Oftentimes field service directors and managers can see the importance of investment within a dedicated Field Service Management (FSM) ahead of their colleagues in the boardroom. In this series of articles Kevin McNally, Sales Director, Asolvi...
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Nov 26, 2018 • Features • Management • Kevin McNally • management • field service • field service management • Service Management • Building a case for investment • Business Investment • Field Service Technologies • Managing the Mobile Workforce
Oftentimes field service directors and managers can see the importance of investment within a dedicated Field Service Management (FSM) ahead of their colleagues in the boardroom. In this series of articles Kevin McNally, Sales Director, Asolvi outlines how to build a case for investment to drive your field service operations forwards.
In the first instalment in this series, we looked at how FSM systems can deliver easy Return on Investment, in part two we explored how investment in FSM solutions can help you achieve better staff retention and now in part three we turn our attention to how the implementation of an FSM solution can improve worker health and safety.
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Perhaps the easiest argument to put forward to an executive board when seeking approval for investment, aside from outlining a clear ROI, is when that investment will mean ensuring the working environments you place your employees in meets any necessary health and safety requirements.
Of course, in a field service scenario, it is not necessarily possible to control the environment in which your field workers will be undertaking their role.
Therefore, monitoring their safety and ensuring they follow all due protocols and procedures is of huge importance.
So let’s take a quick look at some ways in which FSM systems can help you keep your field service engineers and technicians safe.
Geo-Fencing And Lone Worker Support
One reason field service technicians and engineers are particularly vulnerable is simply the fact that they are often working alone - so should an accident happen it could go unnoticed for some time, delaying any necessary medical attention.
There are many lone worker solutions out there, but it is optimum if you can identify a solution that can integrate into your wider FSM system so it can take a data feed of where the engineer is scheduled to be at any given time.
In fact, once that data feed from your scheduling solution is in place, it is possible to establish geo-fencing to make sure that should your engineer either stray outside of where they are supposed to be, or should they fail to arrive where they are supposed to be within a parameter of acceptable margin, alerts can be triggered helping to avoid potential tragedies that can arise from lone worker scenarios.
Indeed, this is one of the key areas in which the importance of the easy flow of data across your field service eco-system can be most easily highlighted – as the benefits literally could mean the difference between life and death.
Smart Scheduling To Ensure Your Engineers Are Safe
The next item on the list is an obvious one, and one that any good dispatcher even working without an FSM solution will pride themselves on doing – however, by automating it we can remove the potential for human oversight and error.
Many FSM systems will allow you to set clear parameters against a job during set up so that should maintenance or repair need to be scheduled then unless those parameters (such as the job requiring specific qualifications or more than one technician being required) are met the job cannot be scheduled.
This relatively simple, yet highly effective inclusion within an FSM system can ensure that the right engineer(s), with the right qualifications, are sent to the job, helping to avoid any potential health and safety issues that could result from under qualified or undermanned service teams being sent out.
Guide Your Engineers When Onsite To Safe Processes Every Time
Even the most experienced engineers can make mistakes – and these are oftentimes the result of simple complacency – again something that can be overcome through the use of mobile tools often found within an FSM solution.
For example, by building a checklist on the engineer’s mobile device that is built into their workflow you can ensure essential steps aren’t overlooked.
It may seem like an obvious thing for the experienced engineer to be told to switch off a core valve or even mains power to an asset before undertaking maintenance, but it only takes one rushed moment of forgetfulness on a bad day to cause an accident that could potentially be fatal.
But your FSM solution could help avoid that entirely by only allowing the engineer to begin work on the asset once they have completed the H&S checks on their mobile device.
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Nov 25, 2018 • Features • AI • Artificial intelligence • Future of FIeld Service • MArne MArtin • field service • field service management • IFS • Service Management • Field Service Technologies • Parts Pricing and Logistics • Managing the Mobile Workforce
Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful...
Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the part one of this two-part feature Marne Martin, President of Service Management, IFS outlined why AI in field service is about far more than chatbots, now in the concluding part, she outlines how AI can bring a touch of genius to your field service operations...
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Solving Problems When One Isn't Albert Einstein
Human agents are capable of optimally dealing with a customer, and AI can free them up for the most interesting and demanding tasks. In the case of scheduling technicians in the field, humans are just not up to the numerical challenge of adjusting a schedule in an optimal fashion as humans typically focus in on an aspect of a problem to solve rather than finding the best solution overall.
