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Why COVID-19 is a Galvanizing Moment for Eliminating Physical and Digital Supply Chain Risk

risk

Why COVID-19 is a Galvanizing Moment for Eliminating Physical and Digital Supply Chain Risk

When the COVID-19 pandemic began, the resulting economic fallout was felt across borders and industry alike. From manufacturing to financial services, every industry has been scrambling to minimize the impact of the pandemic on the bottom line. For many businesses, this has helped serve as an urgent wake-up call to take proactive steps to identify and eliminate risk across their global supply chains, which typically span several tiers of suppliers dispersed across the world. Real-time supply chain risk visibility plays a critical role in avoiding business disruptions.

The Economic Risk

There is an immense economic risk that needs to be considered when a business operates a global supply chain. At the start of the pandemic, we witnessed the inevitable ripple effects across not just multiple industries but also across multiple different tiers of suppliers. For example, 3.74% of sub-tier suppliers in the Department of Defense’s ecosystem closed as a result of the pandemic. 75% of small businesses have reported that they have only enough cash in hand for 2 months or less. As suppliers struggle or go out of business, significant supply chain disruptions are common.

This instability coupled with the multitude of other economic crises facing the world, such as ongoing trade friction with China, could precipitate a fundamental collapse of global business as we know it. We must monitor our supply chains for more points of exposure to risks than ever before.

The Data Security Risk

With computer hacking having increased 330% since the start of the pandemic, global businesses also need to account for the cybersecurity risks involved with having a supply chain across multiple countries and potentially hundreds or thousands of suppliers. The data systems of global suppliers are a potential entry point to a brand’s or government agency’s data systems, presenting a major challenge across the global supply chain. Organizations must be able to assess and continuously monitor the strength of supplier data security measures and the changing cybersecurity-related risk associated with their suppliers.

Even after the pandemic subsides, the need for real-time risk monitoring in the extended digital supply chain will persist, especially as cybersecurity attacks grow in sophistication.

New Technology for Physical and Digital Supply Chain Risk Management

When it comes to monitoring risk associated with multiple tiers of suppliers, the majority of businesses are still way behind. According to Gartner, only 27% of companies perform ongoing third-party monitoring and only 2% directly monitor their 4th and 5th party suppliers. Although companies know they’re vulnerable to disruption by a sub-tier supplier, not enough are being directed or given the tools to actively monitor them effectively.

Historically, the majority of businesses attempt to identify, assess and manage supply chain risk manually and only periodically. This is because, previously, automation technology focused on making sense of large amounts of extended supply chain ecosystem data has not been up to the task. Much has changed. The global machine learning market was valued at just $1.58B in 2017 and is now expected to reach $20.83B in 2024, growing at a CAGR of 44.06%. New AI and machine learning-based technology is emerging rapidly and changing the game. This new technology can immediately illuminate risks across all tiers of a global supply chain because data on tens of millions of suppliers is continuously monitored from both a physical and digital supply chain perspective and across numerous risk factors.

Incorporating AI-powered solutions into your supply chain risk management strategy can automate the identification of risks that exist deep within a supply chain. In addition, adopting this technology ensures that an organization has continuous, real-time information to inform ongoing risk management efforts and identify problems before they threaten the business.

There is no way to know when the pandemic and its resulting implications will cease. Or when and where the next global event will happen. Looking ahead, successful businesses will be ready to continue functioning in a safe and secure way regardless of what issues they face. Supply chain-related blind spots and resulting disruptions can pose major complications for organizations that aren’t able to effectively identify and map risk. COVID-19 has driven a greater sense of urgency to shore up these problems. New technology for automated, continuous monitoring of supply chains end-to-end presents a new path toward operational resilience, business continuity, and overall health.

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Jennifer Bisceglie is the CEO of Interos, the first and only business relationship intelligence platform to protect enterprise ecosystems from financial, operations, governance, geographic, and cyber risk in every tier of enterprise supply chains, continuously.

risk

New Manufacturing Priorities: Increasing Agility and Understanding Systemic Risk

COVID-19 dealt an unprecedented blow to the global supply chain. Almost overnight, demand skyrocketed in some sectors and plummeted in others, forcing manufacturers and their suppliers to adapt on the fly.

As many as 94% of the companies in the Fortune 1000 have experienced supply chain disruptions since the start of the pandemic, including global manufacturing titans like Apple. The fact that the world’s largest companies haven’t been immune suggests that abundant resources and relationships aren’t enough to help supply chain partners survive in times like these.

It also suggests that the traditional playbook for dealing with supply chain problems is no longer quite as effective as it used to be. The old methods of responding to crisis demand and supply are no longer enough to withstand these events — this pandemic, as one example, has led to shortages of everything from toilet paper to car parts.

Shifting Focus From Efficiency to Manufacturing Agility

If manufacturers want to withstand the next crisis, they’ll need a new approach. COVID-19 has highlighted the need to move from focusing on efficiency to adapting for flexibility, resilience, and agility.

Arguably, the focus on efficiency — on doing the most with the least — even exacerbated recent supply chain disruptions by leaving producers and suppliers under-resourced and locked into one way of doing things.

