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5 Remarkable Benefits of Machine Learning in Business

machine learning

5 Remarkable Benefits of Machine Learning in Business

In recent years, Machine Learning and Artificial Intelligence have taken the world by storm in every business sector. Marketers and business leaders are in a race of taking advantage of the applications by implementing them in their business.

Many people in the industry have heard about Machine Learning (ML) and Artificial Intelligence, but they have no idea about their usages and benefits. They have no clue how these two technologies can add value to their business and solve their tedious problems.

Machine Learning is a data analysis process that extracts meaningful data from raw data and provides an accurate result with ML algorithms. This information can help in solving complex and data-rich problems. In this way, you can find various data insights without being programmed to do so.

With rapid speed, Machine Learning is evolving. Moreover, it helps organizations in improving their scalability and business performance globally.

In recent times, many giants like Google, Amazon, and Microsoft have implemented machine learning in their business. And they have come up with new machine learning platforms.

Moreover, on a daily basis, too, we are using machine learning, but we are unaware of it. An example of this is the spam detection by Gmail, image and face recognition, and tagging by Facebook and Google Photos. Yes, these are examples of ML. Your business can obtain numerous benefits from machine learning. Hence, in this article, I am going to explain why you should implement Machine Learning in your business and lead the market.

Benefits of Machine Learning in Business

If you implement ML in the right manner, it can serve as a solution to a variety of business complexities and problems while predicting customer behaviors. Let’s see how ML can be benefited to your business.

Real-time Business Decision

Business analysts fetch the data from the internet and pass it to business organizations. This way companies have big data. But it’s not easy to extract the correct information and make a decision from data.

Implementing Machine Learning in your business can help you gain better results. As we know, ML leverages ML algorithms. It analyzes the existing data and understands human behavior. The results help businesses make the correct decision. It allows organizations to transform data into knowledge and actionable insights. This information can be integrated into everyday business processes. Then, it automatically analyses the current business situation, market demands, and deals with the changes. In this way, machine learning can help out many businesses with real-time business decisions. It keeps them ahead of the competitors.

Easy Spam Detection

Spam is promotional messages that are sent via the internet. These emails could be junk mail or simply annoying to the customers. In some cases, it even slows down the performance of the computers. This problem was solved by ML a few years ago by introducing rule-based techniques to filter out pam. This was introduced by email providers.

However, with the help of ML, the spam filters are creating new rules for eliminating spam emails. It helps the network to deal with the spam issue. Phishing messages and junk mail are detected by this system.

Predict Customer Behavior

Many organizations use machine learning to predict the behavior of their customers. It covers the predictive information in prescriptive information to gain more customer base or offer them more customized services. Retail companies can offer the best-personalized product or service to their customers by going through their behavior, purchase pattern, and shopping history. This way, they can improve their demand forecasts.

Enhances Security

Cybersecurity and network intrusions are the major factors of organizations that are affecting their growth. Every organization tries to build a wall of network security and take essential steps for that. They must identify unwarranted networking behavior before intrusion takes place into the full force attack and leak data or affect services.

Moreover, machine learning helps you analyze the network behavior and executes steps to prevent it automatically. ML algorithm adapts to change and replaces manual research and analysis. In this way, you can improve your cybersecurity and unveil security insights.

These benefits of ML can apply to many cases that happen in the business. The manual operation is replaced by the main application of this technology. All the companies are implementing machine learning for their better growth and results.

Product Recommendation

Product recommendation plays an important role in better marketing strategy and valid sales. ML analyzes human behavior, their purchase history, and based on their research, they identify products in which customers are more interested.

With the ML algorithm, it also identifies hidden patterns among the items and finds similar products in groups and clusters. This is called an unsupervised learning process.

This way, you can recommend the products to your customers and enhance the sales of the company.

Maintenance Predictions

This benefit is very important for manufacturing firms where they follow corrective maintenance practices, which are often expensive and inefficient. However, with the implementation of ML, companies can make use of ML to figure out meaningful insights and patterns hidden in their factory data. This is known as predictive maintenance. This helps you identify the risk so you can reduce the chances of failure and increase productivity. This way, you can save money which has to be spent on expenses.

