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Yield Guarantee Program Supports Farmers While Mitigating Financial Risk

farmers

Yield Guarantee Program Supports Farmers While Mitigating Financial Risk

Farmers and enhancement opportunities are the primary focus of the latest partnership announced this week between Growers Edge Financial, Inc. and GROWMARK, Inc.

While some might associate the agriculture sector with outdated operations, the two companies will offer farmers an opportunity for enhancing efficiencies while maximizing profits through the Yield Guarantee Program from Grower’s Edge.

“In today’s stressed farm economy, farmers are incredibly wary of taking on more financial risk – even when taking that leap could boost profitability. They need guarantees,” said Joe Young, president and chief operating officer, Growers Edge. “Working with strategic partners like GROWMARK, we are providing the financial incentives farmers need to confidently adopt the new technologies that can ultimately drive their long-term sustainability and business success.”

Through carefully and strategically combining AI from Growers Edge’s Growers Analytic Prediction System (GAPS) and information gathered from GROWMARK’s Product Yield Trials, farmers can now rely on the predictive performance and exactly how to benefit from the technology, minus the increased risk for wasted resources and costs.

GROWMARK is committed to helping our customers grow their bottom line with new ag technologies, which makes Growers Edge an ideal partner for us,” added Lance Ruppert, director of agronomy marketing technology, GROWMARK. “The Growers Edge team is removing some of the risk and creating a new value stream for both the farmer and our technology providers. We think the yield guarantee program will help customers deploy the technologies needed to improve profitability, and we are eager to see it in action.”

To read more about how this is changing farming strategies, please visit: Growers Edge Financial or GROWMARK.

machine learning

How Machine Learning Is Transforming Supply Chain Management

Supply chain management is a complicated business. A lack of synchronization or one missing entity can interrupt the entire chain and result in millions in losses.

In a market environment where businesses are continually striving to cut costs, increase profits, and enhance customer experience, disruptive technologies like machine learning offer a window of opportunity. By exploiting the enormous amount of real-time data and leveraging the cloud power, it improves decision making, process automation, and optimization. It can create an entire machine intelligence-powered supply chain model. It also helps companies improve insights, mitigate risks, and enhance performance, all of which are crucial as the global supply chain war wages on.

Gartner recently announced that innovative technologies like blockchain and Artificial Intelligence (AI)/machine learning would significantly disrupt existing supply chain operating models. In addition to advanced analytics and Internet of Things (IoT), machine learning is considered one of the high-benefit technologies. This is because it allows dynamic shifts across industries and enables efficient processes that result in significant revenue gains or cost savings. 

So, it is no surprise then that, in another industry update, Gartner predicted that at least 50% of global companies would be using AI-related transformational technologies in supply chain operations by 2023.

There are three key ways in which these transformational technologies empower businesses:

Monitoring: By connecting equipment, products, and vehicles with IoT sensors, companies can monitor goods and operations in real time.

Analyzing: Advanced analytics convert data into actionable insights and help businesses understand the reason behind specific incidents and how they impact the business.

Acting: Valuable insights as a result of data crunching help businesses address planning challenges and automate processes to improve efficiency.

So, adopting machine learning in supply chains is critical for companies to stay competitive in the long run. However, what aspects of the supply chain will be impacted by machine learning? Let us find out.

A Myriad of Benefits to Supply Chains

If you get the algorithms right, the benefits of using machine learning are innumerable. The algorithms can predict supply trends based on human behavior, resulting in personalized customer service with lower inventories and better utilization of resources. We take a look at several such benefits of machine learning below.

Brings Real-Time Visibility Which Improves Customer Experience

According to a Statista survey, visibility is a significant organizational challenge for 21% of supply chain professionals. Visibility has been a buzzword in supply chain circles for more than a decade now and every technology so far has promised to improve visibility in some way. But, is machine learning contributing anything here? 

The combination of IoT, deep analytics, and real-time monitoring is improving supply chain visibility, helping businesses achieve delivery commitments and transforming the customer experience. By examining historical data from various sources, machine learning workflows discover complex interconnections between various processes along the value chain.

