12 Manufacturing Tips for a Brilliant 2017 (Part 6, Finale)
12 Manufacturing Tips for a Brilliant 2017 (Part 6, Finale)
Manufacturers need to ensure their design phase isn't creating problems down the road, invest in analytics, and ensure leadership is strong at all levels to succeed.
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If you've been following along so far, get ready for the home sprint. We've covered a variety of manufacturing aspects that you should keep mindful about. Whether it's connecting your supply chain or ensuring data flows freely from one person to another, there's a lot you can do to boost productivity. If you want to start from the beginning, we've got you covered: Part 1, Part 2, Part 3, Part 4, and Part 5.
Without further delay, let's dive into the importance of your design phase, analytics, and leadership as well as touching on how GE handles those processes solutions.
Tip 10: Born of Sand and Heat
Manufacturers are finding that the design phase of a product has vast implications on its serviceability and availability in the field. This is especially important for mission-critical products such as aircraft. This story is about an incident that our GE Aviation organization had with a specific component that encased the hot section of the jet engines we make.
As in any good research and development team, the component had been tested under many environmental extremes and was shown to be serviceable. The problem was that this component was failing inspection in the field. This caused a high level of maintenance and potential unavailability of aircraft engines for our customers. One of the aspects of what we call the digital thread is the linkage of information throughout the supply chain, which includes information from design through to manufacturing to the operating parameters in the field.
GE Digital has created a vast network of sensors and analytics that can be used to monitor our aircraft engines as they operate on wings in the fleet. This gave us the ability to analyze a vast amount of information and look for correlations to these specific components that were failing in the field. After looking at the data, it was discovered that most of the failures were associated with aircraft operating in the hot and sandy environment. This was a mystery as these components had been tested in high silica (desert) environments, but they had not initially been tested with the type of sand in that region.
Then the mystery got even deeper; not all the components were failing to the same level across the region. So, it had to be something involved with the way that a specific operator was using the engines. After looking at further information, it was narrowed down to a specific customer in the region. After speaking with this customer, it was discovered that they had a policy of taking off and initiating a higher climb rate than the rest of the fleet, which created more heat in the engine, which led to a higher degradation of the material and more failed inspections in the field.
This information was provided back to the design department, which then improved the material to be more silica resistant in a high-temperature environment. The result was much fewer failed inspections in the field and more availability for our customers. The key here was the linkage of operation information to the design process and leveraging this to make continuous improvements.
Tip 11: A Powerful Case for Analytics
Ever since Thomas Edison launched the Pearl Street Station in New York City in 1882, electricity has played an integral role in the growth of commerce, and is now essential in everyday life. If you ever experienced an extended blackout in a city you know exactly what I mean.
For many years, power has been generated using steam turbines that are driven by either coal or gas-fired power plants. Typical operating procedures for these plants involve running the turbines continually for several years before taking them down for maintenance. Therefore, the design required that a machine that ran continually, and only started and stopped a few times in its lifetime.
With the advent of solar and wind power, these traditional power plants found themselves needing to start and stop more often. This is referred to as cycles within the plant, where—for example—the gas-fired power plant may only come on at night when the sun goes down, and the solar power plant is no longer able to generate electricity.
These cycles will cool down and contract and then heat up and expand the turbine impeller assembly, which makes it more susceptible to cracking. Also, the failure mode of these types of assets that are cycling up and down is very different from the behavior of a plant that is runs at a steady state for many months. Traditional predictability goes out the door.
In addition, other aspects come into play, such as different power utility companies and personnel, weather conditions, utility customer demands, and a host of other differences.
Producers of steam turbines are facing a whole new set of challenges versus what they were facing before with the greater adoption of alternative energy. They needed more information to be able to make better decisions about how to do maintenance on the equipment. Also, they needed better models that could be used to make design enhancements and improve the operating life of the equipment they are manufacturing.
The answer is to create a digital twin of the turbine made up of little digital twins of all the components that make up the unit to allow the monitoring and simulation of these devices in the field.
In addition to this, they should telemetry and sensors on the turbines that collect all that information and ingest it into a database which allows analytics to be performed on the data set.
The analytics will make it possible to improve optimization and "what if" scenarios such as:
- "What if we increased the let down cycle and/or decreased the cycle, what impact does that have on the impeller's material characteristics?"