Wrapping Up

So we can surely conclude that machine learning is the best and essential technology that can boost business growth and reduce errors. ML plays an important role to deal with data-related tasks. It’s also helping business owners to run their businesses successfully.

If you want your business to take to the next level, ML plays a significant role. However, we are somewhere using the benefits of ML, but we are unaware of it. Every business can increase its sales and profits by implementing ML. It doesn’t matter whether you own a small business or large, ML is suitable for every kind of business.

So, you can integrate ML into your business solutions with web and mobile app development services. It takes your business to the up and increases better revenue. If you are into the eLearning business, then you can look out for various trends with the help of Machine Learning.

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Kiran Beladiya is the co-founder of the website design company the name is The One Technologies. He is managing everything from discussing project ideas with clients to its release. Due to the hectic schedule, he could not get enough time to nurture and sharpen his writing skills but he is an avid writer. You can connect with him on Twitter and LinkedIn.

costs

The Pandemic’s Impact on the Shift to Reduce Operational Costs and Improve IT Ops Productivity

The COVID-19 pandemic caused an upheaval in IT operations worldwide and forced businesses to reevaluate ways to cut down expenses, the most significant being operational costs. Leaders were challenged with having to operate with skeletal staff and remote teams, while also needing to keep enterprises running 24×7 with virtually no downtime. Let’s look at how the pandemic impacted IT operations and what needs to be done to ensure that enterprises can continue to operate cost-effectively.

Leaner Team Structures to Scale Down Non-Discretionary Costs

The first step organizations took in 2020 was to downsize the contractual resources and variable talent pool deployed in network operations centers to cut back on non-discretionary costs. Leaner teams were expected to perform a similar quantum of work, which in turn emphasized the need to move to greater automation in IT operations management (ITOM).

Going forward, the challenge will be to attract the right talent, do more with less, and introduce automation in business processes to make do with smaller teams. To help reduce the operations workforce, there is growing interest in intelligent tools for notification and escalation, artificial intelligence and machine learning (AI/ML) solutions that minimize alert fatigue and automation of ticketing workflows via IT service management (ITSM) integrations. Further, some amount of in-house work can be shifted to consultants and contractors on an as-needed basis, so the resource is no longer on the payroll after the project completes.

Remote Management and the Rise of DaaS
With COVID-19 forcing businesses to give up leased and rented office spaces to reduce capital expenditure, skeletal staff were deployed onsite with others moving to a work-from-home mode of operation abruptly. This heightened the need for greater security, availability of the right talent with the required software and hardware resources and the need for collaboration among geographically dispersed teams.

Desktop-as-a-service (DaaS) was one of the largest areas of the cloud to experience an increase in demand because of this shift. DaaS is an inexpensive option for organizations looking to support their workers by providing secure access to enterprise applications remotely. Tool integrations for notifications like Slack as well as remote collaboration tools and meeting solutions like Teams, Trello and Zoom also rose to prominence.

The Rise of DevOps and Agile Practices for Deployment Automation

Organizations needed mechanisms for remote and automated deployments due to staff shortages and the absence of a centrally located workforce. This necessitated agile practices for breaking down organizational silos between software developers and IT operations personnel. In 2021, we expect to see increased adoption and continued use of DevOps and agile practices as well as automation in the application deployment and maintenance process.

Data center automation replaces labor costs with software and configuration costs. Dedicated automation architects can ensure that DevOps and agile practices are implemented across the enterprise, thereby reducing the need for manual configuration, monitoring and maintenance tasks.

Revamping Application Infrastructure and Moving to IaaS for Intelligent Scaling

Organizations chose to review their expenditure on dedicated hardware and software solutions to see if a switch to cloud and open source was possible. Virtualization i.e., moving to cloud (microservice and container-based architectures) emerged as a solution to the conundrum, since it reduces the number of physical servers required in the enterprise and the cost of maintaining applications can be significantly brought down.

