Unless businesses ensure they also take people and processes into account when planning for disasters, they run the risk of not surviving them, writes SAKKIE BURGER, Managing Executive at Business Connexion.
Most companies prioritise the need for restoring IT in the event of a system breakdown. What they do not focus on, however, is what processes are in place to ensure the business can continue when automated or digital processes fail and specifically the role that employees have to play as they are ultimately the custodians of the processes that drive operations. It’s easy to, for example, provide a company with 10 seats to go and restore their IT systems and get them up and running again, but how do you accommodate a company with 100 employees that have just lost their premises in a disaster? This poses a different challenge and there are not many companies, providing disaster recovery in South Africa, that have the luxury of having that amount of space available waiting just to be occupied when there is a need for disaster recovery.
Although most companies are going the route of digitisation, manual processes still have a fundamental role to play. Take an airline, for example. If their electronic system for checking passengers onto the plane goes down, they have to have a manual back office process in place to perform this function. They cannot just ground the aircraft until the electronic system is restored. And herein lies the challenge: not many companies have these contingencies in place and they are putting themselves, their businesses and most important, their customers at risk.
While many organisations have these failover processes in place, they either do not test them regularly enough or their testing practices are inadequate. Many organisations have testing in place, but they perform a paper-based test. They see that there’s a manual process in place, the configuration is there and that it is documented, but that is where it ends. There is no actual testing from end-to-end by recovering on a piece of hardware and making sure it works, that the network is connected and that users can actually sign in and check the data. People tend to do disaster recovery tests to satisfy their auditors rather than making sure the business can continue to run in the event of a disaster.
There are a number of challenges in adopting an adequate disaster recovery strategy. The biggest challenge is the cost. You know you have to have it, but also that you might never need it. The second challenge is distance. What distance is the correct distance for you to have a disaster recovery site, particularly when you take incidents that could affect a broader geographical area into account? Here connectivity also comes into play, because the further away your disaster recovery is from your main site, the more expensive network constituencies become.
Possibly one of the biggest risks companies face is that, while they have disaster recovery processes in place, they tend to set it up on equipment that has become redundant or obsolete. In these cases companies have had to upgrade their equipment, so they use the new technology for their production line and then run their disaster recovery on the old machines. The challenge with this is that when they do need to do a recovery, they find that it’s not compatible or supported anymore, which means they are not capable of recovering core systems in reasonable timeframes.
DR often does not get the attention it deserves because it is an expenditure that is not really productive. That is why there is a trend to outsource their disaster recovery to a third party, where there is an agreement that they have to have the necessary equipment in place to ensure they can run your disaster recovery effectively and efficiently.
Companies that are either re-looking their disaster recovery strategy or implementing it for the first time, need to ensure that they understand which of their applications are the most critical as a first step. Some applications don’t need disaster recovery contingency and you can run your business without them. Interestingly though, between 5 and 7 years ago mail wasn’t deemed a high priority application. Today, that is deemed the first thing companies want to have recovered, because it has become mission critical to the running of their businesses.
Times have certainly changed
Companies must also understand the technology that is involved. You can’t just move a workload from a Unix platform to a Microsoft platform. You must ensure that the work breakdown structures and standard operating procedures and processes are documented, tested and updated at least twice a year. It’s easy to just write a process and file it away in a cupboard and do nothing further with it. It needs to be tested vigorously and on a regular basis. It’s not just about testing it, it’s about change management and fixing problem as and when you are presented with them.
Often change management is the biggest problem in disasters. A disaster happens because something changed and a change request didn’t notify the disaster recovery process of this change. If your disaster recovery manual is not up to date, it could significantly increase the amount of time spent to fix the problem.
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 . 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 . 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.
 The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”
 Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10
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.