Author Archive | Maintenance Technology


6:47 pm
November 15, 2016
Print Friendly

Employees: Retain the Good Ones

Workforce ManagmentHighly engaged employees are 87% less likely to leave their employer than disengaged counterparts. Are you doing what you can to engage your people?

Mary Jo Cheney is corporate TPM coach at GE Appliances, Louisville, KY, and tacks CMRP, CRL, and CPMM onto the end of her name. Her credentials and experience make her an expert on managing and retaining people. She shared that expertise in her presentation, “Ignore the Man Behind the Curtain—Lessons of Leadership from the Wizard of Oz,” given at the 24th Annual SMRP Conference, held October 2016 in Jacksonville, FL.

The Wizard of Oz angle was a creative use of the characters in that classic movie to represent various aspects of personnel management and retention. I’ll spare you the Dorothy, Cowardly Lion, and Tin Man references, but share several of the facts and figures Cheney provided to help you better understand how to identify and retain talented employees.

—Gary L. Parr, editorial director

randmRetaining good leaders

Effective leader retention starts with honest, clear communication, which is not a strength for most companies. Good leaders also stick around if they are fed enriching assignments that challenge their talents. This is particularly true for younger people. Along with that is providing a clear line of sight to the next opportunity.

She also suggested the importance of knowing your competition in terms of who is likely to steal good employees. Cheney told a story of a competitor who bought a billboard sign near the entrance to one of her previous company’s property in an attempt to lure away talent. That sign got the full attention of employees and management.

“Training is critical!” stated Cheney. She asked two questions worth serious consideration: What if I train them and they leave? What if you don’t and they stay?

She also quoted Mark Alan Csonka, the smartest businessman she has ever met: “I hire people who are smarter than me and then I help them grow. I do not feel insecure because they know more than I. In fact, it has made me a better leader.”

Chaos-elimination leadership

In this segment of her presentation, Cheney turned the mirror on herself and her peers with these two questions:

• Are you the the person causing chaos in your department?
• Do you need to control every decision that is made by your employees?

An answer of yes to either or both of those questions is probably not a good thing.

Along with those two questions she suggested the importance of presenting a crystal-clear strategy, having a direct line of sight from the top to the people in the trenches, and knowing your role in a successful strategy.

Some employee facts

Cheney also offered some facts worth noting, obtained from Dale Carnegie Training and Daily Infographics, February 2014:

• $11 billion is lost annually due to turnover.
• 71% of workers are not fully engaged in their work.
• 80% of employees are dissatisfied with their direct manager.
• 70% of employees who lack confidence in senior leadership are not fully engaged.
• Revenue is 2.5 times higher in companies with highly engaged employees.
• Highly engaged employees are 87% less likely to leave than disengaged counterparts. MT


5:52 pm
November 15, 2016
Print Friendly

SAP Material Masters: How do they integrate with the rest of your ERP system?

Someone once told me that the material master is the center of the universe in SAP. There is much truth to that statement.

randmIn dealing with multiple maintenance plants all over the country, the biggest issues I see after an implementation are how the material masters for maintenance were set up. Material masters integrate with every process of your ERP system. They can control a portion of your financials, affect your work orders, wreak havoc with purchasing, and create a situation in which your company is non-compliant with regulations on PSM (process-safety management). What follows is a common, yet critical, issue regarding safety stock.

Material 123 has a safety stock set in the material master (transaction MM02) for a quantity of nine. Someone at the site puts in a reservation for a quantity of 10. We will call this individual person A. Person B puts in a reservation for the same material for a quantity of nine. Person B gets the material delivered from the storeroom to the required area on the same day that it has been reserved. Person A waits for an extended period and doesn’t understand why he is not receiving the materials he ordered.

Material 123 has a safety stock set in the material master (transaction MM02) for a quantity of nine. Person A wants 10 units of Material 123. Person B wants nine units.

Material 123 has a safety stock set in the material master (transaction MM02) for a quantity of nine. Person A wants 10 units of Material 123. Person B wants nine units.

There are two problems with this scenario.

The storeroom receives all reservations for materials through a transaction, MB25. This screen shows by work order, cost center, or requester what material is being requested and the delivery date for which the requirement should be filled.

All reservations for materials are received through a transaction, MB25. This screen shows by work order, cost center, or requester what material is being requested and the delivery date for which the requirement should be filled.

All reservations for materials are received through a transaction, MB25. This screen shows by work order, cost center, or requester what material is being requested and the delivery date for which the requirement should be filled.

