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Rural Sourcing Benefits From Digital Agribusiness Solutions To Meet Global Food Needs

Tanja Reith

With the world population headed toward the 10 billion mark in the coming years, the need for healthy, sustainable and fairly produced food will increase accordingly. Rural sourcing will be integral in meeting these needs. Hyperconnectivity in business and digital transformation of agriculture processes can create a stronger operational foundation for rural sourcing. This transformation can bring affordable, sustainable agricultural production by facilitating smart, traceable solutions throughout operations from farm to fork.

Reimagining smallholder farming network management

Modern farmers are surrounded by a complex system of equipment, vendors, processors, manufacturers, and agrichemical specialists. These technologies are often new to farmers in rural areas and developing countries; however, they can assist them in becoming far greater contributors The processes involved in rural sourcing must stay in sync with increased demand and changing consumer behaviors. Smallholder farming operations can benefit immensely from food traceability and hyperconnectivity innovations. This digital transformation is set to bring agricultural production to new heights of productivity and effectiveness, with additional benefits for rural farmers.

Rural sourcing made easier with digital business solutions

Rural sourcing is an area that’s ripe for transformation through digital agriculture solutions. Current and emerging digitization processes can assist with taking rural sourcing from smallholder farmers in developing countries to new heights of viability and success. The agribusiness value chain can be effectively streamlined, improving smallholders’ lives in a range of positive ways.

For example, rural farmers will be able to connect with financial services more readily. They will have a range of opportunities that were not available to them in the past. A blend of supplier and collaborator business networks, workforce engagement, assets, and the Internet of Things (IoT) assist all of the elements of this process to work together within a digital core. While the digital core facilitates the management of all financial and contractual data, collaboration within a supplier network is also seamless throughout the entire process. Mobile advances, the cloud, and IoT all come together in innovative software solutions to manage all the crucial components of rural-sourcing digitization.

The following are some of the key areas:

  • Identify farmers and expected crop yields. Research and due diligence in a rural area are streamlined and made intuitive, accurate, and efficient.
  • Broadcast prices and plan logistics. Keeping all relevant parties abreast of pricing considerations and key processes is fully automated, as are the processes themselves.
  • Consult and train farmers. Getting rural farmers up to speed and at top efficiency is also enabled with software solutions.
  • Record quantities and qualities. Full tracking of products from farm to fork is easier than ever with digital agriculture. Both quantities and full descriptions can be included.
  • Truck loading and offloading. Hyperconnectivity of the processes involved in digital farming ensures full food traceability until it reaches its destination – and at every phase along the way.
  • Mobile data exchange/track and trace standards. Data can be exchanged rapidly while on the go and kept up to date for all parties involved. Information, correspondence, and productivity data are always readily accessible, as is key information about products that are en route.
  • Mobile and SMS payments. All financial aspects of digital farming, including SMS payments, financials, and controlling, can be effectively managed with software solutions.

Processes involving integrated sourcing for crops or commodities from rural areas assist farmers by making training and best practices available. Tracing shipments in line with area and industry standards is also supported. It all starts with a direct connection to local farmers and allows for the accurate tracking of the resultant products to ensure efficiency. Digital farming can bring it all together. Transparency of origins is improved as well as the settlement process, reducing fraud risk. The hyperconnectivity of digital farming allows for more effective food traceability, more efficient farm operations, better food safety and quality, and an overall more beneficial experience when working with the rural farmer.

With effective rural sourcing management, everyone wins

Businesses implementing digital farming can more readily and consistently provide sustainable processes and positive working conditions. Farming practices can more easily be kept in line with fair trade labels and other key standards and certifications.

Learn more about digital transformation for the agribusiness industry
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Tanja Reith

About Tanja Reith

Tanja Reith is a solution manager for the Agribusiness vertical in the Industry Cloud organization at SAP. She has over 15 years of experience in solution management and go-to-market roles for enterprise software, engaging closely with customers and partners across different industries such as agribusiness, consumer products, and financial services. Tanja’s ambition is to drive shared value resulting in business value to our customers while making a social impact and improving people’s lives.

Future Cities: A Nexus Of Smart Utilities, Smart Transport, And Smart Tourism

Dr. Hichem Maya

Increasing urbanization represents one of the greatest challenges and opportunities today. The goal is to sustainably and holistically develop these future cities that will work on a smart, integrated, and connected digital network to improve livability and grow economic prosperity.