A dynamic scheduling engine (DSE) driven by AI algorithms is designed to solve complex scheduling problems in real time—problems much too complex for any human dispatcher or customer service agent to handle, especially when at times individuals will act myopically based on their area rather than for the greater good of the company and its customers.
"Even a static service schedule can be handled in myriad different ways and decisions regarding which technician to send to which of several jobs in what order are often made based on suboptimal heuristics..."
Even a static service schedule can be handled in myriad different ways and decisions regarding which technician to send to which of several jobs in what order are often made based on suboptimal heuristics.
“Steve’s son is in daycare in this part of town, so I will schedule this appointment last, so he will be close by.” Sometimes jobs are scheduled based on first-in, first scheduled, regardless of the actual urgency of requests that come later.
Manual or traditional software-based scheduling may be a workable solution for service organizations with a very small number of technicians each engaged in a small number of jobs during a day. But it does not take many technicians or jobs for the number of possible solutions to outstrip human computation capabilities either individually or as a group.
Even at the low end of the spectrum, a human dispatcher cannot quickly identify all the possible solutions and pick the best one. With two technicians and four service calls there are already 120 possible solutions— different combinations of technician, job and order. Two technicians, and five service calls yields 720 possible solutions. Four technicians and 10 service calls present a dispatcher with 1,037,836,800 possible solutions.
But the time you get to five technicians that must complete six calls each—a total of 30 calls, you have 12,301,367,000,000,000,000,000, 000,000,000,000,000 possible solutions.
Finding the optimal solution becomes even more complex as additional and rapidly-changing factors are added into the mix:
- Emergent jobs come in that must take precedence over those already scheduled
- SLAs and other contractual requirements demand that some jobs be completed within a given timeframe
- Technician skill sets that influence which tech is sent to which job
- Tools and materials currently in stock on each service vehicle
- The current location of a technician in proximity to each job and to drop locations for inventory that may be required for a job
- The duration of each service call, both in terms of estimated time required to complete the call and whether a current job is running over the estimated time, resulting in knock-on effect on subsequent jobs
Former world chess champion Garry Kasparov, in his book Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, makes clear that even his mind is not capable of computing possible solutions and outcomes as rapidly or effectively as an AI algorithm.
"Automating the schedule through AI not only enables a much higher level of service but frees up dispatchers to handle those “beautiful or paradoxical moves” that may delight a customer or solve a tough problem...“
The human mind isn’t a computer; it cannot progress in an orderly fashion down a list of candidate moves and rank them by a score down to the hundredth of a pawn the way a chess machine does,” Kasparov writes. “Even the most disciplined human mind wanders in the heat of competition. This is both a weakness and a strength of human cognition. Sometimes these undisciplined wanderings only weaken your analysis. Other times they lead to inspiration, to beautiful or paradoxical moves that were not on your initial list of candidates.”
Automating the schedule through AI not only enables a much higher level of service but frees up dispatchers to handle those “beautiful or paradoxical moves” that may delight a customer or solve a tough problem.
In the end, collaborating with intelligent machines will get us further faster than going it alone. According to Kasparov, the best chess is now played as grandmasters use computers to analyze positions, opponents’ games and their own games—elevating the level of play. In an interview with the Financial Times, Kasparov, who famously had matches against an early chess supercomputer, described how the best chess is now played by combining “human intuition and understanding of the game of chess with a computer’s brute force of calculation and memory.”
“I introduced what is called advanced chess; human plus machine against another human plus machine,” Kasparov said. “A human plus machine will always beat a super machine. The computer will compensate for our human weaknesses and guarantee we are not making mistakes under pressure … the most important thing is not the strengths of the human player. It is not the power of the computer. But it is the interface. It is the corporation.”
Legacy Approach to Inventory Logistics
Service management for many businesses relies on inventory … if completion of a service call requires inventory and you are out of stock, you cannot meet your commitment to the customer. When a service request cannot be closed on the first visit, it is often because the right part is not on the truck or immediately available.
So, service management software should encompass inventory management functionality, and that functionality should include automated reorder points for each part. The ability to take parts availability into consideration is a critical data set for AI to work on as parts are a critical determinant in first-time fix and job completion where parts are a factor. It also is a key aspect to successful SLA and outcomes-based commercial relationships.
