Using Service Lifecycle Management (SLM) to Support Your Customers While You’re Servicing Their Equipment

Every day you deal with a multitude of customers who vary by type, size, installed base, usage, personality and everything else that ultimately differentiates one customer from another. However, one thing always remains constant – their business systems and equipment are critically Important to their day-to-day business operations. Despite this common thread that runs through virtually all of the customers you support, it is still important to recognize that each customer account will likely be different in terms of:

  • The various types, brands, models and numbers of units they have installed at their respective sites;
  • The ages of the individual units that are covered under their various Service Level Agreements (SLAs), or supported viaa Time & Materials (T&M) basis;
  • The usage patterns of the equipment at their individual locations (i.e., continuous intermittent use; single vs.multiple shifts; simple vs.complex multifunctional peripheral applications; and so on);
  • The volume, capacity or throughput they regularly execute; and
  • Many other unique and/or specific differentiators.

For some of your customers, their equipment is an integral component of what they do on a day-to-day basis. Customers in all industry segments, whether it be legal, financial, medical, real estate, government, or other highly-demanding markets, will tell you that their systems and equipment are essential to their business operations, and that when their equipment is down, their production is severely affected. For some, even a small piece of connected equipment may be the only means they have for providing their customers with a receipt, order confirmation, or other important transaction-generated documents. In fact, for many in the latter category, their reliance on the equipment you support may be even more critical to them (at least on a relative basis).

Regardless of the specific industry market segment or type of customer, there will always be a basic level of reliance on the business systems and equipment they have installed at their facility. In addition, you will find that your customers will also be relying heavily on your organization to ensure that their equipment is always up and running as required – and as expected. As such, it is important to recognize that in the customer’s mind, if the equipment is not working optimally – regardless of the technology that may have been built into it – it is worthless.

Since there is just so much that the customer is either inclined or permitted to do in order to get the equipment back in working order following a failure, in most cases, your field technicians will be the sole entities that they can count on to make that happen (that is, aside from remote monitoring and diagnostics, etc.). Accordingly, they will need to approach the servicing and support of the equipment with a great deal of professionalism and responsibility. Customers usually do not care whether the cause of an equipment problem is due to a hardware or software failure; a paper jam; or whether it was the unit’s fault, their fault, or nobody’s fault in particular. All they know is that when they needed to use the equipment, it simply did not work.

This is typically where the organization’s field technicians come into the picture. In many cases, they represent the only “real” physical manifestation of the service and support that keeps their equipment up and running – or at the very least, they may represent their first line of service and support defense. Your customers may rely heavily on the equipment itself to support their day-to-day business operations; but they rely even more on your organization and your field technicians to ensure that the equipment can continually do what it is supposed to do.

This is a unique area where most services organizations – and their dealers and distributors – can use some help! The good news is that there is a Service Lifecycle Management (SLM) software solution available for users in every industry, size and geographic coverage segment. The implementation of anSLM solution can provide a comprehensive set of integrated business solutions that empower strategic initiatives while driving tactical execution.

Companies that install, repair, and maintain business systems and equipment can increase their competitive advantage, grow top-line revenue, and bolster bottom-line profitability through the use of an effective SLM solution. Among the basic features and benefits of SLM functionality for a typical Field Services Organization (FSO) may best be summarized as follows:

  • Comprehensive Contract and Service Level Management
  • Service and Sales Integration
  • Increased Help Desk/Contact Center Effectiveness
  • Field Service Efficiencies

Comprehensive Contract and Service Level Management

Through SLM, customer contracts and Service Level Agreements (SLAs) can be structured in ways that best fit the business, as well as the businesses of their respective customers. Key items such as maintenance and repair service; preventative (or predictive) maintenance; remote monitoring, diagnostics and repair; and draw-down contracts can all be easily established and managed. As such, the organization’s services management can be assured that all of the obligations of its customers’ SLAs are well-planned for – and met – and that all of its mission-critical commitments to the customer are being honored.

In this way, services revenues are maximized, and there is little risk of experiencing lost revenues. Company representatives can quickly and easily verify both the customer and vendor entitlements, thereby eliminating any costs that might otherwise be associated with providing customers with parts, consumables or services they are not entitled to under the terms and conditions of any existing warranties or contracts. This also ensures that any and all dealer claims will be quickly processed.

Service and Sales Integration

The Service and Sales Integration functionality of an SLM software suite can be relied on to enable the manufacturer’s and dealer organizations’ field service technicians and contact center personnel to more thoroughly service the company’s accounts, while also driving increased revenue in the process. By placing intuitive, easy-to-use sales tools into the hands of the appropriate service employees, the number of new opportunities to up-sell and cross-sell equipment, parts and consumables to existing customers will increase multifold.