Cost savings in cloud services have a real, immediate and perceptible cash impact, as moving to the cloud reduces capital expenditures for servers and related network equipment, transforming one-time capital costs to monthly operating expenses. The deployment of virtual management systems enables faster adoption of cloud platforms. Co-sourcing environment management functions provides the added advantage of having the right talent managing the environment with technical know-how and service guarantees in place.

Cloud providers can also provision additional resources like disk space, CPU, memory and communication lines faster and cheaper than on-premise servers and infrastructure. Intelligent workload trend-based capacity forecasting can help deliver resources accurately and avoid unnecessary expenditure.

Software licenses for new and existing tools can be re-examined to ensure that the cost of onboarding and integration with the existing toolset does not include hidden expenses or jeopardize existing investments in the ITOM infrastructure in any way. Eliminating unnecessary tools will also reduce the annual maintenance bills and staff time required to keep systems up and running.

Preventive Healing and Automation for Maximum Uptime

As businesses moved to more digital transactions and saw a marked increase in online traffic due to storefronts being shut, the primary challenge was to provide close to 24x7x365 uptime with reduced IT operations personnel, something made possible by automation. Enterprises adopted artificial intelligence for IT operations (AIOps) solutions providing proactive incident detection and autonomous resolution capabilities coupled with ITSM integrations, so the entire ticketing process was completely automated without the need for human intervention.

Traditional AIOps solutions suffer from certain shortcomings, including the inability to predict issues before they occur and initiate preemptive measures to avert outages. However, preventive healing solutions use patented techniques providing predictive detection of issues and allowing for remedial steps to be put in place so the issue can be averted. Some modes of preventive healing include dynamically optimizing or shaping the workload so the underlying system behavior remains unaffected, provisioning additional resources in cloud environments so the system can handle workload surges or projecting resource requirements based on a what-if analysis of future workload trends so businesses can perform app-aware scaling. Automation of ticketing workflows can be achieved by integrating notification and ITSM platforms.

Despite predictive alerting, some issues may still occur due to sudden network or storage outages, hardware glitches or third-party dependencies being unavailable. In such cases, accelerated root cause analysis with event correlations and suggestions on where the error originated can significantly reduce mean time to repair (MTTR). In the hands of a skilled IT operations analyst, time-synchronized contextual data comprising logs, diagnostic data, business error codes and code-level traces prove invaluable in establishing the chain of causation and closing the incident with minimal time and effort spent, thus leading to a more cost- and resource-efficient data center.

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ABOUT THE AUTHOR: Girish Muckai is the chief sales and marketing officer at Heal Software Inc., the innovator of the game-changing preventive healing software for enterprises known as HEAL, which fixes problems before they happen. To learn more, visit http://www.healsoftware.ai/.

automation

Is Automation Improving the Employee Experience?

Figures alone are incapable of telling the full story of automation. It’s made up of nuances and exceptions, like what type of jobs will need replacement, and how soon. Industry demands, employee skill level, job training, and resource redeployment can vary the outcome. Is it possible for automation to go too far—to render employees obsolete?

Automation has no universal effect but it does have a measurable one. This is evidenced by MIT Researcher Daron Acemoglu and Pascual Restrepo of Boston University. Their study observed how robotic automation impacts job reduction. The researchers found that the introduction of one robot per 1,000 workers reduced the employment ratio by 20 percent.

Though 20 percent is significant as far as job-reduction is concerned, it’s not the robotic takeover that was heralded with apprehension and even fear in many headlines. Some industries are tasked with a great degree of urgency to automate, as with car manufacturers, while others are slow, to adapt to changes without some precursor event.

Since the global pandemic, hotels are experimenting with robotic butlers to service rooms instead of in-person staff. Human fee collectors on toll-roads and bridges may soon become a thing of the past. Places with high concentrations of manufacturing have embraced the future of robotic automation and technological advance.

The Risk is Already Here

Whether an industry is willing to automate or not, automation in some form is, for the most part, inevitable. Economies rise and fall on reinvention. Sooner or later, whether by dramatic market disruption or slow, plodding technological advances, jobs are shaped by automation.