Once inventory data is available and integrated, a powerful DSE may also be configured to influence inventory logistics so parts and materials are housed in warehouses, satellite offices or inventory drop locations closer to anticipated demand, with inventory matched to jobs in a forward or current day schedule. In one very large implementation of IFS Planning and Scheduling™ Optimization—in the London underground transit system—inventory and tools are dropped ahead of each service visit so technicians who ride the subway to the service site can pick them up.
This is only possible with a high degree of coordination between the service schedule, inventory logistics and an AI-driven scheduling tool.
Conclusion
Service organisations should recognise the tremendous potential AI holds—they can harness it to transform their operations, outflank their competitors and disrupt their markets. We are only starting to tap into the different ways AI can be used to better solve the problem of delivering optimal service in a rapidly changing environment as adoption is still lagging despite the real benefits AI brings. The good news is there are several straightforward and easily accessible ways service executives can harness AI technology right now, today.
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Nov 23, 2018 • Features • Future of FIeld Service • Outcome based services • Preventative Maintenance • field service • field service technology • Internet of Things • IoT • Service Management • Servitization • Advenaced Services • Service Management Technology • Managing the Mobile Workforce
Adopting IoT as part of the greater service and business environment involves keeping up with industry changes as they take place. That means incorporating better measures when needs arise in any business area and keeping cost-effective solutions in...
Adopting IoT as part of the greater service and business environment involves keeping up with industry changes as they take place. That means incorporating better measures when needs arise in any business area and keeping cost-effective solutions in mind for the future progress of the company as a whole...
Already, 76% of companies are using IoT data analytics to establish product and/or process quality imperatives. Their decision makers can analyze IoT data to improve solution recommendations, feedback on installations, demonstrations, specific services, and others.
IoT also serves as a signifier for opportunities to improve more processes, such as identifying popular products and managing inventory.
Respondents to a recent research project undertaken by WBR and commisioned by Astea believe data should be usable in decision making at a variety of business levels. In every case, a majority of companies have either adopted IoT for specific business functions or plan to do so in the next 24 months. But companies prioritize customer-facing initiatives—service, products, and satisfaction—over internal functions such as business projections and aligning service data with financials.
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Customer Satisfaction & Loyalty:
73% of companies have incorporated IoT (42%) or plan to do so within 24 months (31%) for the purpose of customer satisfaction and loyalty. More companies have incorporated IoT for this purpose than for any other measured in the study.
With connected data, companies are able to understand and fulfil customer demands better thanks to improved communication. In this way, minor technological improvements can be made without delay or other consequences.
Service Processes & Optimization
Respondents agree that connected data and IoT have helped streamline processes across departments. By leveraging IoT data, they can measure efforts for overall growth through set channels, be they internal or service-driven.
Now, 41% of companies have incorporated IoT for process optimization, a close second to customer satisfaction and loyalty. Thirty-six percent have already incorporated IoT with service processes; more companies plan to do so within 24 months (37%) than with any other business function measured.
Product Uptime
Companies’ attention to customer experiences carries over to product support, where one respondent cites “notable improvements” to uptime in both industrial and consumer-driven channels. One healthcare executive says IoT helps them sustain products “during times of higher demands, especially due to the fact that these are used during medical procedures.”
More than one-third of companies have incorporated IoT for product uptime (34%); more than one-quarter of companies have plans to incorporate IoT with product uptime (30%) within 24 months.
Business Projections & Decisions
IoT data can be applied to various business requirements and provide essential statistics to support managerial functions. Derivations from reliable signals allow for better judgements when making business projections and decisions.
Over one-third of companies have incorporated IoT for business projections and decisions (35%); more than one-quarter of companies have plans to incorporate IoT with business projections and decisions (27%) within 24 months.
Predictive Maintenance
Respondents’ ambitions for better response to maintenance needs extends to real-time automated reporting, a better understanding of their products’ “general maintenance structure,” and even signals for customers to be proactive—to seek out maintenance themselves.
Several respondents cite their use of predictive reporting for scheduling, sustainability, and research methods, among others. Only 32% of companies have leveraged IoT for predictive maintenance; however, 29% plan to do so within 24 months.
Aligning Service Data with Financials
Fewer companies have incorporated IoT to align service data with financials (26%) than any other business function in the study. But the data suggests this is a growth area. More companies (61%) are either planning to incorporate IoT in this way within 24 months or are interested in incorporating IoT in this way than with any other business function.