Amazon is a prime example as it is using machine learning to enhance its customer experience by gaining an understanding of how product recommendations influence customers’ store visits.

Cuts Costs and Reduces Response Times

As per Amazon’s regulatory filing in 2017, their shipping costs increased from $11.5 billion in 2015 to $21.7 billion in 2017. And, it’s not just Amazon. Many other players are struggling because of rising shipping costs. In fact, in one survey, more than 24% of supply chain professionals expressed that delivery costs are the biggest challenge for B2C companies.

By applying machine learning to handle demand-to-supply imbalances and trigger automated responses, businesses can improve the customer experience, while minimizing costs. Operational and administrative costs can also be reduced by integrating freight and warehousing processes and improving connectivity with logistics service providers.

Machine learning algorithms’ ability to analyze and self-learn from historic delivery records and real-time data helps managers and dispatchers optimize the route for each vehicle. This allows them to save costs, reduce driving time, and increase productivity. 

Machine learning can also be used to detect issues in the supply chain before they disrupt the business. Having an effective supply chain forecasting system means a business has the intelligence to respond to emerging threats. And, the faster a business can respond to problems, the more effective the response will be.

Streamlines Production Planning and Identifies Demand Patterns

When it comes to machine learning’s role in optimizing complex supply chains, production planning is just the tip of the iceberg.

Sophisticated algorithms are trained on existing production data in such a way that they start identifying future buying, customers’ ordering behavior, and possible areas of waste. This helps businesses tailor production and transport processes to actual demand as well as improve their relationships with specific customers.

For example, by anticipating and acting on the specific needs of your customers before they even arise, businesses can establish themselves as reputed brands capable of recognizing customer needs. 

There is so much volatility in global supply chains that it will be challenging to forecast demand accurately, without technologies like machine learning. However, reaping the full benefits of machine learning might take years. So, businesses should plan for the future and start taking advantage of the machine learning solutions available today.

Investing in machine learning and the related technologies today means increased profitability and more resources for your business tomorrow. Businesses that can use machine learning in their supply chains will have better plans, resulting in less “firefighting” and fewer inefficiencies.

 

Tech Mahindra Ltd. Opens O’Fallon, MO Location

M Property Services officially announced the addition of Tech Mahindra Ltd. to its WingHaven Development earlier this week. The technology-focused global company – which is specialized in areas pertaining to digital transformation, consulting, business reengineering and software solutions, now boasts an address on Technology Drive in the O’Fallon, Missouri region.

“Due to the many amenities throughout O’Fallon and the WingHaven development, we were able to invite a world-class tech company to open a facility within the city’s boundaries which would allow it to continue supporting world-class companies in O’Fallon, MO and numerous other large companies outside of the O’Fallon area,” said MPS Chairman Paul McKee, Jr. “So many of Tech Mahindra’s employees currently live in O’Fallon and WingHaven, so the location for the new technology center was ideal.”

As innovative solutions for micro services, automation, artificial intelligence, security, machine learning, cloud computing, big data, data and analytics, and blockchain serve as primary drivers behind the expansion, Tech Mahindra’s new 14,000-square-foot Technology Center also supports efforts in addressing the needs of customers.

“As part of our TechMNxt charter, we are committed to inspire our partner ecosystem, academia and employees to focus on innovation in next gen technologies and customer experience,” said CP Gurnani, Managing Director and Chief Executive Officer at Tech Mahindra.

“We believe it is our responsibility to invest in the local communities we operate in, and this is a step towards supporting increase in employability of future technologists, and delivering enhanced experience to our customers globally. We look forward to seeing the innovations that come out of this center as we develop real-world solutions for a digital future,” Gurnami concluded.

TeleSense Addresses Global Grain Ecosystem Challenges

Grain spoilage may soon be a thing of the past for global grain operators and transporters. IoT tech innovator TeleSense recently announced its acquisition of Danish wireless sensor technology company, Webstech. Through this acquisition, TeleSense announced it will amp up IoT efforts as it now has access to the largest global remote-sensed dataset and plans on integrating Webstech’s industrial automation capabilities, solar/battery power functionality and sensor spears to its current solution.