The organization’s service technicians are out in the field every day talking to, and interfacing with, its customers; why not also provide them with the tools and resources they can use to close – or at least open –additional sales opportunities within this virtually captive customer base!

Increased Help Desk/Contact Center Effectiveness

SLM can also allow the organization to increase its call handling efficiencies, especially in the areas of first-call resolution and call avoidance rates. This will ultimately result in the lowering of internal service costs, and commensurate improvements in existing levels of customer satisfaction and retention. In many ways, business systems and equipment services have been somewhat commoditized over the years, and the only way that one services organization (or its dealers) can establishment a competitive advantage over another is to differentiate (i.e., improve) the way in which they support the customer base after the initial sale.

The best way to do this is to provide superior levels of help desk and call center support empowered by a robust SLM capability. By arming your call center personnel with accurate and up-to-date customer and installed equipment base information – be it entitlement, configuration, or marketing campaign data – the organization will be able to greatly increase its ability to sell, cross-sell, and upsell its entire portfolio of products, services, parts and consumables.

Field Service Efficiencies

Leveraging the field service automation tools inherent in the SLM software allows the organization to optimize its field force capacity utilization, resulting in significant operational efficiencies as field technicians quickly become empowered to increase revenue generation and recovery. By streamlining and managing the invoice process, billing cycles will be lowered, as will other key areas, such as Day Sales Outstanding (DSO), etc.

These improvements will almost immediately go directly to the bottom line as you will be able to manage your cash flow and receivables much more effectively. Similarly, by streamlining and managing your service inventories (such as trunk stock) more effectively, you will also be able to realize significant inventory cost reductions.

What many OEMs and dealer organizations seek is an end-to-end, enterprise-wide SLM solution that addresses the complete equipment/service lifecycle, from lead generation and sales quotation, to service and billing, through asset retirement. They are looking for a solution that both integrates and optimizes the critical business processes that all services organizations have to face with respect to providing their customers with the levels of service and support they require.

Services organizations that provide their customers with any combination of products, parts, services and consumables must be able to not only fix the customers’ equipment, but to fix the customer as well; however, the ability to do so may vary greatly from one organization to another. However, the most successful organizations will ultimately be the ones that have the right mix of management, personnel, tools, resources and solutions (i.e., Service Lifecycle Management), all working together to provide their customers with the levels of service and support they require – and expect!

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Why Knowledge Discovery? Your Organization May Be Sitting on a Goldmine of Data!

These days, more than ever, businesses are operating in data rich environments. Data emanating from every-day business operations, sales and customer account activities, service call activity, financial and economic transactions, regulatory reporting and all the other business-related events of the world are routinely captured and stored in databases. Existing global databases are adding terabytes of new information daily. Every moment of every day bank transactions and electronic funds transfers, point-of-sale systems, hospital tests and procedures, factory production lines, airline reservations, service calls and even electric meters and gasoline pumps are creating digital records that are stored somewhere in a database.

The vast majority of these data, however, will never see the light of day. More often than not, these data will be stored for a specified period of time, in some cases as required by law, and then “purged” to make room for more current data of the same kind. This process is likely to repeat ad infinitum, each time replacing the “old” data with “new” data until the “new” data itself becomes “old” and must once again be replaced. Yet in many cases these data can represent a “rich ore” of valuable information and knowledge about the domain from which it has been taken.

What better source is there to learn about patterns of customers’ preferences and buying habits than from the customers themselves; not just what they tell you they need or like in a Customer Needs and Requirements/Satisfaction Survey, but what they actually buy. What better source is there to learn about equipment failures and service requirements than from the equipment itself; not just from what your field technicians tell you, but directly from the equipment. What better source is there to learn about the risk in lending or extending credit than from your business’s own financial successes and failures; not just from what your banks or creditors tell you, but from your own financial experiences, both good and bad. The list goes on and on.

Organizations are always searching for knowledge that can advance their cause and keep them abreast of the market, anticipated trends and the competition. Marketing managers would love to know what makes their customers “tick”. Manufacturing managers would do anything to find out how they could improve the quality of their products, even by just a fraction of a percentage. Not to mention the securities traders who would “sell their corporate souls” just to keep a half-step ahead of the pack in being able to detect a change in trends or receive an “early warning signal”.