Despite the prioritization of functions that drive customer success, it is in business projections, business decisions, and aligning service data with financials that companies take an increasing interest in incorporating IoT.
At least one-quarter of companies have already incorporated IoT for each of these purposes. Have you?
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Nov 22, 2018 • Hardware • News • Panasonic • Captec • Dell • Enterprise Mobility • field service • field service technology • Getac • rugged hardware • samsung • Service Management
Captec, the designer and supplier of specialist computing platforms, has teamed up with market-leading rugged tablet manufacturers to provide 360-degree in-vehicle solutions.
Captec, the designer and supplier of specialist computing platforms, has teamed up with market-leading rugged tablet manufacturers to provide 360-degree in-vehicle solutions.
The specialist tablets from Dell, Getac, Panasonic and Samsung are designed to accommodate the needs of demanding applications in industries including construction, utilities, and fire and rescue.
In the modern working environment, it is crucial for fleets to have the tools that allow them to work efficiently through improved communication and efficiency. By securing these strategic partnerships, Captec provides fleets with high-performance technology and the flexibility to achieve an optimal balance between performance and budget for their application.
With features including high ingress protection, ruggedisation, built-in GPS and barcode scanners, the tablets meet and exceed the needs of mobile workforces across multiple sectors.
Each tablet can be combined with a vehicle dock, specifically designed to accommodate it – sourced by Captec from its trusted vehicle docking partners, Gamber-Johnson and Havis.
To provide a true 360-degree solution, Captec offers a complete installation service including full vehicle audits, custom cabling, onsite installation performed by experienced engineers and tailored through life support.
For more complex applications demanding a high level of customisation such as additional ruggedisation for uninterrupted operation in hostile conditions, Captec offer bespoke engineering and technical services. From the initial design concept through to testing and validation, Captec can make any necessary modifications quickly and effectively at its dedicated in-house engineering facilities.
Ian Langton, Product Manager, Captec, said: “We have partnered with leading technology providers in complete confidence that, when combined with our installation service, we can address the performance, reliability and longevity needs of today’s mobile workforce. Our aim is to create a one-stop-shop for fleet managers looking to equip their vehicles with rugged and reliable mobile computing.”
In addition to offering models from suppliers through strategic partnerships, Captec has recently designed, developed and introduced a brand new rugged tablet of its own.
The VT-681 tablet is a fully rugged device, powered by Windows 10 IoT, making it ideal for fleets requiring a cost-effective solution, without compromising on performance or features from other well-known suppliers.
Find out more about Captec’s in-vehicle computing solutions @ www.captec-group.com/in-vehicle
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Nov 21, 2018 • video • Features • Autonomous Vehicles • field service • field service management • Service Management • Brønnøy Kalk AS • Driverless vehicles • Volvo Trucks • Parts Pricing and Logistics • Managing the Mobile Workforce
In a landmark agreement between Volvo Trucks and Norwegian mining company Brønnøy Kalk AS, six autonomous Volvo FH trucks will transport limestone over a five-kilometre stretch in a mine. Tests of this solution have been carried out successfully and...
In a landmark agreement between Volvo Trucks and Norwegian mining company Brønnøy Kalk AS, six autonomous Volvo FH trucks will transport limestone over a five-kilometre stretch in a mine. Tests of this solution have been carried out successfully and will continue throughout 2018 to become fully operational by the end of 2019.
The deal represents Volvo Trucks’ first commercial autonomous transport solution that will run in a real operation. It is a new solution whereby the customer buys a transport service where Volvo Trucks takes full responsibility for the delivery of the limestone to the crusher.
Whilst we may be still some way from seeing autonomous vehicles being used in service logistics, this is an interesting and possibly pivotal new development.
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Nov 21, 2018 • Features • AI • Artificial intelligence • Future of FIeld Service • future of field service • MArne MArtin • Workwave • Chatbots • field service • field service management • field service technology • IFS • Service Management • Service Management Technology • Wrokforce Management
Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful...
Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the first of a two-part feature, Marne Martin, President Service Management IFS, offers her expert insight...