“Spoilage and energy optimization in drying grain continue to be multi-billion dollar issues; TeleSense provides the data insights needed for players throughout the global grain ecosystem to improve safety and profitability,” TeleSense CEO Naeem Zafar said. “The acquisition of Webstech greatly accelerates our entry into the European market and provides millions of additional historical data points to further refine our machine learning technology and predictive algorithms.” 

The TeleSense GrainSafe™ AI platform serves the grain supply chain as a scalable solution through monitoring temperature and humidity levels and providing real-time view of the stored grain to ensure quality conditions are maintained. With this innovative, portable, and wireless solution, the company is ready to expand its presence beyond the U.S. and Australia and make its entry in the European regions.

“How grain is stored, handled and traded in the years to come will change as new IoT-enabled technologies take hold throughout the supply chain,” added Peter Votkjaer Jorgensen of Maersk Growth Ventures. “We think that this acquisition by TeleSense will accelerate the mission of higher sustainability and efficiency in the grain supply chain.”

The company also confirmed it will expand with a new Denmark office and onboard two new additions to the team. Maersk Growth Ventures’ Peter Votkjaer Jorgensen will serve on the Board of Directors of the newly created TeleSense Europe ApS subsidiary and Webstech’s CEO Thomas Kylling will serve as managing director for TeleSense’s European team.

“After operating in the European remote sensing space for almost a decade, I was absolutely blown away by TeleSense’s integration of data science with an IoT solution for grain,” noted Kylling. “I think that TeleSense will help drive the automation of the grain supply chain, and I’m excited to help lead the effort in Europe.” 

forex

How to Analyze Data for More Profitable Forex Trading

Successful forex trading is the art of being able to predict when currencies are going to shift in value in relation to each other, and what direction that shift is going to be in. The good news is that those fundamentals are relatively simple; is the dollar going to weaken against the yen? Will the pound pick up against the euro? Another piece of good news is that there are huge swathes of data available to the average retail trader to enable them to make these decisions. Of course, you may opt to rely on your instincts and make decisions as the situation in the various currency exchanges unfolds before you.

While there is a place for this kind of fast thinking and quick decision making in forex trading, it will only ever form the basis of a stable and successful long term strategy – one which delivers consistent levels of profit – if the quick decisions are built upon the foundations of a clear and thought through long term plan. And this kind of planning is only possible if you know exactly what kind of data to be on the lookout for, and about the tools which are available aid in your analysis. 

The complexities of big data in the age of seamless digital communication are such that it would be impossible to summarise every possible metric or analytical approach accessible to the retail trader in the space available. What is possible, however, is an overview of the main planks of data analysis a trader needs to bear in mind, and a look at a few of the types of tool which can make that analysis easier and more accurate.             

Forex Fundamentals 

When a trader buys and sells shares the analysis required is focused, in the main, on the good health of otherwise of the company in question, and whether the various indicators predict that the shares are likely to rise or fall in value. Wider market conditions have an impact as well, of course, but these conditions would be the same for any stock being traded, which places the emphasis firmly on the choice of stock.

Where forex trading is concerned, however, the fundamental issue is always going to be the relative strength and weakness of a pair of currencies. Looking ahead in an effort to take advantage of shifts in value means analysing macro-economic figures such as interest rates, unemployment rates and GDP (gross domestic product). Many of these figures are firmly fixed in the economic news cycle, meaning it’s simple for a trader to see in advance when a country or bloc such as the EU is likely to announce figures which might impact on currency fluctuations (predicting what this impact will be is a more complex matter altogether, of course).

Requiring more vigilance to spot, on the other hand, are the sudden shifts which might be triggered by an event such as a comment in a ministerial press conference which is assumed to increase the chances of a no-deal Brexit and so sends the value of the pound dropping. Fundamental analysis based on one-off events of this kind requires a close attention to detail, up to the minute (or even second) access to newsfeeds and the willingness to take up positions instantly.        