Innovation in this context means different things to different sectors. For citizens, it’s about quality of life, well-run institutions, and jobs. For businesses, it’s about the ability to thrive and innovate. For urban governments, it’s about transforming operations, empowering officials, and engaging better with citizens.

One of the top priorities of Dubai Plan 2021 is to create a smart and sustainable city that is integrated and connected. By developing and investing in a long-term strategy for success, governments can collaborate with technology providers on things such as improving traffic congestion, rethinking the distribution of utilities and resources, and optimizing tourist movement throughout the city.

Smarter traffic flow to reduce congestion

Congestion costs cities billions in fuel and wasted time, as well as increased accidents and pollution. Combining an intelligent traffic management platform and a traffic congestion management system could be the solution Dubai needs to manage roadway traffic. This approach involves intricate measurement of traffic flows and congestion to inform city planning.

Big Data from smart applications can deliver instant insights on traffic flows to help streamline movement within the city. Centralization of data from smart sensors supports data quality and contributes to a traffic data model with a flexible interface for leveraging tools and value-added innovations.

Smart sensors, along with algorithms applied to incoming data, can also enable decision-making to improve congestion and traffic flow. These tools can provide an early warning system for traffic problems, support public travel guidance systems, and create a working framework for traffic control from all perspectives. In each case, the aim is a safer, less congested city.

Smarter utilities to save resources

To meet customer, regulator, and shareholder expectations, the role of utility companies is expanding beyond providing utility services into connecting various components of the new energy economy.

Water is a critical resource that’s in short supply, and leaks, which are costly in terms of money and the environment, must be promptly fixed. IoT sensors in the water distribution network detect leaks early on, reducing the risk of water pollution and conserving scarce resources.

Energy distribution can be controlled with smart billing that incorporates meter and device management, as well as enterprise asset management. The results are significant: reducing the annual service and maintenance cost by 31% by resolving issues at the root, and achieving a 71% improvement in recordable accident frequency by integrating safety and health systems with asset management.

Smart tourism to welcome visitors (and support citizens)

Dubai’s smart city initiative helps tourists to feel welcomed in the city by promoting the local economy, capitalizing on tourist expenditures, and distributing tourism to minimize crowding. The advantages of the network include information about real-time traffic, local attractions and landmarks, and transportation arrival and departures.

The tourist network connects public and private enterprises with tourists, visitors, and constituents. Leveraging Internet of Things capabilities; Big Data; predictive algorithms; and in-memory computing, mobile, and hybrid cloud platforms delivers the right proposals and offers from the relevant service providers to tourists at the right time, in the right context, and based on geolocalized and personalized profiles.

iBeacons placed at selected tourist attractions could push information of interest to the user. Businesses can also leverage user behaviors and selections to target and engage customers more effectively. Not only will this system improve the tourist experience, but by successfully distributing tourist crowds, citizens will enjoy a better quality of life.

With this network, the city can also create tourist profiles to improve urban planning and better balance visitor crowds by enticing tourists to change their plans in response to compelling offerings made by the city’s partners.

The vision for a smart city platform will enable Dubai to establish an urban network that can directly link small business owners with customers (both citizens and visitors) through low-cost distribution channels.

For more on how cities are leveraging smart technology, listen to experts discuss Smarter Cities: Future Metropolis and Societal Impact.

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Dr. Hichem Maya

About Dr. Hichem Maya

Dr. Hichem Maya leads the industry digital transformation and value engineering team in the Middle East and North Africa at SAP. The organization helps businesses from a variety of industries to identify the proper value generated through digital technologies adoption.

How Digitalization Is Helping To Bring Clean Water To India

Ajitabh Das

Staggering economic growth may have improved the lifestyle for millions in India, but there are still millions who struggle for basic amenities like clean water.

According to a report by WaterAid, a global advocacy group on water and sanitation, around 63 million Indians don’t have access to clean drinking water. To compound this shortage, an estimated 40 per cent of the supplied water is lost to leakages in pipes and connections. Those statistics are why the Indian government has accorded a high priority to universal access to drinkable water.