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Artificial Intelligence (AI) will impact every industry and every business discipline—including field service management. But how quickly will practical solutions be available that enable the typical medium to large field service organization to take advantage of AI? And by practical solutions, I mean AI that delivers knowledge efficiently, processes solutions to complex data sets, and automates repetitive activities to allow human workers to focus on personalized service, solving complex problems and escalations, i.e. what people do best.
In some cases, these easily applied solutions are still on their way to market. In three specific areas, however, practical AI applications for field service are already commercially available as proven, commercial off-the-shelf software delivering real business value.
AI For Customer Interaction
First impressions matter. And unfortunately, the first interaction a customer has with your service organization often involves several missteps. Chief among these are long wait times on hold due to high call volumes. And then, as a customer attempts to reach out through multiple channels including email, chat and phone, the resulting data stream goes into separate siloes that are disconnected from each other, resulting in disjointed communication.
"Today, AI solutions can solve both these problems, but it requires more than “just” chatbots..."
Today, AI solutions can solve both these problems, but it requires more than “just” chatbots. Commercially available AI software that ties into chatbots is capable of learning which answers posed in a chat are appropriate for each question and automating a significant majority of chat interactions. A chatbot can be taught to answer commonly encountered questions, like inquiries about when a technician is scheduled to arrive. Of course, at some point, the AI chatbot may get stuck when personalized service is required, and a human agent takes over the discussion thread without missing a beat. This should be seamless not only to the customer but for the internal customer service, ticketing and support systems as well. The chatbot—regardless of whether driven at a given moment by AI or a human agent—should update the same customer record as other channels including social media, phone and email.
And from interactions, the AI functionality learns from answers provided by human agents and gets better and better at answering questions through learning processes. A truly advanced AI chatbot will also seamlessly hand off the chat to a human agent when the extent of its learning is overtaken. Only then can the entire customer experience be unified and consistent, even with a static number of agents handling a rapidly growing fluctuating volume of customer interactions.
AI-based chatbots, for instance, can enable a good agent to handle up to five or more chats at a time. It can capture Facebook messages and tweets and direct them to an agent or to AI for intervention. AI alone can handle, typically, between 50 and 60 percent of requests, freeing up human capacity or lowering staffing levels required to handle a given volume of activity.
Enables Management By Exception
In the case of AI applications for the service organization, a primary driver for ROI is that it enables humans to manage by exception. A high volume of activity can be automated, and humans intervene primarily when a situation falls outside the business rules or logic built into service management software. AI doesn’t eliminate the need for human interaction—it makes the human interaction more focused on what humans do best—handle escalations and complex decision making for unique cases.
At one IFS customer, an AI chatbot handles about 50 percent of interactions— primarily those reaching out to cancel their service after a free three-month trial period. Interactions cancelling a free subscription are handled entirely through automation. But if a longer-standing customer is cancelling their service, the interaction gets routed to an agent dedicated to saving the account.
Some interactions are by default easily handled by AI. If 30 percent of inbound contacts are requesting information on the arrival time of a field service technician, it may be possible to automate 90 percent of that 30 percent of contacts. But it is also important to consider the demographics of the customer base. Millennials are more likely to communicate via chat or social media, so if a significant percentage of customers are under 40, heavier reliance on chatbots and AI may help you increase engagement by streamlining your customers’ preferred method of interaction.
"Management by exception is also more successful when an AI application has access to extensive information about each customer..."
Management by exception is also more successful when an AI application has access to extensive information about each customer. So full integration with enterprise resource planning, field service management and other enterprise tools is essential. AI tools can be more effective if they have more rather than less information on the status of the customer’s account, including their maintenance or service history and warranty or service level agreement entitlements.
Integration between an AI chatbot, email, voice, social and enterprise applications is important for another reason. It enables one version of the customer record. Lacking this, a customer can send an email, and get no response. They send a direct message through Twitter. Then call and sit on hold. Then initiate a chat. All these interactions may not appear in a central customer record, but there have been three attempts to contact the company. Right from the first contact by email, the clock started ticking on a service level agreement.
Full integration can also enable a customer service team, once a customer request is resolved, to close off all queuing activations at the same time for the various contact methods associated with a customer case. Failing this, a service organization may spend a significant amount of time chasing customer requests that have already been resolved.
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Nov 20, 2018 • Fleet Technology • News • fleet technology • Verizon Connect • Berg Insight • field service • field service technology • fleet management • Service Management • EcoFleet • Fleet Complete • Managing the Mobile Workforce
The number of active fleet management systems deployed in commercial vehicle fleets in Europe was 7.7 million in Q4-2017, according to a new research report from the IoT analyst firm Berg Insight.