Oftentimes the answers to these questions are contained in the data that businesses routinely collect, store and discard from their ever-growing databases. Many companies have already recognized the potential of this source of knowledge and have invested substantial effort and significant amounts of resources to uncover the precious knowledge “hidden” in their data. Among the various emerging technologies being utilized, some employ a combination of both the traditional and newer, more “exotic” paradigms in a field known as knowledge discovery, or database mining.

Credit card issuers are using advanced knowledge discovery methods to identify usage patterns that indicate fraud in an attempt to execute more effective fraud avoidance systems and, ultimately, minimizing their exposure to losses. Warranty management organizations are using similar methods to detect fraud in an attempt to reduce their traditional losses in this area.

Digital marketing companies use related methods to create more targeted and effective lists for the products and services they are promoting to improve their overall effectiveness. Automotive companies use the same techniques to discover patterns of failures and corresponding information to incorporate into the proprietary knowledge bases that they distribute to their authorized dealers and licensed mechanics. Many more applications of a similar nature span across businesses and industry segments of all types under the banner “Let The Data Work for You”.

The analogy of database mining to quarry mining is very appropriate too. In ore mining the process goes through tons and tons of dirt in order to extract one precious gram of gold. Similarly, in database mining, one may also need to go through very large quantities of data just to get to the “one piece of information that makes it all worthwhile”.

However, it is typically at this point where traditional analytical methods and approaches have failed, and the businesses that have historically used them have pretty much “given up”. Going through a large “mine” of raw data only to transform it into a somewhat smaller pile of statistics or summary tables is of very little use and often quite discouraging, and questions like; “What do the data mean?”, “How can we make use of it?”, and “How does it relate to our bottom line?” are all hard to tell.

Traditional statistical methods make assumptions about the data used and require a model in the form of an hypothesis that one can then either accept or reject. Quite often the data do not conform with the assumptions and there is no model. In addition statistics excludes from its realm many forms of data that are quite common in the expression and representation of some of the phenomena that are around us. To overcome these drawbacks, the process of extracting knowledge from data has turned to machine learning techniques.

Machine learning techniques, developed under the umbrella of Artificial Intelligence (AI), were originally patterned after a unique human intelligence trait – the ability to acquire and create new knowledge. From this basis, new and highly sophisticated AI techniques have been developed using a broad array of disciplines and strategies, and reflecting various levels of success.

In later stages of research some of these techniques have been incorporated into a knowledge acquisition process which represents a critical step in the process of building and maintaining knowledge-based systems. Prior to the development of such a process, this was typically the area that represented the largest “bottleneck” in terms of actually having the capability of building and using knowledge-based systems in practical business applications. Moving from this point forward (i.e., to expanding the use of learning mechanisms to database mining knowledge discovery), the distance is very short.

Today, knowledge discovery tools and methods employ a broad range of technologies and methodologies. Neural networks are probably the best known and most widely used approach to machine learning. The technology is quite versatile, relatively mature and has been used very successfully in a broad array of applications ranging from the screening of credit card applications, to placing geographically-based advertisements in national magazines, to reading handwritten addresses and routing the mail. Other discovery methods are based on technologies such as information theory, fuzzy set theory, rough set theory, nearest neighbor metrics and others.

Finally, with respect to the question “Why knowledge discovery?”, the answer should be more apparent by now. Your organization may be sitting on a “goldmine” of data which could be converted into useful knowledge – knowledge that can be used to help you focus your strategic and marketing planning efforts; monitor and improve the quality of your production and service delivery processes; and explain your customers’ sensitivity to your competitive pricing structure, customer service performance, brand name recognition, advertising and promotional campaigns or anything else you would like to learn about the markets in which you operate.

Many organizations have already recognized the potential benefits of these new technology applications and are utilizing these tools to lead them to smarter, more efficient and more productive operations. The list of such companies is growing every day – and your organization should also leverage the knowledge to join them.

An SFG℠ Analysts Take: There’s Nothing Artificial About Artificial Intelligence

[After you read our latest Blog, below, please be sure to take the time to participate in our 2018 Field Service Management Survey Update. We’ve already sent out our “Last Reminder” and will be closing the survey shortly. However, we don’t want to miss out on receiving your responses and insight! Simply click on the following link to access the survey questionnaire: https://t.co/wbTKMLWdpP.] 

The global field services community is always looking for “the next big thing” to impact Field Service Management (FSM), and many research analysts (including myself) are far too willing to debate whether something like 3-D printing, wearable technology or Augmented Reality (AR) are merely new technology “fads” or, rather, transformative technologies that will ultimately (and quickly) change the face of field service forever. [Note: I believe they’re transformative!]