Technical

Technical analysis is based not on real world events beyond the confines of the currency exchanges, but on in-depth analysis of the way in which the price of currencies has moved in the past. By focusing on charts of price movements and analysing them with a variety of tools – both manual and automatic – a trader can identify patterns which have repeated in the past and can be expected to repeat again in the future. Past performance is no guarantee of future success, of course (some clichés become clichés because they happen to be true), but the relative stability of the major currencies, over the long term, means that patterns of movement can become relatively predictable. 

Market Movements

A further method of analyzing the forex markets is by watching out for larger than usual shifts in the number of traders investing in a particular currency. As soon as a large number of traders invest in a particular currency, the future pool of people who might opt to sell that currency expands, with the result that the potential value of that currency is impacted upon. Analyzing market movements could be referred to as depending upon the wisdom of crowds. As has been shown in the past, that wisdom can often be mistaken. A stampede to buy or sell a specific currency could be triggered by knowledge of where the value of that currency is heading, but it could also be caused by a simple self-fulfilling prophecy – sometimes, if enough traders take a position, enough other traders assume there must be a good reason for doing so and follow suit, creating a pattern which feeds off itself with little or no external justification.     

It’s not a question of which of these three modes of analysis is the most effective, since the best results will always be gained by combining elements of all three. The deluge of data which is available, however, particularly where technical analysis is concerned, means that the wiser trader will make use of some of the tools which are available:

Session highlighter

One of the key attractions of forex trading is the fact that the currency markets are open somewhere in the world 24 hours a day throughout the week. The fact that different markets are open at different times of the day means that the sessions within those markets are likely to have different impacts on the pairs of currencies which a trader is working with. A session highlighter tool can be used to divide a traders charts into these various sessions, and then to highlight any movement that occurs over set periods, such as a minute, a specific number of minutes or an hour.     

Volatility Tool for Forex 

A volatility tool will show a trader how much, and in what way, a pair of currencies has moved on an hourly basis during a period such as the last thirty days. This enables the trader to build up a fuller picture of the way the currency pair behaves, and note any patterns such as recurring movements on specific days or at a specific time of the day. The more advanced versions of the tool will calculate the typical movement range and, given a time period by the trader, will display a percentage probability that the pair will stay within the set range.   

Signal service

Signal service providers offer instant information in the form of tips, delivered either by experts or AI systems, which recommend trades are made at a certain time and price on the basis of analysis. There are different types of signal services available, some based on fundamental analysis (i.e. news which might impact on the markets) and some on technical analysis. Signals shouldn’t be confused with the kind of AI that trades automatically on your behalf – they are merely providing information in a timely manner which it is up to you, as a trader, to interpret.  

Undertaking and applying analysis is a key practice of any successful trader. The degree of analysis a trader carries out will depend upon their inclination and appetite for hard number crunching, but the rule to remember is that while there really isn’t such a thing as too much analysis (as long as it’s used to eventually take a position), the concept of too little analysis is all too real.   

FinTech

FinTech: 5 Automation Trends That Are Impacting the Industry Right Now

The FinTech industry is rapidly moving toward automation as a source of efficiency. The move to specific tools and software programs increases speed and accuracy of processes. It also keeps employers on their toes as they need to quickly evolve and learn. Many of these programs previously required specialized training and adaptability.
Automation helps with repetitive procedures and simplifies complicated tasks. It increases accuracy and safety measures, while minimizing human error. Expectations indicate that the FinTech industry will extend its tech integration significantly over the next four years.
Here are 5 automation trends that are impacting the Fintech industry right now:

1. Human Resources Management: This used to be one of the least automated components, but now software like Workday and 15Five are building platforms to assist workflow with related systems that support employee management. Finance companies increasingly recognize that their people are the most valuable resource and need to be managed more thoughtfully as well as efficiently.

2. Mobile: Finance companies now consider mobile oriented tech as part of the core work-flow. The industry relies heavily on its ability to get work done efficiently. FinTech continues to utilize software which speeds up communication and productivity. Mobile used to be considered a security risk by the financial industry. Now it is considered a way to enhance productivity as well as provide more flexible workflow for employees.

