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Big data key to energy

The current energy situation in South Africa has caused many companies to look at ways to manage their power usage. However, JACO BARNARD of Wipro. says that in order to properly manage consumption, vast amounts of data need to be collected.

Given the current South African power and energy situation, energy management has become a necessity rather than a choice, particularly in the retail environment. Reducing carbon footprint while adopting sustainable strategies to balance business objectives with environmental responsibilities has become critical. Aside from increasing international pressure to adopt greener technologies as part of sustainability initiatives, the cost of energy has become a significant challenge. While energy-saving initiatives around lighting, heating, ventilation and cooling can provide assistance, these are often capital-intensive projects that need to be implemented effectively to deliver maximum benefit. In the low margin, high volume retail environment, it has become essential to keep the spiralling cost of energy under control to maximise profitability by optimising operational expenses. This requires data, and more importantly insight into data that can drive actions that will help retailers optimise energy management to curb costs.

The importance of data

For many retailers, problems with power supply can be catastrophic. Without power, cold chain logistics can be compromised and hundreds of thousands of Rands worth of perishable stock can be spoiled. In addition, stores themselves cannot operate, losing business and customers. As a result, many retailers have resorted to backup power and alternative energy sources. However, these initiatives are often costly, particularly if energy consumption is not managed and optimised. In order to manage the cost of energy, both from traditional and alternative power sources, effective energy management is required. This in turn requires data, as without data around metering, measurements and monitoring it is all but impossible to gain the insight required to manage energy consumption. Collecting consumption data is the first step, as this data can then be analysed to deliver the required insights to drive energy saving and improvement initiatives.

By collecting large volumes of data around energy consumption, costs, asset operations and business policies, it is then possible to determine potential operational savings. For example, temperatures can be optimised according to locational and seasonal climate, unnecessary lights and cooling can be switched off when not required, and efficiency of working assets such as refrigerators can be assured. This data can also be collected over extended periods and analysed to determine long-term trends, energy leakages such as chronic equipment efficiency issues, insulation problems and more. Savings can then be achieved by correcting major issues and fine-tuning operations and controls. There are hundreds of ways that energy consumption can be improved across areas such as lighting, electrical, cooking, air conditioning and refrigeration systems. This means that there are many opportunities for savings, but there is no ‘one size fits all’ approach. Big data and analytics are the crucial components in effective energy management.

Making big data work for energy efficiency

The first step in improving energy efficiency requires the establishment of savings protocols. Pilot studies should be carried out and savings strategies that can be actioned with data should be determined. The range or significance of savings can then be used to determine the feasibility of rolling these solutions out, based on spend and expected returns. The second step is to set up data collection mechanisms. The volume and method of data collection depends on the current technology deployed and how granular the data is required to be – for example monthly invoices on energy consumption will typically not provide enough visibility, so it may be necessary to implement a monitoring solution that provides sample data every half an hour to provide more accurate insight. Data may be collected via Building Automation Systems, directly through controllers or through management applications. Many legacy and proprietary systems do not allow any access to data, in which case metering and sub-metering analysis must be incorporated.

Simply collecting data will not enable retailers to determine savings, so once data has been obtained, it must be analysed in order to provide insight. This is a specialist skill set that may be expensive to maintain in-house, so often it is advisable for retailers to collaborate with an expert service provider. Given the volume of data, it is also advisable that structured methods and toolsets be put into place across all sites for analytical purposes, another area where an expert provider can assist. The final step is action, as savings will not result from insight alone. Volume is also essential, and retailers need to action initiatives based on insight across all or most of their sites to produce meaningful savings.

Beyond energy

Big data can be harnessed for more than just energy efficiency, and has potential to deliver significant additional advantage in the retail space. For example, the customer experience can be improved by collecting data from various channels and using it to improve in-store temperatures for comfort, or by utilising online channels and sensor data to tailor the shopping experience. Store layouts can be improved, footfall can be increased and more. In addition, analytics can be leveraged to improve asset maintenance to help bring down maintenance costs and improve asset life. The data from in-store devices, video and wearable technology has the potential to improve sales effectiveness and improve workforce productivity. All of these initiatives will use the same foundation of big data and analytics.

Ultimately big data analytics, whether in the form of energy management or other initiatives, can help retailers to improve their competitive edge. More efficient operations and reduced costs lead to enhanced profits, as do improved customer experiences. Data and analytics are the crucial elements to more successful, more efficient and more profitable retail environments.

* Jaco Barnard, Head of Retail at Wipro, South Africa.

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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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