If you’re a performance specialist whose job it is to take the manufacturing and packaging sector to new heights using continuous improvement (CI) methodologies, approaches and tools, you’re in for a tough slog.
Stymied by cultural issues, paper based tracking, or a belief that existing software systems give a factory everything they need, the barriers are enormous when it comes to pushing a performance improvement project through. The pain is common, whether you’re a plant manager, CI lead, automation supplier, equipment manufacturer, or contractor.
The reality is, most systems and processes in a factory have been designed to make root cause analysis tough, and without good context, it’s extremely difficult to identify root causes to build out performance improvement projects.
If the genesis of your CI project is a downtime or efficiency issue, you can probably sympathize. Sure, fault and error code notifications are retrievable, but you’re missing context to help you get to the root causes of issues, as software providers over the last twenty years have completely underestimated the power of context and root cause identification.
Look no further than your organization, relying on your operators or supervisors commitment to walk to a personal computer to enter information on root causes of downtime events, long after they occur. The screens they interact with were designed as an afterthought, simply streaming data in with text boxes for data entry. Very little appreciation has been given to user experience affecting adoptability of the systems, and dramatically impacting the context you get from system users.
Here are a few hypothesis you can test and challenges you can take to explore the power of a discrete, context-first system, which ultimately will challenge how you get to the root causes of downtime in your operation using new approaches.
1. Ignore Your Normal Workflow to Explore the Power of a Context-First Approach
Test our hypothesis that it’s worth putting a resource onto a time-bound, scoped out project, solely to collect root cause data associated with potential issues in your factory. Use a semi-automated system that is web enabled to collect critical information on context and events.
The goal here is to collect more powerful diagnostic data than you can by aggregating fault codes like you usually do, allowing you to get to the root of your problems effectively and rapidly, without having to leverage your existing cumbersome systems.
Remember, this is a moment-in-time experiment, to prove the power of the context so that you can make changes to your existing systems and internal processes. You’ll be out the labour cost of the resource you dedicate to your project, and a little bit of your time, but, you can usually get an automation supplier to support such an effort for free.
You may even find that your natural inclination to rush to connect all of your disparate machine based systems with software doesn’t make sense after you try this. There are times where a mobile-based system to retrieve better context on events that lead to factory issues will lead to integration with that system at a later date if possible, streaming machine data into the contextual system and not the other way around.
2. Put in a Context-First System to Trial New Equipment
Test our hypothesis that you can work with your original equipment manufacturers (OEM’s) and automation suppliers to get real-time access to trial related data, including quantified outputs from the trial on the value created by new equipment or consumables you may be buying.
With a context-first system for trials, you can define success criteria before you begin a pilot and build this into your tracking efforts of the trial on site. This includes defining how long a trial will run, what objectives define a successful pilot, and what parameters will be measured throughout the pilot to gauge movement towards meeting those objectives.
Mobile devices can be configured so that it’s clear what information field based partners or on-site employees at a plant need to collect throughout the trial period. Then, you can receive real-time diagnostics from the floor. With an appropriately configured system and a powerful reporting engine, you’ll have all the intelligence you need to share successful pilot or trial results with plant management, procurement, and other decision makers.
If the trial was borne out of downtime issues, the power of context-first downtime tracking will be immediately evident.
3. Picking Your Own Context-First Project and Calculating the Financial Impact to Change How You Track Downtime
This one is simple. Pick a simple project to address an issue that you know is costing you money (think downtime or rework), given a lack of context. Deploy a discrete system to help you collect data, and review the data at the end of the project relative to the project goals. Now calculate the financial impact of what you’ve learned. Test our hypothesis that putting context ahead of a system driving only machine data, isn’t always a “garbage-in” and “garbage-out” scenario. One of our partners just did exactly this, to the tune of identifying $2.5M+ in unknown issues in 1 day.
To learn more about how mobile-based performance improvement systems designed with context in mind, click here.