The number of active fleet management systems deployed in commercial vehicle fleets in Europe was 7.7 million in Q4-2017, according to a new research report from the IoT analyst firm Berg Insight.
Growing at a compound annual growth rate (CAGR) of 15.2 percent, this number is expected to reach 15.6 million by 2022. The top-15 vendors have today more than 100,000 active units in Europe. TomTom’s subscriber base has grown both organically and by acquisitions during the past years and the company is the clear market leader on the European market and reached an installed base of about 708,000 units at year-end 2017. Masternaut is still in second place and had achieved an installed base of an estimated 250,000 units.
Berg Insight ranks Verizon Connect as the third largest player in terms of active installed base with around 235,000 units. ABAX, Microlise, Gurtam, Viasat, Bornemann, Teletrac Navman, Trimble, Transics and OCEAN (Orange) also have more than 100,000 active devices in the field. The HCV manufacturers are now growing their subscriber bases considerably in Europe thanks to standard line fitment of fleet management solutions. Dynafleet by Volvo, FleetBoard by Daimler and Scania Fleet Management are the most successful with active subscriber bases of 117,000 units, 108,000 units and 219,000 units respectively as of Q4-2017.
The consolidation trend on this market continued in 2018. “Thirteen major mergers and acquisitions have taken place in the past twelve months among the vendors of fleet management systems in Europe”, said Johan Fagerberg, Principal Analyst, Berg Insight. ORBCOMM acquired Blue Tree Systems in October 2017. The acquisition of Blue Tree solidifies ORBCOMM’s transportation portfolio by adding truck in-cab and refrigerated fleet vehicle solutions to ORBCOMM’s cargo solutions. In December, ABAX acquired Danish Fleetfinder. The acquisition added approximately 7,000 vehicle subscriptions to ABAX Group’s existing subscription base.
Later in December, Viasat Group acquired a majority stake (51 percent) in Locster based in France. January of 2018 started with two acquisitions. EcoFleet was acquired by Fleet Complete and Verizon Connect moreover continued its European expansion with the acquisition of Movildata in Spain. Coyote acquired a 70 percent stake in Traqueur in February 2018. Later in April, Trackunit acquired UK-based telematics provider Satrak. GSGroup purchased Care4all based in Denmark in July 2018. Vehco was acquired by AddSecure in May 2018, which gave Vehco the structure and financial strength to further expand within fleet management. Vehco’s third acquisition followed in August 2018 when the company took over the ownership of Groeneveld ICT Solutions. AROBS Transilvania Software acquired SAS Grup in September.
The latest two transactions were done in September and October when Viasat Group acquired Detector in Spain as well as 60 percent of the shares in TrackIT Consulting in Portugal. Mr. Fagerberg anticipates that the market consolidation of the still overcrowded industry will continue in 2018–2019.
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Nov 20, 2018 • Features • 3D printing • Aftermarket • Artificial intelligence • copperberg • Inventory Management • field service • field service technology • Service Management • eCommerce • Parts Pricing and Logistics
In an age of servitization and advanced services, spare parts management has become something of a difficult beast to fully grasp for many companies who are offer aftermarket services.
In an age of servitization and advanced services, spare parts management has become something of a difficult beast to fully grasp for many companies who are offer aftermarket services.
For example, in a world of guaranteed up-times, the cost of failure to keep an asset running can often far outweigh the lost revenue from the sale of the replacement part needed to get the asset back up and running and fully functional again.
Yet, the path to servitization is not an easy one to tread - so is it worth cannibalising what for many service companies is a reliable, consistent and strong revenue stream in its pursuit?
Whichever route companies ultimately turn to, one thing is certain, spare parts management is going to be a crucial aspect within the service delivery sector and as with mobile workforce management, there are a number of technologies and innovations that are emerging that could change the way we approach parts management in the future.
Therefore it was with great interest that we took a look at the insights from a recent research project undertaken by Copperberg. The research was conducted online across the last month primarily to Copperberg’s own audience of conference delegates.
In total the there were 65 responses to the survey and these representatives were all professionals within the sector ranging in seniority from parts managers through to Managing Directors - although the main body of respondents were at the division head/director level on a national scale.