Even as the pandemic rages on, the takeaway is clear: Manufacturing agility is more important than operational efficiency. Efficiency strives to make incremental improvements, but reality calls for manufacturers to make sudden, sweeping changes in the face of unprecedented challenges.

Understanding Systemic Risk

The challenges the pandemic brings to the supply chain represent nonsystemic risks — or risks that are out of manufacturers’ control. Problems that manufacturers can control with the right tools and processes, on the other hand, are systemic risks — specifically, problems that start on the ground floor when machines break, then disrupt production lines, then cause issues all the way downstream in the supply chain. It’s like one domino toppling down the rest.

Nonsystemic risk puts additional pressure on manufacturers to ensure that their facilities are as systemically risk-free as possible to remain flexible around dramatic swings, either up or down, in demand.

Now that COVID-19 has proven anything is possible when it comes to nonsystemic risk, manufacturers must turn their focus toward creating agile operations to contain supply chain issues in whatever form they arrive. For example, when a nonsystemic risk like the pandemic forces a manufacturer to shut down 80% of its production lines in a low-demand environment, the lines that are still operating will need to function at maximum productivity.

If the remaining lines aren’t up to par, the company will become vulnerable to slowdowns that could turn away what few buyers still exist, or the company may, once again, have to go through the time-consuming and costly process of shifting production to different facilities.

Nonsystemic risk highlights the need for organizations to uncover and manage systemic risks, taking control over whatever factors they can to keep bad situations from getting much worse.

Finding Opportunities in Manufacturing Agility

Managing systemic risks starts by understanding on-the-ground conditions, particularly when it comes to machines. When the unexpected happens, machinery bears the brunt of the consequences. As it ramps up or down in response to sudden changes in production, it must continue to operate efficiently, productively, and without downtime.

When manufacturers can gather insights on machines at the ground level, regional leaders can view those insights collectively to uncover companywide opportunities to manage systemic risk at large.

By de-risking operations in this way, these companies stand to gain substantial predictability and flexibility around their machines, which opens up opportunities for greater productivity and more effective, efficient asset planning at the corporate level. Focusing on the following three areas can help you uncover systemic risk and opportunities for more agile production lines:

1. Create redundancy in your production lines.

As a first step, you should have multiple lines running at the same time. This way, if one does fail, you’ll have a backup plan. That strategy itself should be viewed as the first step in a much larger strategy — as it really just serves as a crutch approach to mitigating systemic risk. Running many lines that are susceptible to machine failure and downtime can actually become a significant financial risk in manufacturing.

The eventual goal should be to have every line be your best line — which means no machine downtime. To create that kind of system, you need to understand the details about why your best line operates at maximum productivity and why your other lines pale in comparison. The real value of creating redundancy in your production lines will come from the insights you gather on your machines to optimize every machine companywide, making each production line your strongest one.

2. Leverage machine health data.

So how do you gain insights on individual machines in your production lines and view that machine health data collectively to reduce risk along every production line? It’s easier than you might think. In fact, sensors and IIoT networks combined with AI algorithms can do most of the work for you, showing you where risk and opportunity exist in manufacturing operations across entire asset classes.

When these tools give you insights into the health of all the components of your supply chain, you can both replicate what’s working best and predict what might fail in the future, removing the weakest parts and replacing them with stronger ones. Machine health data can help you find your best configurations so that you can replicate these practices across all your facilities.

3. Facilitate remote collaboration.

The factory floor as we’ve long known it is changing. That was true even before the pandemic, but COVID-19 sped up the transformation. Social distancing practices have meant that up to half of manufacturing employees have been unable to work on-site. Digital collaboration and remote work are the new normal everywhere, and the manufacturing industry is no different.

The good news is that by utilizing digital collaboration platforms, you can bring a lot more opportunity for flexibility and agility into your company. When your teams can collaborate from anywhere, you can draw on the institutional knowledge of your entire organization. Cloud-based software that identifies machine health problems and allows for remote collaboration to address them lets your teams work better together, even from farther away.

The pandemic forced everyone to adapt fast, but it should also force everyone to question their assumptions about what manufacturing looks like in 2020 and beyond. Surviving isn’t about becoming as lean as possible — it’s about being agile enough to stay in front of the waves of change, even the ones you’ll never see coming.

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Artem Kroupenev is VP Strategy at Augury, where he oversees Augury’s AI-based machine health, performance, and digital transformation solutions. He has over a decade of experience in building products and ecosystems and bringing technological innovation to market in the U.S., Israel, and Africa.

artificial intelligence

Artificial Intelligence Market to Reach $54 Billion by 2026

According to a new study published by Polaris Market Research, the global artificial intelligence market is anticipated to reach USD 54 billion by 2026. The advancements of robots and the rise in their deployment rate particularly, in the developing economies globally have had a positive impact on the global artificial intelligence market.

Augmented customer experience, expanded application areas, enhanced productivity, and big data integration have highly propelled the artificial intelligence market worldwide. Although, the absence of adequate skilled workforce, as well as threat to human dignity, are some of the factors that could affect the growth of the market. However, these factors are expected to have minimal impact on the market attributed to the introduction of advanced technologies.

