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How to reconnect field service workers

The field service industry has always been an early technology adopter. TOM DEVROY, Senior Product Evangelist for Enterprise Service Management at IFS, identifies three technology-led developments set to disrupt field service.

The field service industry has always been one of the earliest adopters of new technologies, from the original PDAs to IoT-enabled devices. Now, a new generation of technology is uniquely positioned to transform the field service industry, promising to reduce costs and dramatically improve the quality of service organisations can offer. Tom DeVroy, Senior Product Evangelist for Enterprise Service Management at IFS, identifies three technology-led developments set to disrupt field service and discusses how flexible and modular resource planning infrastructure will help organisations reap substantial rewards.

Effective field service is about proactively managing your workforce and inventory in order to meet the constantly sliding scale of customer expectations. As a result, field service organisations are constantly looking to improve on the key metrics to better serve customers: first-time fix rate (FTF), mean time to service (MTTS) and mean time to repair (MTTR).

Three new technology driven developments are establishing themselves in the market, with the potential to dramatically impact these field service metrics to benefit both the customer and service provider:

·         Advanced mobility: augmented reality, instant messaging platforms and native apps

·         Predictive analytics enabling prescriptive maintenance

·         Optimised scheduling and demand forecasting in an IoT world

First: Beyond mobility: augmented reality, instant messaging and native apps

A mobile workforce needs a mobile-driven field service strategy. In a recent study on mobility, performance and engagement, 60% of employees said mobile technology makes them more productive in the workplace. But field service organisations are now moving beyond simple mobility, looking for more intelligence and flexibility from their mobile computing platform in order to take full advantage of next generation devices.

Native apps are a key part of this – allowing engineers to receive instant updates, access repair information or collaborate with product experts without leaving the job site. Instant messaging platforms such as Slack and WeChat are also allowing field service engineers to keep connected, with more information and collaboration supported on their mobile device. Engineers are able to contact other colleagues for assistance in real-time – reducing the need to return to base for assistance.

Seeing is believing

ABI Research shows augmented reality is on the rise, and Gartner predicts businesses will purchase 53 million tablets by 2016. There are instant benefits for field service engineers. Mobile solutions now allow engineers to receive real-time feedback and expertise while on the job, enabling repairs to be completed more quickly and efficiently. An IFS partner, XMReality, is already working on pioneering augmented reality projects like this.

With this remote guidance, a support technician is able to watch and guide the engineer through every step of the repair without having to leave base. Using smartglasses, engineers are able to see a real-time and interactive demonstration of the repair job right in front of their eyes. These skills can be leveraged anywhere and anytime with the capability of modern mobile technology – drastically improving FTF.

Second: Beyond business analytics: predict and prescribe maintenance

The rise of IoT sensors and integrated technology on equipment is also enabling more efficient field service. Instead of scheduling maintenance when a fault is recorded, predictive analytics and the remote monitoring of equipment through IoT means faults can be detected before they become a problem.

Combined with business intelligence to make sense of the big data being captured through IoT, predictive analytics can be used to find actionable data to inform business decisions. Enabling service organisations to be proactive in regards to equipment performance, means moving away from calendar-based scheduling and towards predictive maintenance.

IFS has a predictive maintenance capability embedded in its field service applications, allowing better allocation of an engineer’s time. With sensors deployed on the factory floor, service organisations can monitor vibration analysis of bearings and predict when machine parts will start to degrade, then schedule maintenance proactively.

Field service solutions should be able to find and collect patterns of data from past actions and use this information to create generic rules to highlight how processes and services can be improved in the future – delivering new insight into operational efficiency.

Mobile devices are now able to run intelligent diagnostics and capture potential problems. Based on the diagnostic output, the mobile device is able to recommend a maintenance plan and the various tasks needed to be performed, before the engineer gets on site. This technology is going one step further than just predicting when faults will occur, and will prescribe which action needs to be taken in order to fully maintain that asset.

Prescriptive maintenance will take into account budget, time and other constraints and provide an optimal order of actions and the work orders to fully maintain that equipment – all in a matter of seconds.

Third: Staying ahead of schedule

First-time fix rates are an important KPI for field service organisations, but recent Blumberg research shows that the industry average for first-time fixes was under 80%, meaning 20% of jobs require additional follow-ups. Inefficient scheduling results in a lower first time fix rate and longer time to final resolution, as unqualified engineers can be sent and the necessary equipment may be unavailable.

Although not a new technology, schedule optimisation is a foundation on which new technologies can thrive. By combining scheduling with data from IoT devices, the next generation of schedule optimisation tools go much further and help to forecast field service demand, SLAs and potential resource needs – all in real-time.

IoT-enabled sensors can trigger actions when an event changes, and automatically re-schedule jobs around this. This combination allows field service organisations to improve FTF, MTTS and MTTR by consistently scheduling the right engineer for the right job, at the right time.

Don’t get left behind

These new technologies are going to bring serious benefits to field service organisations because they are so tightly integrated with delivering improved customer service and improved bottom lines.

In what is a dynamic and changing market – with tech-savvy customers demanding higher and higher levels of service – it is vital for organisations to be able to implement these cutting edge technologies.

The new breed of enterprise solutions takes away the risk

Traditional field service management solutions are simply too cumbersome and inflexible to enable field service organisations to reap the benefits. To quickly benefit from these latest advances, organisations need the backing of a new generation of flexible, agile enterprise solutions.

Traditional enterprise solutions can take months or years to simply implement, let alone adapt to an entirely new technology. The new breed of modular enterprise solutions are designed to remove the time and pain of modifying existing processes, and instead maximise the opportunities of new technology. These agile systems negate the need to fully customise legacy systems – a costly and timely process – and are enabling organisations to quickly adopt new technology, without the risk of losing out on a competitive edge.

These disruptive technologies and the digital transformation they require offer benefits that resonate throughout the whole service organisation – from the top-down. Strategic planners have real-time visibility to plan tasks and schedule the workforce in an industry with many unknown elements, from customer unpredictability to traffic, and even the weather.

This, in turn, directly empowers technicians, providing them with the right tools and information at their fingertips to better perform their job. But ultimately the most important stakeholder reaps the benefits – the customer receives the best possible level of service.

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Now IBM’s Watson joins IoT revolution in agriculture

Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production

IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.

By 2050, the world will need to feed two billion more people without an increase in arable land [1]. IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.

Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”

New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.

Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.” 

The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 [2]. Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.

The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.

Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.

IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.

The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

[1] The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”

[2] Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10


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What if Amazon used AI to take on factories?

By ANTONY BOURNE, IFS Global Industry Director for Manufacturing

Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.

At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.

If Amazon did factories

If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.

Manufacturing with knowledge

Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.

In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.

As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.

Knowledge for manufacturing

For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.

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