The majority of respondents were from Europe although other regions, including China, were represented. The respondents were largely from manufacturing verticals, which would be anticipated given Copperberg’s flagship event the Aftermarket Business Platform is also a manufacturing dominated event. However, there were a number verticals within the manufacturing sector represented including heavy machinery, medical and automotive.
So let us take a brief look at what trends the research revealed...
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Inventory Management:
Inventory Management sits at the heart of good parts management as without the ability to track components and parts at any given time as they move from depot to the field (and potentially back again depending on a companies approach to repair and reverse logistics) everything else within in the equation becomes open to inaccuracies and subject to guesswork.
Indeed, the importance of inventory management appears to be hugely important within the organisations represented within Copperberg’s research with 91% of the respondents ranking it as being either four, five or six on a scale on to six with six being very important. In fact, almost half of the respondents (43%) listed Inventory Management as very important (6) - further emphasising the significance of inventory management in the context of spare parts management.
So it is absolutely shown to be clear in the research that the focus on inventory management remains one of utmost importance for the vast majority of companies.
Parts Pricing and eCommerce:
Parts pricing is also another area that was unanimously outlined as being important to the survey respondents.
This is particularly interesting as the fact that so many companies still view parts pricing as being highly important to them could be viewed as an indicator that the revenue streams that come from spare parts sales is still very much a critical part of the aftermarket landscape.
In fact, 86% of respondents stated that they felt parts pricing was at least a four on the same scale as listed above, however, here it was just under a third of respondents (32%) that felt this issue was very important.
eCommerce is of course another area that is heavily linked with parts pricing and there are indeed some correlations between the two areas, yet in terms of responses, eCommerce remains somewhat less of a priority than pricing.
With regards to eCommerce, exactly two-thirds of the respondents (66%) listed it as a four, five or six with only 16% seeing it as being very important (6).
This is quite an interesting difference between the two as we might have anticipated these results being more closely aligned.
One assumption, however, may be that with regards to eCommerce the solutions have now matured and so most manufacturers in 2018 may have at least some form of eCommerce solution in place - perhaps this explains why it is viewed as less of a priority?
This is certainly though an area for further discussion - something that will be surely had at the Copperberg Spare Parts Business Platforms which are running in Q1 next year.
Digitalisation:
Digitalisation is the key buzzword of the last few years although given that it encompasses a number of important shifts within the current evolution of business processes this is perhaps to be expected and there is no denying the importance of digitalisation within the field service sector and it is also a major consideration within the closely related function of parts management as the research reveals.
Digitalisation was ranked was 71% of the respondents to the Copperberg survey as being listed as either a four, five or six on their scale of importance, with 22% of respondents listing it as a six i.e. very important.
This places digitalisation as being deemed to be not quite as important to the respondent base as Inventory Management and Parts Pricing but more important than eCommerce.
What is interesting to note here is that these two very specific niche challenges seem to be in some-ways the eternal, perennial headaches of the sector, whilst broader, business-wide concerns such as digitalisation are possibly more likely to appear as an issue to overcome in the short-term which in themselves could lead to improvements in other areas - such as improved inventory management for example.
Which leads us neatly into...
3D Printing & Artificial Intelligence:
Two perfect examples of exciting new technologies that are emerging would be 3D Printing and Artificial Intelligence (AI) - with one set to play a hugely significant role in the niche of spare parts management, whilst the other will play a broad role in almost all sectors, including spare parts management.
So how do the industry experts who made up the Copperberg respondent base see each of these exciting technologies impacting the spare parts management sector?
With regards to AI just over a third of respondents (35% ) thought it would be important to some degree (again listing it as either a four, five or six).
However, less than a tenth of the respondents (9%) felt that AI was currently very important for them.
In terms of 3D printing, surprisingly the numbers were even lower.
In fact, less than a third of companies listed 3D Printing at a four or higher and only 8% of respondents felt that 3D Printing was very important in the sector currently.
Parts Logistics:
One area, however, that was overwhelmingly listed as being important within the field of spare parts management across the next 12 months was that of parts logistics.
94% of respondents listed parts logistics as being at least a four in the scale of importance with over a third (35%) going on to state that they felt parts logistics was important enough to warrant being listed as a six.
This makes parts logistics one of the most important areas in the spare parts sector across the next twelve months according to this respondent base, although Inventory Management is very important to more companies.
