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GlobalData Discusses Quantam Computing and its Impact on Auto Manufacturing

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.

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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.”