3. Customer Support: More automation is taking over customer service. This support has advanced tremendously with certain software programs that include internal systems to support customers. Software systems such as Fresh Desk and Zen Desk are cutting down on the head count needed for customer service departments in some companies. But more importantly these new systems are improving the customer experience and the lives of the people working in those departments.

4. Billing/Invoicing: Payments systems like Stripe, invoicing and billing systems like Freshbooks, and more advanced ERP systems Netsuite are examples of programs that continue to reinvent the way FinTech is automating business functions. Although many companies are still at least partially stuck in the past of creating manual invoices and payments, these automated systems are increasingly taking over. Both the customer and the vendor win with greater automation in this area. Vendors cut costs and get paid faster. Customers benefit from this greater efficiency of vendors with lower prices or higher value delivered for their purchases.

5. Accounting: Xendoo, Zoho, Quicken online and other systems automate are automating the accounting, bookkeeping, and tax filing functions of businesses. Traditional accounting software, and human bookkeepers and accountants, still have an important role to play in this area, but the accounting business is rapidly changing as well due to technology. The number of people involved with these activities is likely to shrink dramatically as automation takes over more of these functions. Ultimately businesses and their customers will benefit from this via lower operating costs that allow for better value to be delivered rather than spent on administrative functions like accounting.

It is crucial for companies of all sizes to be knowledgeable about this trend and keep their business updated as automation continues to reinvent Fintech industry jobs. You have to be able to adapt quickly to these changes. Our previous ideas and habits of doing business are changing, and we have to keep up with those changes or be left behind by competitors who will adapt more quickly

Automation is impacting Fintech employees in a variety of complex ways so it’s critical for employees to have a greater understanding of and training on different software systems to ensure they keep up with the automation and benefit from it rather than viewing it as a potential threat to their jobs. There is no way to stop technology. All of us need to work hard to stay on the right side of its inevitable progress.

Resilinc: AI to Support Weather Risk Assessment & Mitigation for Suppliers

Leading provider of supply chain visibility, Resilinc, identified a major oversight in terms of weather-related risk assessment and preparation within the supply chain. The company released surprising statistics revealing how unprepared supply chain suppliers are during potentially disruptive events such as hurricanes and weather-related catastrophes, sparking the deployment of an AI and data sciences-based hurricane-preparedness solution to better prepare supply chain resilience.

Among the statistics revealed in the Resilinc supply chain database, 35 percent have poor logistics recovery, 27 percent of supplier sites lack business continuity procedures, and 37 percent have no backup power.

Unfortunately, it comes as a surprise to many supply chain managers that a large proportion of their suppliers are woefully unprepared to withstand major disruptive events like hurricanes,” said Sumit Vakil, Resilinc CTO. “This lack of transparency is especially true in the sub-tiers of a supply chain.

More than seven years of supply chain and hurricane data in conjunction with the company’s expertise was combined to create a customized, automated solution for avoiding and assessing the risk at hand in the face of hurricane-related disasters and weather-related disruptions.

The company outlined the following core capabilities of the solution to include: multi-tier supply chain mapping down to the product and part-level, supplier surveys and site readiness assessment, dashboard incorporating AI and recommendations, and ongoing monitoring throughout hurricane season featuring real time supplier impact confirmation during live events.

Taking it a step further, Resilinc’s solution will evaluate customer key metrics, supplier site vulnerabilities, regional hurricane risks, revenue risks, and more.

“Based on data, heuristics, history and other factors, Resilinc will come back with very specific recommendations, such as ‘move inventory from that site,’ or ‘evaluate your safety stock for that part,’ to provide clients specific targeted recommendations to mitigation action and protect revenue,” said Resilinc Senior Director Jon Bovit.

Source: Resilinc

AI

Report: U.S. Companies Led AI-Tech Acquisitions 2014-18

.Leading data and analytics company, GlobalData, released a report this week highlighting companies that dominated the artificial intelligence-tech space from 2014-2018. In the report, four out of five top acquirers were U.S. based: Facebook, Microsoft, Apple and Splunk. These companies represent a combined total of 30 acquisitions during the time period studied. Accenture made the list as the only non-U.S. based company, representing six acquisitions total.

