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Can machine learning solve IoT data challenge?

A new report from Forrester advises CIOs to leverage machine learning to turn the tsunami of data obtained in Internet of Things (IoT) deployments into actionable insights.

Successful companies in the industrial sector that are doing this are not only predicting problems and opportunities before they occur, but are also developing new revenue streams during their digital transformation.

Large volumes of data are required to train and then exploit machine learning algorithms, and fortunately that data is now easily accessible, especially as IoT gains traction in industries. According to Forrester’s Paul Miller, senior analyst serving CIO professionals and lead author of the report, “Put Data to Work in the Industrial Internet of Things,” machine learning is becoming a powerful tool in efforts to win, serve, and retain customers.

“It’s easy to focus on automating or augmenting existing processes with IoT, and this can deliver real cost savings and efficiency gains. But the bigger opportunity is using IoT and machine learning to drive entirely new business models, with far-reaching implications for the way in which your products are built, sold, used, and maintained,” explains Miller in the report.

Some organisations are already seeing good results by combining machine learning with IoT:

  • Ocado, one of the UK’s online-only grocers, has augmented its human packers with robots that swarm and cooperate. Average picking times have dropped significantly from two hours to just 15 minutes.
  • HUK-Coburg, a German car insurer, has partnered with IoT and telematics company Robert Bosch to develop a usage-based insurance and rescue solution which monitors driving patterns and rewards safe driving habits. Good drivers have seen premiums drop by as much as 30 percent.
  • Siemens’ claimed that shortly after giving control of the turbines to a set of machine learning algorithms at a gas-fired power station, emissions of nitrogen oxides reduced by almost 20 percent beyond the best engineers could achieve.

Miller also points out that Forrester currently identifies three core scenarios driving IoT adoption: designing connected products and experiences; operating connected business processes; and consuming connected insights. He also says that Forrester is now observing three broad classes of adoption for IoT.

Asset monitoring and control 

Although basic asset monitoring and control is rarely exciting, the report points out that this is often the first experience of IoT within the industrial sectors. Moreover, Miller writes that when the experience is done right, its return on investment could free up the resources to pay for future developments.

Some examples of these uses include smart meters to monitor energy usage; keeping track of movable assets in the transport sector; and managing temperatures in smart buildings.

Prediction and action 

The report acknowledges that the migration from asset monitoring and basic control to prediction and action is a big step, particularly for manufacturing firms that have typically focused on the physical aspects. Forrester advises that in order to succeed, companies must gather data from their own systems and from the environment in which those systems operate. They should extract insights from that data, (perhaps using the digital twin concepts that most IoT platforms support), and then interpret those insights and take action.

According to Miller, data and the insights extracted from it, are key to digital ecosystems that so many organisations now try to control.  IoT devices are an important source of data, but it’s vital that organisations understand and use the data in a timely and effective manner. Forrester believes that this is an important juncture where machine learning begins to play a real part in an organisation’s use of IoT.

Some examples where this next step in the IoT / machine learning can benefit companies include: Smart buildings which monitor weather and adjust temperatures in anticipation; transport companies anticipating failure as a means to better manage moveable assets; and building supply chains which are able to adapt to allow for customisable production, but still retain efficiencies and optimisation of resources.

Powering new business models

While the progressive use of IoT and machine learning is helping drive efficiencies as described above, Forrester believes the truly digitally minded CIO can make use of IoT and machine learning to imagine and implement entirely new business models.

Some examples of these new businesses models include: train-as-a-service offerings where the manufacturer owns and maintains the trains and simply sells their services to the rail companies; and compressor manufacturers selling compressed air by the litre to buildings. In both these instances, the manufacturer can monitor equipment, predict failures and ensure less downtime, while the customer gets exactly the service they need at a more competitive rate, without carrying the asset on their books.

Finally, Forrester cautions that while companies make the transition from physical to digital organisation, CIOs will need to ensure that they facilitate the transition and avoid putting a chokehold on the evolution – which could, ultimately, damn the organisation to irrelevance.

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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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