Whenever a new technology (or a new application for existing technology) is introduced, the initial discussions may range from “It will be the best thing since sliced bread” to “it will never be accepted by the marketplace”. Most, fortunately, find their way into the ability to support the increasingly expansive functionalities of today’s (and tomorrow’s) FSM solutions. Technologies like Augmented Reality (AR) have already established a strong foothold in field service, both as a standalone platform or, integrated with Virtual Reality (VR) into a Mixed Reality (MR) platform.

However, the one “new” technology for which there is virtually no debate, even among the industry’s diverse research analysts, is Artificial Intelligence (AI). For that matter, you can also include Machine Learning (ML) in this category.

What makes AI and ML so different from most of the “new” technologies we have seen talked about in the past is that, first and foremost, neither one is really a “new” technology. The term “Artificial Intelligence” was first introduced in 1956 at an academic conference. However, it was not until 1961 when mathematician Alan Turing (the lead character in the movie, “A Beautiful Mind”) wrote a paper on the application of machines to “simulate” human beings and their ability to perform intelligent tasks – initially to play chess (and to win at it!). [Even I co-authored a published article on neural networks and artificial intelligence applications for field service back in 1993!]

Fast forwarding to today, we see just about every services analyst writing about AI and ML. For example, analyst firm, Gartner, included both AI and ML among its “Top 10 Strategic Technology Trends for 2017”, stating that, “AI and machine learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing or application. Creating intelligent systems that learn, adapt and potentially act autonomously rather than simply execute predefined instructions is primary battleground for technology vendors through at least 2020.”

Further, Gartner “advises CIOs to look at areas of the company that have large data sets but lack analytics. AI can provide augmented intelligence with respect to discovery, predictions, recommendations and automation at scale” – a perfect fit for field service!

However, research firm, Forrester, believes that “there is still a lot of AI progress to be made before machines can truly understand and guide next best actions” and that “Robots, AI will replace 7% of US jobs by 2025 (i.e., “16% of US jobs will be replaced, while the equivalent of 9% jobs will be created – a net loss of 7% of US jobs by 2025.”)

UK-based firm, iTouchVision cites the following four areas where it believes AI will likely have the greatest impact on the field service segment in the coming years:

  • Customer Experience – Primarily through the use of chatbots, “it will be possible to help customers with more speed and accuracy. These bots containing the customer and their equipment information can find out the problem and suggest a quick fix”.
  • Work Productivity – AI overcomes the hurdles faced by manual dispatchers. In the near future, we may also see the replacement of human dispatchers with an AI virtual assistant that considers all the service event parameters including unexpected events. It increases the job completion rate in the first visit by ensuring the worker has right tools and skills.
  • Predictive Maintenance – Predictive, rather than Preventive, maintenance is “the way to increase asset life and quality. The machine-to-machine interaction and the connected devices drive predictive maintenance. It eliminates the unnecessary technician visits to check machine condition”.
  • Data-Driven Decisions – “AI is all about data. With AI in use, it is possible to take more strategic decisions. Reduced repetitive administrative work allows human operatives to focus on predictive analysis. It governs end-to-end work and data flows with automation. Continuous data evaluation and processing presents a clear picture with analytics.

Overall, AI (and ML) are certainly not “artificial” – nor are they simply current fads or trends that will eventually bite the dust. They are real – not artificial; and, as such, should be carefully – and quickly – considered for incorporation into the field services management solution your organization uses to run its services operations.

Best-in-Class, Best Practices, or Benchmarking? Which Way Should You Go?

“Best-in-class” customer service and support is what all services organizations strive to achieve. However, many experts suggest that attaining “best-in-class” status in all aspects of customer service is – well – impossible! Even the very best customer service-focused organizations typically have one – or more – areas where they are not able to provide “best-in-class” customer support. However, whether a “best-in-class” organization really does – or can – exist, one thing remains absolutely clear: your organization must do everything it can to be perceived by its customers as being as close to “best-in-class” as possible.

In order to effectively move toward attaining “best-in-class” status, services organizations need to rely heavily on the formulation, development, and implementation of what is commonly referred to as “best practices” to support their customer service operations. The United States Government, General Accounting Office (GAO), defines “best practices” as “the processes, practices, or systems identified in public and private organizations that perform exceptionally well and are widely recognized as improving an organization’s performance and efficiency in specific areas”. The agency goes on to say that, “successfully identifying and applying best practices can reduce business expenses and improve organizational efficiency.”