“Technology companies have been the dominant deal makers in the AI space. However, with artificial intelligence making inroads into diverse sectors, the buyer universe in expanding and the space is also attracting investments from non-technology companies,” said Aurojyoti Bose, Financial Deals Analyst at GlobalData.

Top Deal Makers-Payment Tech_V2

“The high number of American firms attracting investments in the AI space is a testimony to the country’s dominance in AI technology. The recent launch of American AI Initiative program also augurs well for the development of the sector or start-ups operating in this space,” added Bose.

Additional insights in the report confirm the U.S. as a leading region for targeted acquisitions, representing 70 percent of those acquired by the top five in the list. Regions closely following include the UK, China, India, Canada and Israel due to the talent pool and innovative technology offerings.

Top Deal Makers-Payment Tech_V1 Table

“With increasing adoption of AI across sectors, this space is bound to witness growth in an already burgeoning M&A activity. Corporates are extensively evaluating options to integrate AI in their business operations and automation initiatives. Going forward, AI solutions will be an integral part of their strategies,” Bose concludes.

Source: GlobalData

Streamlining Global Trade: How AI-Enabled Business Networks Can Make Your Business Smarter

We hardly need reminding of the global challenges facing companies today, from increased competition from low cost foreign competitors, to tariffs and changing regulations. As if that weren’t challenging enough, there is the need to keep abreast of technology innovations such as digital business networks, artificial intelligence and blockchain, that are giving startups the opportunity to leapfrog more traditional and mature companies. Within this landscape, companies need to transact with more and more companies, using different systems and often in different time and regulatory zones, which increases the complexity of doing business exponentially.
So, how do you address these issues while modernizing and continuing your business?

Digital Transformation or Consumer-Driven Transformation?

Fundamentally, conducting business is about supplying to the demand of the end consumer. Businesses that win are the ones that create demand with innovative products; or, better serve existing demand. Regardless of the approach, successful companies also understand that they need to be sensitive and responsive to customers’ needs and the market.

Technology serves a vital role as the means by which businesses register demand, plan, forecast, make, move and sell products and services. Technology can make or break a company’s ability to react to changing consumer demand, market shifts, and supply constraints. It can also limit a company’s ability to fully exploit the innovative technologies that are constantly emerging.

For example, most companies are split into functional silos. This is partly corporate culture and partly technological, thanks to the typically inward-focused nature of enterprise systems. Whatever the causes, silos inhibit visibility, speed and agility. Steven Bowen, in his book Total Value Optimization, calls silos “one of the most pervasive and profound barriers to real competitive advantage in every company.” This problem is multiplied across the global business footprint and across the worldwide supply chain, with regions, countries and trading partners having their own systems and silos.

For optimal functioning, all departments need to align around the corporate strategy, and all trading partners around the same objectives, with the primary concern of serving the end customer as effectively as possible. After-all, it takes just one weak link to drag down the performance of the whole supply chain.

Networks Break Out of the Box

We can learn a lot from disruptive leaders in today’s world, as companies like Uber and AirBnB have each disrupted their respective industries because of their ability to sense and respond to consumer demand and match it to supply in real time.

For instance, Uber leverages a multiparty network where all drivers and riders connect to a single platform and drivers are routed to riders, automatically. What’s more, you use the same platform to summon your ride (or your stay in the case of AirBnB), make the contract and transact payment, and rate the other party. Both are “end-to-end” platforms that handle all aspects of the search, booking, payment and review processes. They make the whole process seamless and provide value for both buyer and seller.

The multiparty networks at the heart of these types of businesses, are creating huge efficiencies by eliminating delays and costs and connecting all parties in real-time with a single, authoritative version of the truth. No silos, no delays, no confusion; just a frictionless network for transacting with anyone, anywhere, anytime.