If the inventory clerk does not have the requirements date set in order, it is possible that orders are filled out of order. In this situation, person A should have had the requirements filled before person B.

The second problem occurs when MRP (materials-requirements planning) is running. SAP will see that the safety stock is set at a quantity of nine. Therefore, that is all that the system will ever try to keep in stock. When A and B entered a total requirement quantity of 19, the quantity of nine in stock will be issued and MRP will create a requisition for the remaining amount. However, this will still produce a deficit, as the safety stock requires nine.

To assure persons A and B receive the units they need, the site should run transaction MC44. This will generate the exact number of inventory turns in a period, per material.

To assure persons A and B receive the units they need, the site should run transaction MC44. This will generate the exact number of inventory turns in a period, per material.

To fix this problem, the site should run transaction MC44. This will generate the exact number of inventory turns in a period, per material. It shows that the material is consumed at a rate higher than the safety stock setting and will allow the analysis and data to confirm that the safety stock should be increased to a number that will meet the site’s requirements.

Ensuring that safety stock is set to an accurate number can reduce the amount of purchase requisitions created and assure that orders are filled in a timely manner to meet requirements and, in parallel jobs, will not be delayed. MT

Kristina Gordon is SAP Program Consultant at the DuPont, Sabine River Works plant in West Orange, TX. If you have SAP questions, send them to and we’ll forward them to Kristina.


5:26 pm
November 15, 2016
Print Friendly

Heed Design Letters When Replacing Motors

By Mike Howell, Electrical Apparatus Service Association (EASA)

Too often, replacement specifications for three-phase squirrel-cage induction motors cover only basic nameplate data such as power, speed, voltage, and frame size, while overlooking other important performance characteristics such as the design letter. This can lead to misapplication of a motor, causing poor performance, inoperability, or failures that result in unnecessary downtime. To avoid these problems, familiarize yourself with the following speed-torque characteristics and typical applications for design letters that NEMA and IEC commonly use for small and medium machines (up to about several hundred kilowatts/horsepower).

NEMA Designs A and B, IEC Design N

• Characteristics include low starting torque, normal starting current, low slip, and relatively high efficiency. (Slip, the difference between rotor speed and synchronous speed, is necessary to produce torque. As load torque increases, slip increases.)
• NEMA Design A typically has higher starting current and lower maximum torque than NEMA Design B and IEC Design N.
• Typical applications include fans, pumps, and compressors where starting torque requirements are relatively low.

randmNEMA Design C, IEC Design H

•Characteristics include high starting torque, low starting current, and medium slip (achieved by using a double-cage, high-resistance rotor design).
• The high-resistance rotor results in greater losses at normal operating speed and, consequently, lower efficiency than NEMA Designs A and B and IEC Design N.
• Typical applications include conveyors, crushers, reciprocating pumps, and compressors that require starting under load.


NEMA Design D

• Characteristics include very high starting torque, low starting current, and high slip.
• The robust rotor design typically incorporates a single-cage with brass alloy or high-resistance aluminum alloy rotor bars.
• The high-resistance rotor results in lower efficiency at the operating point.
• Typical applications include high-impact loads, sometimes involving flywheels, such as punch presses and shears. These motors see significant slip increases with increased torque, which, for example, can facilitate delivery of kinetic energy from the flywheel to the impact.

Using the wrong motor design for an application is another way of spelling trouble. For example, replacing a NEMA Design D motor in a shear application with a NEMA Design B unit can result in rapid failure, even if the power rating of the machine is doubled.

When replacing motors, give your supplier as much information as possible about the existing motor and application. If you need more information about design letters, see NEMA MG-1 and IEC 60034-12. MT

Mike Howell is a technical support specialist at the Electrical Apparatus Service Association (EASA), St. Louis. EASA is an international trade association of more than 1,900 electromechanical sales and service firms in 62 countries that helps members keep up to date on materials, equipment, and state-of-the art technology. For more information, visit


7:57 pm
November 14, 2016
Print Friendly

A Good Day for a Maintenance Manager


By Dr. Klaus M. Blache, Univ. of Tennessee Reliability & Maintainability Center

Arriving at work in the morning, my personalized digital device (attached to my wrist) provides a schedule of the day’s activities. Moving toward my desk activates my computer and turns it on. When I sit down, the retinal scan confirms my identity. I see that one of my technicians is printing a 3-D temporary part. Another is using a drone to conduct a roof and pipe inspection for a system-reported leak and checking construction progress.