However, in order to actually know whether your organization is currently performing at – or near – a “best-in-class” level, it will first need to “benchmark” exactly where it stands with respect to the customer service performance of other organizations – both in and outside of its field. This, of course, is commonly known as “benchmarking”. The American Productivity & Quality Center (APQC) defines “benchmarking” as “the process of improving performance by continuously identifying, understanding, and adapting outstanding practices and processes found inside and outside the organization.”

We like to define “best-in-class” primarily as “customer service performance that successfully addresses the gap between the organization’s performance and the customers’ needs and requirements, and taking the necessary steps to close that performance gap.” While this may not take you all the way to a “best-in-class” level compared against all industries and all other services vendors, it will at least take you to where you are providing the highest levels of customer service and support you possibly can.

The GAO suggests the following guidelines as to what “best-in-class” is all about, based on the results of the benchmarking research it has conducted in the private sector:

1.  Make it easy for your customers to voice their concerns, and your customers will make it easy for you to improve.

Nobody likes to receive constructive criticism or have someone complain about their customer service performance to a supervisor. However, you should accept every customer-voiced concern or complaint as just another one of your “marching orders” to improve – or fine-tune – your organization’s customer service and support skills.

2.  Listen to the voice of the customer.

Customer service leaders demonstrate their commitment to resolving customer concerns by listening directly to the voice of the customer. By investing your time in communications with your customers, the payoff will be an easier path to get the job done – regardless of whether it is a service call, responding to a customer request or inquiry, or anything else that the customer feels is important.

3.  Respond to customer concerns quickly and courteously with common sense, and you will improve customer loyalty.

Customers tend to “reward” vendors who can quickly – and repeatedly – resolve their problems by remaining loyal customers. Quick problem resolution can add greatly to the foundation that you are trying to build in support of customer loyalty – and repeated quick problem resolution will all but certainly “close the deal”.

4.  Resolve problems on the initial contact – build customer confidence, and save money.

A customer callback that requires two or more company personnel to follow-up will typically cost much more than a call that was handled right the first time. Resolving a customer problem on the initial contact can also significantly build the level of confidence your customer has in your organization’s ability to get the job done. And once you earn this level of trust, it will be difficult to lose it.

5.  Technology utilization is critical in problem resolution.

Your company probably already uses a number of technology-based tools to support its field engineers’ ability to quickly resolve customer problems – but they need to use them! These tools should be used – as a matter of course –as support in providing customers with quick and effective solutions.

6.  Continue to train your employees in customer service and support.

Regardless of what customer service training you may have provided to your employees in the past, chances are they already need more training in order to remain effective. There are always new technologies and tools being developed to support their ability to provide “best-in-class” customer support.

7.  Focus on getting the job done; not just dealing with the symptoms.

If routine equipment and/or customer problems are effectively being resolved initially at the front-line, company management can focus more on improving the core processes, policies, and guidelines that drive customer service performance and customer satisfaction throughout the organization. “Best-in-class” companies use formal processes to, first, identify the problems and; then, to empower their employees to resolve them as quickly as possible.

The main lessons to be learned from approaching customer service from a “best-in-class” perspective are as follows:

  • Satisfying the customer must be your top priority.
  • View customer concerns and criticisms as opportunities for improvement – not just as problems.
  • Make it easier for customers to voice their concerns; this will make it easier for your service engineers to resolve their problems.
  • Effective customer service and support relies heavily on two-way communications
  • Well-managed customer service and support processes make everybody’s job easier – and customers more satisfied.

All of the tools you need to become a “best-in-class” provider are already in your hands; but, you have to make them available to all of your employees – along with the empowerment to use them!

SFG℠’s Analyst Take Perspective on the PTC – Rockwell Automation Partnership

Rock Solid? Or, Just Another Stop Along the Way to PTC’s Next Partnership and/or Acquisition

On June 11, 2018, PTC, a global leader in assisting companies to “reinvent the way they design, manufacture, operate, and service things in and for a smart, connected world”, and Rockwell Automation, “the world’s largest company dedicated to industrial automation and information”, announced that they have entered into a definitive agreement for a strategic partnership that is expected to “accelerate growth for both companies and enable them to be the partner of choice for customers around the world who want to transform their physical operations with digital technology”.

Based on a comprehensive review, augmented by interviews with key PTC executives, SFG℠ believes that there are many aspects to the PTC-Rockwell Automation partnership that are generally positive, with only a few potential red flags that may potentially become somewhat problematic down the road. Basically, it will all depend on the ability of both organizations to execute effectively, and in sync with one another.

[To download a complimentary copy of the full SFG℠ Analysts Take paper that provides additional details, please click on the following Weblink: @@@ PTC-Rockwell Partnership Analyst Take Paper (18-07-24-01)]