- “What if I increase the turbine pressure ratio? Does it improve the performance of the combined cycle cogeneration system?”
- "What if we scheduling of maintenance this month versus next month; does it increase the failure conditions of this specific turbine?”
With these insights, the team could make more informed decisions, which included:
- Better information on how to stock parts across the supply chain, depending on operating conditions and projected failure rates
- The ability to source parts based upon the projected performance of parts in the fleet. This led to better pricing negotiations with suppliers.
- Less downtime for inspections as the wear can be predicted more accurately.
- Better able to model the need for personnel and equipment deployments to specific facilities across a geographical area; better marring maintenance needs with available resources.
- The ability to create customized maintenance schedules which included lubrication, cooling, optimization, and inspection requirements based upon the varying operating conditions
- More effective scheduling of replacement unit manufacturing and MRO operations on plants based on field service projections. This helps reduce inventory and conserves cash.
Tip 12: The Leadership Component of MES
Build on, Don’t Replace
Process equipment is installed in a plant with the expectation that it will last for 10 or 20 years. Furthermore, these plants have manufacturing operations and intelligence systems like PLCs, SCADA, and historians where a lot of intellectual property has been invested and providing real value.
To leverage this investment, a manufacturing execution system (MES) or manufacturing operations management (MOM) system needs to leverage the existing systems while adding specific value where a specific outcome is needed. These new investments should be added, leverage existing system data and provide a demonstrable ROI quickly.
Everyone in the Plant Needs to use the Same System to See Value
To get the most of your manufacturing operations management system, everyone must be “on board” with the idea of using the same system. This can cause some angst among employees who like to do things their way, or are concerned with too much management oversight. Also, some operators think they can do better or are stuck in the old way of doing things. Unless the system is capturing all the information from all the machines and all the operator actions, it’s very hard to correlate performance across the entire plant.
Furthermore, operators may think that this is just another way for management to keep an eye on them, the big brother in the sky. They think, “I don’t want anybody to know how I operate my machine on my shift because I might do some things that are unconventional and get blamed for that”. This makes it hard to capture that tribal knowledge that the old-timers have on how to run the process and pass it on to a newer generation of operations personnel. But the reality is, you can’t optimize what you can’t measure, which means leadership needs to work with plant employees to show the empowerment that the new system provides.
Tight Margins Make it even More Important to Make Incremental Investments
Let’s face it, margins are tight and getting tighter. Take CPG industries, for example. They have squeezed every cost out of the plant that they could. This makes it even harder for IT to justify investments. Maybe it’s time to look past the four walls of the plant for productivity improvements. Furthermore, the ubiquity of cheap sensors has not yet met with success in deployment in mass across the organization. Users are looking for incremental investments that will result in immediate wins that can be used to self-fund the next level of technology investment. They want something that is modular and provides greater agility in solving problems. More direct outcome-based.
At the same time, the IT department is getting pressured to reduce costs. This usually results in a reduction in headcount. What is needed is to drive a new initiative that will have longevity and have a longer tenure of these IT professionals so they can carry the project through to completion and see the benefits from the strategy. MES systems are expensive to put in and require a faster payback than a five-year capitalization. Management doesn’t have the patience to wait for the benefit. Also, how can you guarantee that the operators will leverage it to get the benefit?
What’s a Manufacturing Leader to Do?
This goes into the leadership component of making MES and MOM investments. It takes a charismatic individual and like-minded team to get the entire organization behind the idea and to get the benefits from technology investments. The organization needs to use the investment and the tools provided. Many times, management is slow to change and people with great experience working in a department or field assume the solution based on past judgment rather than allowing fresh insight on how to approach the problem. Investment in an MES system versus 10 engineers with clipboards. Some management still believe this is a better investment versus the real-time collection of information and correlation that only computers can provide. They are slow to change their minds and approach to solving problems.
Technology investments should be part of long-term vision, with incremental steps on how to get there, but always with the end goal in sight.
At General Electric, we’ve experienced these things firsthand in our own factories and we can help by providing some counseling on how to approach the organizational change within your departments. Islands of information are not a technical problem. Information silos are created by people. So, choosing to become a digitized organization requires a mindset change and the effective leaders in the organization.
Published at DZone with permission of Steve Garbrecht , DZone MVB. See the original article here.
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