A third technician is using a maintenance-assist robot to perform simple, repetitive maintenance tasks. (This robot can also be used in the emulate mode to copy the exact movements of the technicians for more complicated and heavy work.) The technician is using safety/training glasses that provide step-by-step visual directions. In the actual work she performs, an enhanced ergonomic glove provides additional gripping strength to avoid carpal-tunnel injuries.

From the Enterprise Management System (EMS), I get a quick overview of production, reliability, and maintenance. By applying Weibull analysis, the software provides a chart showing how much can be gained by improving production efficiencies and how much can be gained by improving reliability. This information is supplemented by a computer-generated verbal summary that can be used in place of or with the charts. The EMS data are integrated with a “learning system” that makes some decisions (within defined parameters) and reports on those decisions and underlying reasons/rationale. Production processes are statistically controlled.

As soon as there’s evidence of out-of-bound parts, they’re corralled for recycling. This, however, doesn’t happen often. Process capability is typically better than six sigma (3.4 defects/million). Any small deviations from cycle time are monitored for each piece of equipment to enable timely maintenance interventions and assure throughput requirements. Since the machinery and equipment are purchased with significant “design-in” reliability and maintainability specifications, optimal MTTR (mean time to repair) times are realized. Among other things, those design-in specifications include items such as color-coded and easily accessible lubrication points and known replacement parts that are engineered for quick disassembly and positioned to not affect more expensive parts at removal.

Purchasing is responsible for life-cycle costing (LCC) and then measured on life-cycle performance of machinery and equipment (M&E). Component and M&E providers are selected based on best historical MTBF (mean time between failures) and reliability growth. Every part has an RFID (radio frequency identification) tag, making it easier to find parts and perform Root-Cause Analysis (RCA). This is ongoing within the learning system. Once a week, the global continuous-improvement team meets virtually (using 3-D imaging to view all participants) to make decisions with data and recommendations from the learning system. The main purpose of this meeting is to address needed decisions that are beyond the programmed scope of the software.

Everything that happens in the plant is related. A reduction in reactive maintenance improves safety. Fewer repairs mean greater throughput and lower costs. Senior leadership wants to immediately reduce maintenance cost, but data points show that safety, throughput, and cost would all be negatively affected if that were to happen. Management reconsiders and takes another approach.

Looking back, do you ever wonder how yesterday’s plants made a profit? My grandfather used to tell me stories about large backlogs, high levels of reactive maintenance, sporadic use of predictive technologies, and excess inventory. It’s hard to believe that anyone could run a business that way. Fast forward to today. While we may not yet be ready for the scenario described in this article, don’t hesitate to think big. Great things happen when individuals want to make a difference, take some risk, and tenaciously implement. MT

Based in Knoxville, Klaus M. Blache is director of the Reliability & Maintainability Center at the Univ. of Tennessee, and a research professor in the College of Engineering. Contact him at


9:09 pm
November 9, 2016
Print Friendly

Move From Raw Data to Smart Work

Manufacturers are flooded with data. Here’s some guidance to help you put that data into context, understand it, and make it work for you.

By Tim Sowell, Chief Architect, Software, Industry Solutions and Stan DeVries, Senior Director, Solutions Architecture, Schneider Electric

In today’s “flat world” of demand-driven supply, the need for agility is accelerating at a rapid rate. This is driving leading companies to transform their operational landscape (systems, assets, and culture) to a “smart work” environment. This move toward agility transforms thinking from a process-centric view to a product and production focus, requiring a dynamic, agile work environment between assets/machines, applications, and people. The paradigm shift from the traditional “lights-out manufacturing concept” of fully automated systems to an agile world of dynamically planned yet scheduled work requires:

• automated embedded intelligence and knowledge
augmented intelligence using humans to address dynamic change.


At the foundation of this shift is an environment in which a worker can have the mind space to understand the larger changing situation and make augmented, intelligent decisions and actions. To provide this, data are transformed naturally into operationalized information upon which decisions can be made, then combined with “tacit, applied knowledge,” providing incredible value when taking operational actions.

The explosion of information across industrial operations and enterprises creates a new challenge—how to find the “needles” of wisdom in the enormous “haystack” of information.

Listen to MT editorial director Gary L. Parr’s interview with one of the article’s authors.