Intelligent Business Networks

The opportunities for business-to-business companies are even greater, because the buyer-seller-mover relationships are complex and multi-layered. There are many more silos, blindspots and delays in a global supply chain compared to a simple one-to-one relationship of rider to driver as in Uber, or guest to homeowner as in AirBnB.Thus, a multiparty network that connects all seamlessly in real-time, has a much bigger impact. Instead of a business partner waiting days or weeks for information to flow upstream, they get it instantly and can react immediately. If a customer’s promotion is selling more than expected, a supplier with visibility to sales can proactively plan for increased orders and ramp up its own supplies and production.

And it gets better. Advanced multiparty networks not only share data, provide real-time visibility, and enable business partners to collaborate as events happen, they also leverage technologies like artificial intelligence and machine learning to optimize and automate processes. “Intelligent agents” monitor conditions across the supply chain, things like sales data, inventory levels, orders, shipments and how they relate to critical milestones. They can predict sales and identify trends and anomalies.

Because all business partners are on the same network, intelligent agents are able to not only flag potential issues, but actually intervene to solve them. They are able to continuously reconcile sales data with projected inventory levels, shipments from ocean and domestic carriers, along with actual lead times, to predict supply and demand issues in advance and then execute proactive solutions to avoid them.

With the vast amounts of data flowing across the network, these systems are ideally suited to machine and deep learning, enabling them to identify patterns and correlations. This data can then be used to predict sales and cascade the order forecast back through the supply chain through distribution centers, manufacturers and suppliers. They continuously monitor the sales and inventory in near real time, and can foresee issues like pending stock out swell in advance. Intelligent agents can then resolve them by autonomously reallocating supply from DCs to stores, adjusting forecasts, creating new orders and helping manage logistics processes.

Better yet, the system monitors the outcomes of these autonomous actions and recommendations, and continuously “learns” and adapts to recommend and execute the most effective resolutions to similar problems in the future.

Data-Driven Agile Ecosystems

This is merely a glimpse into the nature of these emerging intelligent business networks, as they are evolving rapidly. Unlike traditional systems, they are able to harvest new and unstructured types of data, such as weather, traffic, social media chatter and more.
The volume of data in today’s supply chains is set to explode with the increasing use of sensors and live streaming data from containers, vehicles, handheld devices and industrial machinery.

While the sheer volume of data is overwhelming for human managers and for traditional systems, it is ideal for multiparty networks with machine learning algorithms and intelligent agents. They can learn from it and extract new insights that can drive better operations, higher service levels to customers and lower costs for all automatically, and often without the need for human intervention.
Multiparty networks are smarter, enable easy onboarding of trading partners (with a single connection) and provide pre-built solutions with PaaS tools that enable rapid tailoring and extension of functionality to suit changing business needs. Further, the multiparty network model makes it easy to consume both legacy data and leading-edge data from technologies like IoT, product authentication, 3D printing, and blockchain.

It is often said that intelligence is really the ability to adapt to change. Multiparty networks are built to be adaptable through and through. For instance, connections between trading partners are virtual and thus easily reconfigured should trading relationships change. The permissions controlling each trading partner’s rights over visibility and execution on each specific data object is configurable and can easily be turned on and off through a simple user interface. Old and new technologies can co-exist, with multiparty workflows coordinated across different systems and parties. Software developer kits make it easy to adapt existing network solutions or build entirely new ones on the platform.
The result is an extremely adaptable network platform, supporting an agile ecosystem of all trading partners centered on serving the customer at the highest service level at the lowest cost.

About the Author
Nigel Duckworth is a marketing strategist at One Network Enterprises, provider of a blockchain and AI-enabled network platform that enables all trading partners to transact in real time. To learn more, visit https://go.onenetwork.com/article-one or follow them at https://twitter.com/onenetwork

 

Artificial intelligence

THE RISKS, CHALLENGES & OPPORTUNITIES OF PROCUREMENT POWERED BY ARTIFICIAL INTELLIGENCE

Artificial intelligence, better known as AI, is popping up everywhere as the panacea for everything. There appears to be no limit to where it can be used to make businesses work smarter to improve profitability. The International Data Corp. (IDC) Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide forecasts that cognitive and AI spending will grow to $52.2 billion in 2021.