Labor costs are a bulky segment of any businesses’ expenses, and streamlining efficiencies generally results in cutting manual tasks down with software. This is smart for a couple of reasons. It introduces cost reduction to the product and ultimately consumer when there is less labor involved. It saves employees time to devote to higher strategy tasks. It eliminates frustration, wasted effort, and job satisfaction. And it cuts needles complexity out of business processes.

Professor Daron Acemoglu, explains the general outlook for the future of jobs as pertains to automation:

“It certainly won’t give any support to those who think robots are going to take all of our jobs. But it does imply that automation is a real force to be grappled with.”

The Human Element

The main concern is that automation eliminates the need for human employment. Not all automation has uniform effects and not all employees desire relocation within the same company to new or different roles. Employees are unique. Some who are made redundant will choose to leave and reinforce their skillset with higher education. Others will make lateral moves to teams that prove a higher match for their current desires. And still, others will be equipped with more strategic skill sets through the introduction of technology to keep their jobs, but in a smarter way.

In Accounts Payable, high-touch paper backlogs do not become high-performance back offices overnight. The reduction of friction and drag on everyday tasks like cutting paper checks and securing payment approvals in real-time is a transformation borne of automation.

A Goldman Sachs report from 2018 projected how B2B payments will grow into a $200tn industry by 2028, doing over 5X the volume of transactions as the retail payments market. This poses an incredible opportunity for automation to unburden manual paper-based tasks that are needlessly repetitive or vulnerable to automation.

According to a survey by Hyland Software, Accounts Payable staff spend  30% of their time on routine tasks including data capture, manual invoice intake, resolving unmatched invoices, and finally chasing down payment approval. It is an industry poised for automation as a matter of necessity. Especially in a workforce that is increasingly remote.

Plenty of AP managers understand the annoyance and overwhelm of cutting hundreds of physical checks and distributing them among multiple sites for wet-ink signatures. Yet, they may not know the digital visibility and ease that’s possible.

Automation Reduces Some Jobs but Reinvents Others

Understanding automation-driven changes requires employer investment. Equipping AP staff to harness the power of automated tools means avoiding massive layoffs and job terminations. Organizing around a shared automation goal is essential to help AP teams—and businesses alike—trim down on manual excess, avoid redundancies, and ensure staff is not underemployed.

Automation is here to stay, but the good news is, employees are adapting to more strategic roles because of it. Companies that are early adopters of technology have the highest chance of improving employee job satisfaction while facing the future proactively.

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Lauren Ruef has collaborated with Nvoicepay, a FLEETCOR Company to write about financial technology since 2016. Nvoicepay optimizes each payment made, streamlines payment processes, and generates new sources of revenue, enabling customers to pay 100% of their invoices electronically, while realizing the financial benefits of payment optimization.

manufacturing

3 Ways Data Analysis Improves Manufacturing

Data Analysis is the process of inspecting, transforming, cleansing, and modeling information. The goal is to discover useful details that inform decision making. Businesses use it to statistically and logically find ways to improve their products and services.

With accurate data analysis, manufacturers can streamline their operations and do away with old ways of doing things. But most companies still haven’t understood the impact well-analyzed information can have on their business. This article outlines three ways data analysis improves manufacturing.

Increase in Energy Efficiency

According to Friendly Power, the United States manufacturing sector accounted for 32% of all energy consumption across industries in 2018. The industrial sector consumed approximately 27 000 trillion BTU that year. Fuel manufacturing processes accounted for a considerable part of that figure.

It calls for a need for reduced energy consumption by manufacturers. The best way to achieve this is for companies to become smarter energy managers. While they have been working towards this goal, there is a need for manufacturers to do more.

Data analysis shows companies diverse ways of improving energy efficiency and reducing consumption. They can achieve this by following a systematic or holistic approach.

With small investments and fewer larger investments, manufacturers can reduce their energy consumption. Analyzing information will help companies understand the ways they expend energy.