One of the analogies for the value and type of information is a chain from data, through information and knowledge, to wisdom. In the industrial-manufacturing and processing context, it may be helpful to use the following definitions:

• Data: Raw information that varies in quality, structure, naming, type, and format.
Information: Enhanced data that has better quality and asset structure and may have more useable naming, types, and formats.
• Knowledge: Information with useful operational context, such as proximity to targets and limits, batch records, historical and forecasted trends, alarm states, estimated useful life, and efficiency.
Wisdom: Prescriptive advice and procedures to help achieve targets such as safety, health, environment, quality, schedule, throughput, efficiency, yields, and profits.


To illustrate this transformation, imagine driving along an unfamiliar California freeway in a GPS-enabled rental car:

• Data: The GPS knows that I am on a freeway, traveling at 80 mph.
Information: It is “situationally aware” that I am heading south on the I-405 freeway.
Knowledge: It works with other services to determine that 10 miles ahead the traffic is stopped, and provides me with a warning that I will be delayed due to a traffic hazard. It has combined traffic knowledge with my location, speed, and destination to provide timely, advanced decision-support knowledge that I can use to potentially take an action.
Wisdom: The GPS provides two alternate routes, giving me the time and characteristics of each route.

Without requiring me to take my eyes off the road and use an A-Z directory, I have been:

• warned ahead of time of an issue that could prevent me from reaching my destination on time
given two alternatives and the information necessary to realize my goal with either choice.

There is no reason why this same transformational journey from raw data to wisdom cannot apply to manufacturing operations.

Avoid the Pitfalls

Many companies stand on the edge of a data swamp that is growing quickly, with the Industrial Internet of Things and Smart Manufacturing providing access to an exponential level of additional data from their industrial value chain. This data influx can either bog down growth or, if leveraged to achieve proportional knowledge and wisdom, create a new level of operational agility. The Fourth Industrial Revolution (Industrie 4.0) provides a framework for leading this ubiquitous transformation. Major industrial organizations are now realizing the incredible value that can be extracted from data and are combining time, resources, and technologies, such as big data and machine learning, with a new evolution in operational culture to leverage this potential.

Those operating in manufacturing have been living for decades with vast amounts of data located in historians, equipment logs, and across their extended supply-chain network. Data, in and of itself, is not of much value. The same can be said for reams of paperwork that document best practices—it isn’t of much value sitting on a desk or in a document-management system.


Operational Data Management

We all talk about the ability to generate data from different devices. This can be valuable, provided there is some enterprise integration. But, can you really have effective information if there is no context?

The challenge is how to gain this context and then sustain it over several devices (things) without having a significant impact on those devices. In other words, how does one add, remove, and evolve devices? This requires an operational data-management system that is a “yellow pages” of the system, providing the context and relationship between devices and the operations.

An operational data-management system provides the ability to register new devices and data input, while maintaining the detail in the device, and then provide the bigger operational process alignment. This provides the association, which is alternate naming of that device so other applications can find and interact with it. Often, other systems and machines have a different outlook on the process and will use different naming and references for the device. An operational-data-management capability provides this association and ability to align many devices without a change in the underlying applications or devices.

From a data to information point of view, it provides the context needed to gather data and transform it into information, so that big-data analysis and other tools can be applied and convert that information into knowledge. Knowledge provides a pattern to ensure that contextualized operational data (production, quality, machine status) is integrated with templated collaboration activities, and ultimately broad value/supply-chain management.

Without this, companies have a real risk of gathering significant amounts of data and being unable to create the associated proportion of information, knowledge and, eventually, wisdom. Knowledge allows companies to put architectures and systems into place and gain contextualization while providing the plug-and-play ability for devices to be added to the solution.

The cost to store and share data has dropped significantly, and a simplistic expectation is that, although storage is growing by a factor of millions in only a few years, the pattern illustrated in Fig. 4 evolves.


Although the pattern might seem to be logical, it is actually a nightmare, because it becomes much harder to discover and translate knowledge and wisdom from another operation, especially in another location, to the local needs. But there is a solution.

To understand the problem better, let’s consider that knowledge includes context. This context begins with local details, including time, location, process or machinery configuration, raw materials, energy, and products being processed or produced. It is already valuable to have wisdom to achieve and sustain best performance for the community, customers, and the corporation. This local context only needs to know its immediate information, if it has enough wisdom.

Now let’s consider what happens when a single site, a fleet of similar sites, or an enterprise have numerous similar operations. How can local wisdom be enhanced by using wisdom from other operations? Solving this problem is important for operations transformation, such as operating physical assets as one (in a chain or as peers) or by supporting multiple operations with a flexible team of remote experts.