In addition to autonomous vehicles, predictive maintenance and chatbots responding to customer inquiries, AI can have an immediate positive impact on the bottom line by helping companies select suppliers that provide goods and services at the lowest price, with the least amount of risk.

Here are some opportunities and challenges of using AI to increase procurement effectiveness.

Spend Analytics

Spend analytics can be armed with AI software to collect, cleanse, classify and analyze expenditure data to help procurement teams identify excessive costs. For example, AI systems can identify when duplicate suppliers were used to purchase the same goods, urgent purchases were made without using better terms in existing contracts, and when there were suboptimal payment terms.

But to find savings opportunities, AI software has to be good at classifying data. Statistical and pattern-based AI techniques can have weaknesses dealing with one-off purchases and infrequently used suppliers. They can also be stumped by new languages and geographies, which happens more and more often as supply chains become global. The best way to achieve ROI is to pilot a system where there is a large volume of transactions involving standard repeated purchases, so that there are more opportunities for increased efficiencies.

Strategic Supplier Sourcing

By using AI, procurement officers can be armed with knowledge about market conditions, upcoming mergers and acquisitions and real-time product and support comparisons. This ensures that there is a data-driven strategy for awarding suppliers, and that procurement is getting the best possible terms.

Using AI also reduces the time required to analyze all of the supporting data. Evaluating responses to a bid process can be reduced by as much as 80 percent. It can also be used, on a continuous basis, to provide recommendations of suppliers on demand. Responding to market opportunities in seconds versus weeks can speed up time-to-market by receiving the needed parts and materials quicker.

Guided buying is also an AI innovation that enables employees to quickly and easily buy goods and services from preferred suppliers with minimal support from procurement teams. Employees can use voice-activated commands to find the best price or a supplier that can deliver on time where there is an urgent request. Many of these systems enable direct communication with suppliers with embedded rules to ensure that the buying process is compliant with procurement policies.

Many automatic personal assistants also have the advantage of being able to learn from experience. But if the AI system is self-taught, there is the risk that it can be corrupted by outside influences, so communications and procedures need to be protected from hackers or rogue employees. For example, the famous chatbot, Microsoft’s Tay, was taught by trolls to use inappropriate language until it was taken off the market for further testing.

Contract Analytics

The majority of organizations do not have a database containing all of the data in their contracts–and they definitely do not have an easy way to extract all that information–so there’s no quick and efficient way to, for example, view and compare agreements. Using AI, companies can review contracts more rapidly, organize and find large amounts of contract data to significantly lower the possibility of contract disputes and increase the number of contracts that they can negotiate and execute.

For example, using AI, company contracts can be accessed based on renewal dates to inspect conditions and negotiate accordingly. Finance and procurement teams can inspect if pricing discounts are not being consistently applied across the organization in line with contract terms or keep track of the wording of specific clauses in different divisions. The beauty of AI contracting software is that it helps organizations maintain consistency in the terms and usage in all of their contracts, which makes it easier to identify instances of non-compliance, and make sure that less-than-ideal provisions are dealt with quickly.

The Challenge: Data and Application Integration

None of the benefits of AI can be realized without a strong data foundation. Firms need to invest in data management—as well as data and analytics—to have a 360-degree view of their business operations. Only if their CRM, ERP and financial systems are fully integrated can they have access to all the data that is required.

Point-to-point integrations can initially appear to be more cost effective when there are only a few systems connected together. But, in time, with more and more data shared with different departments, suppliers and partners, a third party integration platform can result in lower development and maintenance costs while providing the scalability and consistent data handling that’s needed.

Once companies have a strong data foundation with all of the necessary integrations and data sharing, new machine learning-based platforms can be used to enforce the best procurement practices. Although today AI procurement systems are not always accurate, machine learning uses algorithms to learn from data, allowing platforms to continuously improve themselves.

As we start to see spend analysis platforms classifying data at levels of 98 percent accuracy—the same level as human analysts—it is more and more likely that AI will become a trusted tool for the procurement process.

Tsipora Cohen is the global head of Marketing at Magic Software Enterprises, a global enterprise software company headquartered in Or Yehuda, Israel. Visit www.magicsoftware.com.