Real-time data analysis helps plant managers monitor excess usage and untimely consumption. It allows them to prioritize specific energy retrofits. It works for promoting and implementing behavioral changes in employees. Additionally, manufacturers will set informed and achievable energy-saving goals and reduce costs.

Improved Equipment Maintenance and Less Downtime

One thing that slows down and affects manufacturers is equipment breakdown and high downtime. Machines get built for optimal efficiency, but sometimes, several factors affect the way they work. Some of these problems are poor installation, misuse, and lack of downtime coordination.

Companies can prevent this by effectively gathering data. The combination of IoT systems and robust predictive analytics in manufacturing helps manufacturers gain real-time insight. It shows them how well equipment functions on a micro and macro scale.

Data analysis helps manufacturers schedule hours and days for a checkup to keep machines from breaking down. Predictive data allows companies to keep using their machinery until they have to carry out maintenance. It means that they are pre-informed on when they need to check their equipment.

This dramatically helps improve the manufacturing process. The maintenance crew will only work when needed, thereby freeing up personnel for other duties. Data analysis prevents excess troubleshooting and allows the facility to function more efficiently.

On the downtime, analyzing information ahead of time reduces it. Knowing the right steps to take ensures that the machinery functions when it should prevent production lags. A company with non-functioning equipment will lose money and may not meet up with demand.

Boosts Business Operations and Improves Time Management

Aside from the first two benefits of data analysis, it improves business operations and time management. Manufacturers who gather and analyze information can better plan their day-to-day manufacturing process.

It helps companies with their targets and prioritizes activities based on their importance level and urgency. Site managers get a granular vision into the operational insights of their industry. It increases security augmentation, process monitoring, and controls employees’ behavior and working hours.

Data analysis helps a manufacturing company be where it should be at a particular time. It keeps them from falling behind by effectively managing their time and ensuring that every moment counts.

When site managers know when their equipment is most likely to need maintenance, they will not waste time worrying about it. They’ll focus on their work. Accurate data analysis means knowing what problems are likely to arise and making plans ahead to fix them.

Other Ways Data Analysis Improves Manufacturing

The three benefits mentioned above are not exhaustive of the ways data analysis improves manufacturing. Manufacturers who invest in gathering information as topforeignbrides.com do get to understand their business’s supply side.

Every manufacturing business’s essence is to meet demand and make a profit. To do the latter, companies must minimize manufacturing costs. A crucial part of achieving this is following and tracking supplies to ensure that manufacturers do not pay any extra cents.

Data analysis helps manufacturers follow their supplies and every part of the manufacturing process. This way, they can account for every material delivered and make adjustments where needed.

Tracking records helps manufacturers discover unworkable components ahead of time and prevent product failure. It creates better demand forecasts. It means a manufacturing company will predict when the need for their product will go up and effectively meet it.

Demand data is vital for two reasons. First, it guides the production chain, and second, it prevents storing goods for a long time in warehouses. Most companies use information from previous years and sales to make predictions of this nature.

However, it is better to combine past and predictive data in making demand projections and manufacturing plans. By doing this, manufacturer’s reduce their risk and production waste.

Furthermore, data analysis means that business owners will make all manufacturing decisions based on strategic information. Site managers will only make choices that will improve the manufacturing line and their staff’s overall welfare. They will ensure efficient arrangement structures in warehouses and better product flow management.

The Takeaway!

No matter the industry a business belongs to, data analysis is vital, and the manufacturing industry is no different. There are so many aspects of production that the only way to keep track and avoid mistakes is by collecting data.

Knowing when to expect trouble and putting things in place to prevent them is an effective way to improve the manufacturing process. It also helps to know what to produce, when, and who to manufacture for. It ensures that companies pay attention to energy conservation and educate their employees on it.

Finally, companies that capitalize on data analysis have increased efficiency and productivity. They understand their clients and market more, maximize profit, and streamline their supply chains.

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 industries 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.

vector artificial intelligence robotics market refurbished AI

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.

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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.”