One approach to solving the knowledge proliferation problem is to take advantage of a methodology used in distributed databases, where a technique called “federated information” is used. This technique is especially valuable in industrial operations-management architectures. Federated information does not change the local information’s naming or structure, but provides multiple translations, across the database, for multiple similar structures and for multiple contexts such as what financial, technical support, scheduling, quality, and other functions require. It is an alternative to the fragility and complexity of attempting to force a uniform and encompassing naming and structure in an attempt to satisfy all applications and users.


The same approach can be applied for wisdom. Currently, hobbyists and enthusiasts around the world share wisdom for restoring cars, making furniture, playing a musical instrument, and gardening, as examples. Anyone with no experience at all can ask “Where do I get started?” and most respondents will provide some type of advice. In the same forum, experts can share wisdom that is valuable and understandable by them at their level of experience. This wisdom is extremely decentralized, and the experts are providing the local and regional translations.

In the industrial-operations environment, federating wisdom is partially automated by expanding the local context, including information about adjacent operations, about the chain or peers if these operations are being managed as one, and then knowledge is expanded by applying the context of group targets and performance.

Some enterprises have hundreds or as many as tens of thousands of similar operations supported by dozens or fewer experts. Discovery of wisdom is greatly enhanced by maintaining an architecture that enhances local context without modifying or attempting to force burdensome structures on local operations.

Empowerment through Wisdom

So how does operational intelligence/industrial analytics and the movement to wisdom relate? They are different, but all are related to empowerment of an operational workforce to make earlier decisions and take informed actions. One of the big drivers for platforms is to manage variance. We talk about supervisory, MES, information and simulation platforms but we also must have a people platform that covers:

• collaboration between people
activity hosting, including embedded information/knowledge and associated actions
transformation of information to situation awareness for the user who is interested/ interacting
management of operational work between team members

This people platform will mitigate workforce turnover by abstracting the different skill and experience levels with embedded applied knowledge (wisdom), so the experience is now in the system. This is a key concept for operational transformation.

Industrial analytics provide the shift from the past through the present and into the future, based upon high-fidelity models gained from experience. It provides a new dimension to worker tools and transforms the decisions that they are about to make. Industrial analytics combine the future, providing answers to what will happen with the recommended actions to take.

This also provides the answer to “What should I do next?” with experience, forethought, and understanding. Operational intelligence furthers decision making by providing screens/presentations of the situation or known questions with context and awareness.

Operational intelligence provides the worker with an understanding of “now,” where he/she is, and what the future holds, simply and clearly. Increasingly, there is demand for this type of operational window and views. It is not analysis but practical information around the current situation and immediate future. Just a simple view of the task or question provides the clear awareness and actionable answers.

Are these different experiences? No, they are all functional value expansions on each other, and should be seen as building blocks on the road to providing an operational execution knowledge platform, with built-in experience. In other words, they provide a foundation for absorbing turnover and transition in the workforce while maintaining operational consistency and efficiency.

There is a journey to smart work that organizations are now following, much like the first continuous-improvement initiatives that began nearly 50 years ago, such as Lean, Six Sigma, and TQM. Operational execution puts it in the systems and culture that enable proportional growth in knowledge and wisdom so that they can address the dynamic world of smart work. The only difference is that operational data-driven systems can now be a part of these continuous-improvement strategies.

Manufacturing is in a constant drive to improve performance, and transformation of work has become the main method to achieve and sustain this. Higher capacity or more efficient machinery and processes aren’t sufficient anymore. Manufacturers with agile and cyclical operations need a method to remain cost competitive during the lower throughput periods, yet remain responsive enough to take full advantage of high throughput or high margin conditions.

Implementing systems that transform work using higher value information and reliability change when, where, and/or how users make decisions, and is the foundation for this next level of improvement.

Operational transformation through smart work is a journey, and technology is only one of the key elements. The user culture must adapt, similar to the previous waves of quality, safety, health, and environmental improvements. The journey advances with work-process improvements, as applied to sections of a site or an entire site. Existing software must be assessed in terms of delivering knowledge and wisdom and supporting mobile and traveling workers, with the goal of significantly reducing the skill and effort required to maintain them. The journey is worthwhile, practical, and essential for manufacturers not only to stay competitive, but also to thrive.

Tim Sowell is vice president of Software System Strategy at Schneider Electric, Lake Forest, CA. In this role, he leads the direction and strategy for the company’s Wonderware software portfolio. Stan DeVries is senior director, Solutions Architecture at Schneider Electric Software. He works with customers to implement innovative, reproducible data-architecture patterns and reference architectures.