Conclusions:
The results of the survey bring us some interesting conclusions - particularly when we stand them alongside the trends we are seeing from within the field service sector.
Of course, field service and parts management are two leaves on the same branch with deeply symbiotic relationships between the two.
Yet, from this research at least, it does seem that many of the forward-looking discussions we have been having within the field service sector, particularly around emerging technologies such as AI, IoT and Augmented Reality as well as the wider topic of servitization as a strategy for business growth - may be further down the line than their equivalent discussions with our spare parts colleagues - and in some companies that may be significantly so.
Perhaps, part of the reason for this is that parts management is a highly complex beast with a huge amount of moving parts (literally) and even if solutions such as inventory management systems have been put in place it may take time for the benefits there to be truly felt.
However, the simple fact is that no matter how efficient field service management is - it all falls out of the window if parts management is poor - and this is perhaps the greatest learning from the research - that the focus of professionals within the parts management sector currently remains on efficiencies - and for that, we in field service should be hugely grateful.
Want to know more? Click here to Visit Copperberg's website to register for an exclusive white paper based on this research!
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Nov 16, 2018 • Features • Future of FIeld Service • IIOT • field service • GE Digital • data analysis • Edge Computing • George Walker • Industrial Internet of THings • Novotek • Predex
In the age of the industrial internet of things (IIoT), the speed of data analysis is key to effective operation. Edge computing accelerates this process, allowing for industrial data analysis to be performed at the point of collection.
In the age of the industrial internet of things (IIoT), the speed of data analysis is key to effective operation. Edge computing accelerates this process, allowing for industrial data analysis to be performed at the point of collection.
Here, George Walker, managing director of industrial control and automation provider Novotek UK and Ireland, explains the core benefits of edge computing.
Edge computing is the term for when process data is collected, processed and analysed in a local device, as opposed to being transmitted to a centralised system. Supported by local cloud networks and IIoT platforms like GE Digital’s Predix, systems that support edge computing are proving increasingly popular as a means of streamlining the effectiveness of IIoT networks.
For plant and utility managers, this presents a range of opportunities to not only improve the efficiency of operations but to also overcome some of the limitations of centralised IIoT networks. In fact, there are the three main ways that edge computing drives value in businesses.
Greater operational efficiency
Traditional analysis is undergone by transferring data externally, which can delay decision-making as errors take longer to be found. With edge computing capable systems, large parts of the analysis can be carried out by the devices collecting the data.
The benefits of this are two-fold. For one, this can allow plant managers to access partial deep analysis in real time without waiting on lengthy analysis to be carried out externally. This means action can be taken earlier, streamlining the decision-making process.
The second benefit is that the IIoT platform, such as GE digitals Predix, can automatically respond to operational data. The system will be able to automatically adjust processes in real-time. In effect, this would allow for a self-correcting system that is able to maximise uptime and reduce the need for manual maintenance.
Overcoming network latency and bottlenecks
Traditionally, data analysis is carried out by having smart sensors send all their data to a remote location where it is analysed and processed. This is data intensive and can create problems if a network is not robust enough.
Channelling large amounts can cause network latency, which interrupts working within the plant as there will be a delay with transferring messages that run through the same network.
This is particularly problematic for applications where a system needs to act rapidly to a problem, such as in an industrial oven control system in a food production plant, where even a temporary dip in the temperature can result in a batch being unsuitable for market.
In addition to this, the sheer volume of raw data that can be generated in an industrial or utility plant is also likely to cause data bottlenecks in the wider network.
By using edge computing systems and a machine-learning IIoT platform, systems can respond to changes in real-time to prevent problems, while also having edge computers in place to compress the data and reduce network impact.
Lower operating costs
Due to the amount of information being produced, the cost of data storage is becoming a growing concern for companies. Edge computing and its ability to process data without transmitting it, lightens the load put on the network.
Processed data is also less substantial than raw data as calculations can be made that allow the raw data to be compressed, thus reducing file sizes. As such, industrial companies are able to make more economical use of their cloud servers. By minimising storage requirements and the number of storage upgrades required, edge computing can allow for a lower overall operating cost.
It’s clear that there are many benefits to edge computing, both from a financial and operational perspective. Whether a business is still considering adopting IIoT technology or is already making use of such systems, edge computing marks a step forward for businesses looking to streamline processes for efficiency and effectiveness.
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