April 23, 2019
Manufacturing Challenges Blog Series: Complexity
Welcome to the final installment in the Manufacturing Challenges
blog series. We’ve looked at a number of challenges to manufacturers over the previous weeks in this series: audits
, defects and recalls
, operator engagement
, waste reduction
, and data overload
. In this blog, I’m going to focus on complexity.
Complexity can be found just about everywhere in manufacturing, including deployment and configuration of your quality software, setting up and maintaining data collection plans, sifting through the rafts of data we collect, responding to issues in our processes, and much more. Let me show you how InfinityQS can help you with those complexities.
It’s a Setup
When you deploy your quality system for your organization, you have that one nagging question: “Where do I start?” Where you start is an important question, but the answer depends on the answers to lots of other questions.
Is this a company-wide, enterprise deployment? Or internal to your department? Or perhaps it will start with your department and then, over time, roll out to the rest of the organization? Is this state-wide? Regional? All of North America?
Or is this something you’re considering because you want to improve your processes, a long-term commitment, and you want the benefits that this can bring to your bottom line? Lots of questions to answer. The answer to “Where do I start?” depends on who's driving the deployment and the scope of the project.
Start Small and Scale
One great thing about InfinityQS solutions
is that they are scalable. You can enter into your decision about where to start with confidence—because you can start small (which means, hopefully, simple) and scale to whatever size fits your quality project. [My colleague, Eric Weisbrod, VP of Product Management here at InfinityQS, discussed scaling at length in his blog The Ideal Enact Software Deployment
. Definitely worth a read.]
For example, you can start with something basic, like a brand new part you’re going to release for a customer. Just one part; two or three features. You need to do special data collections, perhaps even special reports for the customer. It’s important to know that all the reporting, submitting, and getting access to the data that a big deployment would entail will be the same for a small project. So why not start small? Then, when the time is right, you can scale up to a whole program.
When issues occur—and we all know they will—you need to know exactly how your processes work in order to track down the precise point at which things went wrong. That’s a complex task. The best way we’ve found to do this is by building a process model
of your process(es). Enact®
does just that.
Mapping the Structure—Why It’s Important
It’s all about the Enact process model. This visual representation of your process helps you connect the dots from raw materials all the way to finished product.
One function of the process model is that it can help you work through and map your entire process from start to finish—something you might not otherwise do. Building the process model forces you to know what the inputs and outputs are for each operation step, what features are created along the way, and what and where tentacles branch off.
Building a process model without the Enact tool is a daunting task but Enact makes it easy. It takes the complex and makes it simple, visual, engaging, and online.
Let’s move on to a subject that’s near and dear to my heart, the backbone of any quality project: data. It all starts with data collection, right?
The foundation of data collection is the structure of the data. In practical terms, that means “how are we going to tag the data?” Required: we must tag each measured value with a part, process, feature, and timestamp. But there is a lot more to consider—for example, employee, lot number, data collection name, customer name, reason for the data check, and any number of other bits of information that one might want to tag to the data.
Structuring the data is important to do up-front because later, during analysis and reporting, you will want to sort the data by these different tag fields. You can’t do the kind of sorting you will want to do unless you deal with tagging at the beginning.
However, tagging must be flexible. You must also be able to remove tags. Perhaps you included tagging something like ambient temperature settings as part of your original plan. You obviously thought that was important at the onset, but now you’ve decided that it’s just not as important as you once thought. You should be able to—and with InfinityQS’ software, you can—remove those tags on the fly. That’s flexibility in the face of complexity.
We Know Data
The types of data collections you plan to perform must be flexible, robust. Whether it’s manual or electronic data collections, you need software—you need a company behind the software—that understands data. That includes the many sources from which it comes, where it goes, and how and where it’s stored. Data is our specialty. We understand manufacturing, and we understand data.
Analysis and reports are important. We all know it and live it, and I can’t stress that enough. What good is the data if you can’t explain it to management, or analyze it to determine what is working and not working on your shop floor?
Much of what happens during data collection is driven by the decisions being made at the backend by someone who reads your reports, responds to alerts, and makes process adjustments.
If you set up data collections correctly at the beginning, the analysis and reporting that occurs later on is that much better. So, among the questions you ask during setup, which relates to analysis and reporting, is: Who are the customers of the data?
- Is it limited to an operator on a particular machine? Or is there a broader audience?
- Is it destined for a manager? Or a department head?
- Is someone downstream interested in this data? Maybe they need info to help them set up their machines…
InfinityQS tools enable folks who aren't necessarily at the data collection station to get access in real time to see what's going on at another station, so they can make quick, informed decisions about their own processes.
So, an important part of analysis is dashboards. They quickly tell you when something is amiss or requires your attention. In Enact, they graphically display comparisons of any data you want—across lines, shifts, sites, facilities—at a glance. And then, say you as an operator need or want to drill down into a particular machine or lot, you can easily do that and get to highly detailed information.
One aspect of Enact dashboards that combats complexity is role-based access. Since Enact knows what you need to see based on your role, you don’t need, or have to, look at a lot of extraneous information. You focus on your job, and when something needs your attention because it is directly in your purview, Enact shows it to you with real-time notifications. If it pertains to someone else, or someone else’s line or shift, then they
see it. You remain focused on what’s important to you and continue pushing out quality product. [Here are some links to blogs about dashboards that you might find interesting: 3 Great Things About Dashboards in Your SPC System
; and Dashboard Driven Insights
When you are alerted to a data collection or a potential issue in Enact, how and where you respond are simplified. Workflows provide the solution.
Workflows in Enact stipulate rules for how your organization reacts when an event occurs. By “event” we mean data and timing violations.
- Data violations include manufacturing limits (specification, reasonable limits, net content control limits, etc.), statistical violations, etc.
- Timing violations include missed or late data collections and checklists.
You may define any or all of these to be considered exceptions to your day-to-day operations that occur on the shop floor. And, let’s face it, these types of violations happen to everyone in manufacturing.
The benefit of workflows is obvious: nothing is left to chance, and it’s easy for users to see what is happening—and what they need to do—at a glance. It’s just another way in which InfinityQS sheds light on complexity. [Read a detailed Workflows
So, in short, complexity in manufacturing can be found just about everywhere, from deployment and configuration of your quality software, to setting up and maintaining data collections, sifting through the data you collect, and responding to issues in—and reporting on—your processes. InfinityQS can help you with complexity so you can focus on doing what you do best: making quality products.
Thanks for joining us for this blog series. It’s a lot of valuable information, so please feel free to read the other Manufacturing Challenges
blogs in this series: