ServiceExperts™ is a series of contributing articles from recognized industry professionals offering their thoughts, viewpoints and opinions on the latest trends impacting the service industry. Thomas Maiello has over 20 years of hands-on experience in new product introduction, global technical and product support, field service management and improvement strategies, and services technologies. He is currently the Director of New Product Introduction at Varian Medical Systems (a Siemens Healthineers company). His focus on strategic data-driven programs to drive improved serviceability has resulted in significantly improved services profitability for several companies. Before joining Varian, Tom managed global support teams at Morpho Detection, Smiths Detection, and KLA. Previously, Tom was enlisted in the U.S. Air Force, and earned a commission in the U.S. Army. Tom holds an MBA from Santa Clara University, an MS from University of Southern California, and a BA from State University of New York.
…aircraft engines are designed to work, not to be worked on.”
Mike Gentile, Pratt & Whitney Technical Representative, 1987
The Goal
The goal for most service organizations is a world of 100% machine operational availability, delivered in a highly cost-effective manner. This outcome is enabled by “Smart Machines” and components capable of self-monitoring, self-diagnosing, and the self-healing of imminent failures, with sufficient time to pre-position staff and parts, as necessary, to intervene without causing an operational impact to customers.
The transition from reactive, “break-fix” maintenance to preventive and pre-emptive maintenance will result in the following:
- Improved customer experience, through increased machine availability and planned downtime
- Reduced service costs and improved profitability of the services organization
- Minimized customer disruptions through elimination of unplanned service calls
The goal is to install machines with the minimum of effort and cost and once installed, to pre-emptively troubleshoot and repair machines remotely, exploiting current and to-be-developed-and deployed standardized services technologies (DfX; self-monitoring; health checks; self-calibrations; machine alerts; pre-emptive prescriptive call creation, assignment, and closure; embedded diagnostics, and feedback loops), as much as possible.
Embedded hardware and software diagnostics greatly reduce the need for on-site visits and replacement of fully functioning components, allowing service technicians to replace only components that have failed a pre-initiated self-test.
If required to visit the customer site, the goal is for the service technician to arrive with a thorough understanding of the machine issue, and to have all required replacement parts, tools, and expertise readily available to support and resolve the issue.
Through smarter product design (a.k.a. concurrent Engineering, DfX, smart design) companies should develop the tools and capabilities necessary to support a data-driven services business, where customers are targeted with tailored services offerings, delivered nearly invisibly, and resulting in zero operational downtime, high customer satisfaction scores, and lower services costs.
This goal will require an on-going culture shift to become a proactive service organization: an integrated, systems-based data-driven organization, spanning the services business.
Current View: Types of Maintenance
Maintenance of large, complex, expensive, capital equipment installed at customer sites generally falls in to one of three categories:
- Reactive maintenance: “corrective” maintenance that is generally characterized by the “break/fix” mindset.
- Preventive maintenance: proactive maintenance without intelligence, that is prescribed by completion of PM checklists, monthly or quarterly or annual PMs, and without any consideration for “use of machine” characteristics.
- Predictive maintenance: anticipatory maintenance that monitors the performance and condition of machines during normal operation, a known state, to reduce the likelihood of catastrophic failures and address machine issues pre-emptively.
Through on-going, real-time machine performance feedback; comprehensive and standardized data extraction, analysis, and modeling; and on-going implementation of the initiative across all product lines, companies can shift the service model and deploy a new and disruptive approach for customers, improving overall availability and customer satisfaction scores while simultaneously improving service profitability.
It Starts with the Data
It starts with the data: accurate, timely, complete field service data that answers the following questions:
- How are machines failing in the field?
- Which failures are the most expensive (in both cost and time)?
- Which failures are the most frequent?
- How many of those failures could be predicted?
- Could the failures be mitigated through better preventive maintenance?
- Which failures are the most problematic to effectively troubleshoot?
- What can we do with the data (machine performance logs, calibration logs, log files, …) that the machine currently generates?
- What requirements are being levied in new (and upgraded) product design to address these questions/failures?
- Do the preventive maintenance checklists reflect machine operation and machine failure characteristics?
- Are frequent, on-going installation and field tasks being automated?
A Needed Culture Shift/Transformation
Culture Shift in Service: Technicians will need to shift from the traditional “break-fix” mindset to the preventive/predictive maintenance mindset. Provide the service technician with a prescriptive service call/task: “Check coolant level in subsystem 3” or “Check calibration of X-ray tube”, or “Run Bad Pixel Map on Detector #8”. Offering general guidance or tasks (i.e., “Check machine for faults”) does not provide sufficient direction or granularity to pre-emptively resolve the machine issue.
Culture Shift in Installation: Installation teams are required to plan, stage, install, qualify, and obtain customer acceptance in the shortest time possible to minimize installation costs. Machines need to be designed with ease of installation at the forefront. Developing automated test and upgrade scripts, configuring hardware and software for late-stage final configuration, and automating most of the tedious post-installation calibration and checkout processes will greatly reduce installation time and costs.
Culture Shift in Technology: The technology necessary to enable the transformation of the field service engineer will include developmental and in-place services technologies. These include tools such as augmented reality (AR), new sensors and instrumentation evaluation, secure remote connection to the factory (both machine and technician) to collect, archive, and analyze machine performance data to trigger pre-emptive service events, and components, subsystems, and systems that self-monitor and “call home” pre-emptively, before failure/machine shutdown.
Culture Shift in Engineering: The most important questions that designers and design engineers must constantly ask and assess include:
For the board, component, assembly, system, machine that I am designing …
- How can it fail, and what type of flags, embedded diagnostics, self-checks, and other alerts can I build in to pre-emptively address and resolve the issue before it affects the customer?
- How should the PM checklist be written to ensure that critical failures are monitored and addressed during planned maintenance? What type of machine odometer(s) do I need to design into the machine to facilitate usage-based maintenance and critical component reliability monitoring?
- What components/parts are available that reduce life cycle cost?
Life Cycle Cost Modeling
Management has to make resource tradeoffs. Every time a design engineer is reassigned to work on a service improvement, a customer feature is being de-scoped. Work with the finance organization to develop and vet a Life Cycle Cost Model which calculates the improved lifecycle profitability of the service feature. This enables service leaders to prioritize, and rank order service improvements on those that offer the biggest “bang for the buck”. Evaluate both improved profitability and Return on Investment (ROI) to arrive at a package of service improvements.
Putting it All Together: End-to-End Solution
The successes in reducing service costs and improving customer outcomes lie in the ability to wrap operational performance around the capabilities of the equipment. The key to achieving the overall goal is developing and implementing an integrated and comprehensive approach across all services and engineering disciplines, as well as all product lines, rather than attempting a fragmented, disconnected approach.
Approach or “How-To”
- Benchmark related and unrelated industries/companies.
- Start small with one product line in one region or one customer.
- Baseline the installed base.
- Determine which issues are the most expensive to resolve in dollars and hours.
- Start with the data the machines are already generating and capturing.
- Work with Engineering. Show them the data and develop solutions collaboratively.
- Work with Finance to develop and apply Life Cycle Cost Modeling to possible solutions.
- Implement solutions, collect feedback, and drive feedback.
- Focus on continuous improvement.
In Summary
Product development collaboration, and on-going and continuous feedback in the form of field data (machine performance data, Field Service Reports with all required timestamps, parts usage, First Time Fix Rate, etc.) will drive the transformation. Continuous evaluation of service requirements, technologies, and processes early in product development will enable companies to accomplish the goal of improved customer outcomes in parallel with measurable improvements in services profitability.
You can learn more about designing for serviceability and how your organization can leverage field service data to improve customer and business outcomes, listen to the inService™ Podcast episode, “Improving Serviceability & Lifecycle Profitability,” with guest Thomas Maiello here.
