ultimately with the data, you’re making decisions, and that real time data is so critical. Having that critical data that’s available being processed in real time (as opposed to going back and having to take a look at the data) that criticality, that timing piece is a huge trend in manufacturing that is really, bar none, the most critical step in manufacturing today.
2.What technologies have been and will be relevant to help drive these innovations?
Dave: It starts with connectivity; we’ve got a tremendous amount of information that our machines on the floor are generating. But we have to have that connectivity to say “How do we get that data?” To Pete’s point and what Todd mentioned, if we don’t have that data, we can’t make decisions on it. It’s full-time connection to the equipment on the floor to the operators so that as data is coming in, we’re gathering it, we’re able to make decisions on it.
Some technologies like artificial intelligence and cognitive services and vision that are things we can bring to the floor now that used to be expensive or difficult, but now have really been commoditized (not in a negative sense). There’s definitely product differentiation between the capabilities out there, but in the sense that we can now apply these where we used to not be able to afford to. We can put vision in places that used to be “We’d never go there. That’s not our number one machine. We aren’t going to do it.” Now, that’s the kind of thing we can get past and put real.
This gives us the ability to see what’s going on to collect the data that correlate with that and to apply some of the newer capabilities around artificial intelligence. Get real time schedule information, things that used to take the end of the shift or the end of the day. And heck, at one point, the end of the week. Now those things are happening real time.
Andy: That sounds good. Pete or Todd, anything you want to add?
Pete: Well, to Dave’s point, one’s ability to put real time information in place to bear on decisions has real business impact. Real value prop. So if you can put information and make decisions that help you extend a production run or cut off a production run, that certainly has real time customer service revenue impacts in one case. Or maybe working capital inventory impacts/cost impacts as another case.
It’s not just “kind of fun and it would be nice if we could do this” ,it has real measurable business impact. Put that real time information, maybe with the use of some AI that the technical guys can bring to the table, in place.
Tod: To add to that point for both what Pete and Dave are saying, those benefits that you are referring to are things like global and regional factories having that data intelligence in real time, no matter where you are in the world. So other things are like supply chain management. You know real time supply chain management oversight. Also, and we talk about it all the time, and that is the predictive maintenance. That’s where the AI comes in. You need to keep those machines running, and in order to keep those machines running and keep people constantly building widgets and devices and things like that, that predictive maintenance becomes a critical piece to keep in the factories efficient and flowing.
3.Given the recent economic challenges, in what ways are manufacturers pivoting?
Andy: Pete, I’ll let you take a stab.
Pete: Many companies need to precisely map capacity to demand, so I see a significant emphasis, more so than on the demand side, on the capacity planning side. The demand side is pretty important as well, but if you’re going to pivot into product line extensions or pivot to a different product or change the way you manufacture or do a contract manufacture – all those strategies require some really good solid precision capacity planning.
Get back to this notion of real time: So where are you in your production runs? What does your mix on the production floor look like? How does that relate to your demand? Can you make those alterations in real time as the clock is ticking? Or are you more discreet and have to wait until the end of the day to make your decision for the next day? Big differences in inefficiencies.
So capacity planning is something I see is really, really challenging manufacturers, and that goes into the extended supply chain as well. So if you are a discrete manufacturer and you got 42 different parts that go into your finished good, then you have to have a real good understanding of the inflow of material of those 42 parts from however many of our suppliers are providing those. The communication game becomes really important. It’s not just the supply chain within the four walls of the manufacturer, but outbound to the supplier base or upstream of the supply base.
And for that matter, to capture the demand statement, you need a good understanding of what’s happening downstream in your customer base. So it’s not just the the four walls or the planning exercise for capacity, but upstream and downstream become very important. Real time information becomes really important. Collaboration becomes very important. How you wrap process around all this becomes very important. Extended supply chain capacity planning is where I see a lot of emphasis these days.
Andy: I’m starting to see a trend here. Some of the same keywords starting to be thrown around. Interesting to see. So Tod, I’ll let you take a stab at it. Are you kind of seeing the same things on your end?
Tod: Yeah, absolutely. Capacity planning certainly is one of the things. And again, we talk about efficiencies. We talked about machines, processes, and things like that. How impactful that is is like the difference between either making a part or not making a part, or making a delivery or not making a delivery. And most manufacturers, I’m assuming, at this point cannot not make right now – They need to make their shipments.
So better tools in the way that the manufacturers can pivot is the key to being agile. And if you could be agile, keep machines running, manage not only incoming flows of equipment and product, but then also the outgoing and understand the supply and demand, the better off we’re going to be at being efficient.
4.What technologies have been the most impactful in aiding the pivot and future success?
Andy: I want to swing to the technology part of the discussion and bring it back to Dave. Dave, any thoughts on that end?
Dave: Sure. There’s a couple of things that have been super important for folks, one being the ability to connect and get visibility to that floor. You know, look at us here, remote; that’s something a number of manufacturers are having to deal with right now. That means they need virtual eyes and ears on the floor. That spells out getting visibility, getting your production, even if it’s the basic stuff. What’s our cycles? What’s down? What logging is occurring?
Most of the manufacturing equipment out there has PLC’s and a tremendous amount of data embedded in those machines. Exposing that bringing it out and letting that be visible across the organization. Whether you’re in the same building or the same County, or even across the globe, that’s hugely valuable.
And there’s been an emphasis on those manufacturers who thought everything they did was local. Well, local doesn’t mean what it used to with covid. People are starting to recognize they need to have that visibility. Even if we still want to walk the floor, that isn’t always going to be practical.
Andy: Todd, Pete, any thoughts on it?
Tod: Dave brought up a great point in the connectivity element. I mean, those tools exist. Cloud Services, the main ones; you know the Amazons, the Googles. Those are the key elements to getting that data to where it needs to be processed and understood. So you need to get it from the localized machines and PLCs, etc too. Those data warehouses, if you will. You can look at the data and make real time decisions with that. So connectivity, bar none, is probably one of the most impactful technologies that we see today.
Pete: To add to that, I think your brief comment on data itself. Key downfalls that I’ve seen are issues of latency or accuracy or inaccuracy or incompleteness. And if you look at data, I like to slice that one up into a couple of buckets as well.
You’ve got transaction data and you’ve got what I would call master data. Transaction data is things like orders, inventory levels, inventory status, production data, etc. Master data might be things like practical capacity in manufacturing or throughput, or bombs, or routings, or packaging Masters. Those kinds of things. If either or any of those are less than stellar, your application and your IT functionality is going to produce an undesirable or an inaccurate result and lead to a poor decision. It’s going to have business impact.
So I see an interesting trend: I see a number of companies putting somebody in charge of master data or data management. Somebody either in the technology group or in some cases in the functional manufacturing or supply chain groups because there’s a recognition that if we’re going to rely on technology to drive these decisions, we better have data that’s up to date, not latent, and complete.
Andy: Kinda comes back to the whole concept of “garbage in, garbage out” as far as data goes, right? You gotta make sure it’s the current data that you’re going to be actioning on, so that’s fantastic insight.
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