An extraordinary increase in productivity has been achieved with machine-learning. For instance, Google, with the help of its experimental driverless technology has transformed cars including, Toyota Prius. The integration of various tools by artificial intelligence has helped in the transformation of business management. These tools include brand purchase advertising, workflow management tools, trend predictions among others. For example, Google’s voice accuracy technology has a 98% accuracy rate. Furthermore, Facebook’s DeepFace technology has a success rate of approximately 97% in recognizing faces. Such accuracy in technologies is further anticipated to bolster the market growth during the forecast period.

Currently, North America dominates the global artificial intelligence market attributed to the high government funding availability, existence of prominent providers in the region, and robust technical adoption base. Also, the region is expected to continue its dominance during the forecast period. Moreover, the adoption of cloud-based services in key economies, such as the US and Canada, is considering adding to the market growth in the North American region. The markets in Asia Pacific, MEA and South America region are expected to notice a high growth during the coming years. The growth in the Asia Pacific region is attributed to the increasing demand for artificial technologies by the developing economies. Thus, the region is anticipated to grow at the highest CAGR during the forecast period.

 

Major companies profiled in the report include Google Inc., Intel Corporation, Nvidia Corporation, Microsoft Corporation, IBM Corporation, General Vision, Inc., Qlik Technologies Inc., MicroStrategy, Inc., Brighterion, Inc., and Baidu, Inc. among others.

Key Findings from the study suggest North America is expected to command the market over the forecast years. APAC is presumed to be the fastest-growing market, developing at a CAGR of more than 65% over the forecast period. The artificial intelligence market is presumed to develop at a CAGR of over 55.9% from 2018 to 2026. The high implementation of artificial intelligence in several end-user verticals including, retail, automotive and healthcare is projected to boost the growth of the market over the forecast period. Several companies are making considerable investments to integrate artificial intelligence competencies into their portfolio of products. For instance, in 2016, SK Telecom and Intel Corporation signed an agreement for the development of the artificial intelligence-based vehicle-to-everything (V2X) technology as well as video recognition.

For More Information About Artificial Intelligence Market @ https://www.polarismarketresearch.com/industry-analysis/artificial-intelligence-market/request-for-customization
quantam computing

GlobalData Discusses Quantam Computing and its Impact on Auto Manufacturing

As artificial intelligence continues making news headlines in a variety of industries, GlobalData experts released statements from Volkswagen’s Data Lab team lead, Dr. Marc Hilbert about the risks and opportunities presented. In his statements, Dr. Hilbert addresses specifics relating to quantam computing in the automobile manufacturing sector.

“Security is definitely necessary. I think it’s very important specifically for Volkswagen because I think if you’re not compliant, if you cannot say that our things are safe, you will lose the trust of the consumer. So compliance is something that we are working on also with machine learning, and anonymization, so hiding your personal data within the car. So there’s nobody who can say that this is you, but we still have enough information to understand.”

Quantam computing is on the radar for many industry players as a potential emerging trend. Technology innovations and game-changers alike pose unique sets of challenges and potential solutions, and of course, associated risks.

“Traffic optimization is one of the use cases we’re looking at in terms of quantum computing. Because we think that quantum computing will be one of the emerging technologies which will have a big step in terms of machine learning, in terms of data analysis, and so on. And there are companies like D wave, IBM and Google, which tried to build the computer. So this is one aspect to actually get closer to a solution,” he adds.

“The Volkswagen group is coming from a different point of view. What we try to do is find problems in the real world. What we have today with our customers is traffic jams. We tried to translate this kind of questions in a way that a quantum computer can understand it. And we try to bring those two things together to identify aspects where we can use quantum computing in the next step. So this is our task in the data lab,” Hilbert concluded.

To read the full article, please click here.

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.

 

Maven Wave Earns Google Cloud North America Services Partner of the Year

Google Cloud Premier Partner, Maven Wave, now boasts its second consecutive title as Google’s North American Partner of the Year following recognition during this year’s Partner Summit at Google Cloud Next ‘19. The consulting and technology firm’s outstanding ability to deliver digital solutions to customers served as the focal point of the recognition. Maven Wave is known for developing these solutions through the utilization of Google Cloud innovations.

“It is an incredible honor to receive this award for the second year in a row. This achievement recognizes the extraordinary efforts from our teams who, together with our visionary customers and valued Google Cloud partners, have been able to realize remarkable success in enterprise digital transformation,” said Jason Lee, Partner and Founder at Maven Wave.

Maven Wave has served as a Google Cloud Premier Partner for nine years with specializations in areas such as Application Development, Cloud Migration, Data Analytics, Enterprise Collaboration, Infrastructure, Location-Based Services, Machine Learning, and Marketing Analytics.

“Google Cloud provides industry-leading, cloud-native products that allow us to accelerate the development of innovative enterprise solutions, from modernizing infrastructure to creating intelligence from data and enabling work transformation. We remain absolutely committed to our Google Cloud partnership and look forward to continued success for our customers in 2019 and beyond.”