To meet the enormous demand and improve the delivery of clean water, Indian water storage and transportation companies like Vectus, based in the northern city of Noida, have turned to IT to increase their operational efficiency. Vectus, a leading producer of water tank and piping devises, has 13 manufacturing and 13 Depots sites across India and has grown at an average annual rate of 35 percent. Despite achieving this impressive annual growth, the company faced operational performance challenges with its IT systems.

Employees  had  to  manually  record  customer  orders,  process  billing,  and  keep  track  of  product dispatches. The operational task was laborious and often inaccurate. According to  Manish Sinha, head of IT at Vectus, they “faced major issues with server downtime that caused staff to put in extra shifts to enter data, costing up to 12 million Rupees (USD 180,000) in overtime payments.”

To address their problems, in just four and half months, the company went live in all its 24 locations with next-generation ERP business suite and  has seen impressive results. Sinha says that after going live “we have experienced no downtime and have not once had to restart the server during working hours. In all, we believe we have increased total operating efficiency by 60 percent across the company.”

Vectus has seen  50 percent faster access to real-time data to monitor business performance, enabling smarter budgeting and planning. They can now check real-time inventories to know which products are selling. That helps them plan production and determine which products they should emphasize more for market promotion. By comparing real-time sales data with inventories, the company reduced waste and total procurement cycle time from over 21 days to just 15 days. Because of one-click accounting and cross-business transparency, the company is better prepared to meet compliance regulations, complete audits, and quickly report financial information.

Digital intervention will not fix all the problems related to India’s perennial water shortage, but it does provide a new effective tool for companies like Vectus to drive efficiency and effectively deliver much- needed clear water storage and transportation solutions to its customers.

For more on how technology can improve lives, see From Forest To Pharmacy: Analytics Enables Holistic Healing.

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Ajitabh Das

About Ajitabh Das

Ajitabh Das is a fellow for the SAP News Center editorial team at SAP.

Data Lakes: Deep Insights

Timo Elliott, John Schitka, Michael Eacrett, and Carolyn Marsan

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

Data lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.

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Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

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Artificial Intelligence: The Future Of Oil And Gas

Anoop Srivastava

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. Shell, for example, shelved its plan to invest in Qatar, Aramco put on hold its deep-water exploration in the Red Sea, Schlumberger fired a few thousand employees, and the list goes on…

In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment.

The result is a dire need to optimize performance and optimize the cost of production per barrel. Companies have many optimization opportunities once they start using the massive data being generated by oil fields. Oil and gas companies can turn this crisis into an opportunity by leveraging technological innovations like artificial intelligence to build a foundation for long-term success. If volatility in oil prices is the new norm, the push for “value over volume” is the key to success going forward.

Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.

When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. As an example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease dry wellhead development by 10 percent.

How oil and gas can leverage artificial intelligence

1. Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

2. Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

3. Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines: reservoir engineering, geology, production technology, petro physics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.

AI techniques can also be applied in other activities such as reservoir characterization, modeling and     field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level.

Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

4. Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enables us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

5. Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013, when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea.  In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil to BP, allowing the company to survey pipelines, roads, and equipment in Prudhoe Bay, Alaska. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

6. Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Current adoption of AI

Chevron is currently using AI to identify new well locations and simulation candidates in California. By using AI software to analyze the company’s large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods. Chevron is also using predictive models to analyze the performance of thousands of pieces of rotating equipment to detect failures before they occur. By addressing problems before they become critical, Chevron has avoided unplanned shutdowns and lowered repair expenses. Increased production and lower costs have translated to more profit per well.

Future journey

Today’s oil and gas industry has been transformed by two industry downturns in one decade. Although adoption of new hard technology such as directional drilling and hydraulic fracturing (fracking) has helped, the oil and gas industry needs to continue to innovate in today’s low-price market to survive. AI has the potential to differentiate companies that thrive and those that are left behind.

The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position  to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting cognitive technologies, the future looks bright.

For more insight on advanced technology in the energy sector, see How Digital Transformation Is Refueling The Energy Industry.

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Anoop Srivastava

About Anoop Srivastava

Anoop Srivastava is Senior Director of the Energy and Natural Resources Industries at SAP Value Engineering in Middle East and North Africa. He advises clients on their digital transformation strategies and helps them align their business strategy with IT strategy leveraging digital technology innovations such as the Internet of Things, Big Data, Advanced Analytics, Cloud etc. He has 21+ years of work experience spanning across Oil& Gas Industry, Business Consulting, Industry Value Advisory and Digital Transformation.