Podcast: ARP vs. ERP — The future of CPQ and order-to-cash for industrial operations
Wiley Jones is co-founder and CEO of Doss, a company on a mission to increase the agility of the global value chain. Wiley received his Bachelor of Science in Mechanical Engineering from the University of Illinois Urbana-Champaign and has previously worked for Athelas and Verkada. Wiley recently spoke with IndustryWeek editor in chief Robert Schoenberger about how configure, price, quote (CPQ) and order-to-cash workflows can be used to meet the needs of machinery and industrial companies.
Below is an excerpt from the podcast:
IW: Can you tell us a little bit about Doss ARP? I guess the big question there is ARP is not the term. A lot of people here are looking for ERP.
WJ: You're spot on. ARP is a made-up word, a made-up acronym, as many acronyms are. So, we're not too different from a lot of the guys that have been around for a long time. ERP also is kind of a made-up acronym, if you think about it. Enterprise resource planning doesn't mean a whole lot when you think about the exact terminology. ARP really is about adaptability. Adaptive resource platform is what we call it. The whole goal is to give a lot of our end customers the same kinds of tools that they're used to using in their actual operations, in their day-to-day, building with the same level of craftsmanship that they give allows them to address the biggest problems as they are moving around information and data in their business.
IW: It's interesting. A lot of the work you've done has been with companies working in the grinding or deburring space, which is a little different from straight-line, high-volume manufacturing. You're dealing with a lot of custom work or work that can vary greatly from job to job. So how do you bring that sort of digital transformation or that digital management to something that isn't quite as regular, as predictable as say something like stamping or traditional machining or something like that?
WJ: That's really interesting because I also made that transition myself, personally, prior to even starting the company. I worked in electronics manufacturing, which is a lot of times about as high volume as you get. And that really, I think, ingrained in me the necessity of being able to model data so that you can really easily understand all the inputs and outputs for your business. I found that when we went and talked to people who are doing a lot more made to order, where they get an e-mail in their inbox from one of their customers and it says., “Hey I need X, Y, and Z, and oh, can you make it like last time?” They have to go and dig through, rifle through a bunch of papers or they have to go and look through so and so else’s e-mail inbox to figure out what this person is talking about. Those are a lot of the tactical problems that we see on the ground with customers as they're dealing with made to order and as they're dealing with that configuration. We've tried to do our best to give them tools to help streamline a lot of that in ways that I would say are normally not accessible if you're going to call up someone else on your operations team and say, “Hey, what did we make for so and so last time they called and asked for a part?” It's really tactical, honestly, at the end of the day.
IW: One of the goals a lot of us have when it comes to managing workflows is to automate as much as possible, especially given the difficulty people have finding available skilled workforce these days. And on top of that, just to have that regularity and that repeatability and dependability of the inputs. How do you manage that when you're talking about some variable work?
WJ: I think our framework for this is to really deeply understand our customer’s business in a very specific way from the very start. It's a little bit like being put into one of those psychotherapist chairs. They lay back and they ask you a bunch of questions about your dreams. Our customers maybe feel like that a little bit when we start out and we talk to them. We're asking them so many questions about the nuts and bolts of how their business actually works. How they take in an order, why they're quoting it the way they're quoting it, what are the inputs that are driving, why the quote numbers change depending on the parameters and the profiling, and whatever it is.
Once we have determined all of that stuff, then we think about it a little bit more abstractly. We kind of squint at it. We try to understand it in its basic primitives, which are the same way that you would think about machining, like this is a bend or this is a cut or this is a hole. We think about this the same way on the data modeling side. We say this is a table, it's got a bunch of line items, it's got a bunch of numbers that all multiply through like a formula. And we can reconstruct something that will automate it on just a couple of inputs. All you need to put in are a few dimensions and a few parameters and it'll spit out a quote back to your customer instantly. That's the automation that is really powerful when you think about taking and transforming the tactical, what's actually happening in someone’s business, into a reproducible mechanical process. It's actually not that much different than just designing machinery at the end of the day.
About the Podcast
Great Question: A Manufacturing Podcast offers news and information for the people who make, store and move things and those who manage and maintain the facilities where that work gets done. Manufacturers from chemical producers to automakers to machine shops can listen for critical insights into the technologies, economic conditions and best practices that can influence how to best run facilities to reach operational excellence.