Machine learning (ML) applications are reshaping the traditional networking framework with scalable and efficient networking infrastructure, directly impacting almost 70 to 80% of an organization’s IT spend. In short, the network supporting the supply chain in most organizations is evolving quickly in ways that promise a more powerful network for critical tasks.
For example, in the current scenario, replacing inefficient legacy systems with converged infrastructures that use machine learning platforms for applications such as load balancing as a preemptive measure towards network downtime, access control, or intrusion prevention have become essential.
In the traditional client-server architecture where transactions occurred between a single client and a server. In the present scenario, revolutionized applications access multiple servers and databases, creating a flurry of machine-to-machine traffic like never before and altering conventional traffic patterns. Additionally, in an era of digital transformations, with ever changing network usage pattern, ML enables network administrators to deploy intelligent and adaptive network infrastructures. These self-learning networks are able to gather data from various network nodes and generate networking models that are regenerative and self-healing.
Consequently, the rate of adoption of networks powered by machine learning is exuberant, as it finds its application throughout all the major industry verticals. Major adopters being manufacturing; banking, financial services and insurance (BFSI); utilities; retail; telecom; and healthcare. These industries are riddled with legacy systems that are being swept across by a new age of digital transformation.
This new age of technology evolution puts cutting edge machine intelligence not only within core infrastructures but also applies it to planning, designing, and implementation of network infrastructures. Eventually, the emerging networking architecture will entirely revolutionize the existing networking and data center infrastructure, with ML integrated within the control plane that is decoupled from the data plane. The foundation is being laid to create separate controlling application platform that can create policies and provide network intelligence to support organizations, especially as they prepare for big data, surging network traffic, cloud computing, and bring your own device (BYOD) environments.
More than ever before, it is essential to have a pre-emptive maintenance plan for IT infrastructure in place. Possession of legacy systems can be truly a disadvantage here, as it would hinder the capabilities of ML to overcome any critical single point network failures from completely stalling the IT operations within an organization. This could really be costly for a manufacturer, logistics company or financial institution where even a short period of downtime can amount to huge business losses.
Taking a manufacturing example, revenue estimation, demand forecasting, and supply chain management leverage the machine learning technology to make the overall manufacturing process efficient, agile, and economic. Currently, the supply chain in manufacturing industry has a plethora legacy systems, making the whole process of evaluating performance and maintenance of IT and other operational assets extremely time consuming and expensive. Further, ML opens several major applications such as predictive maintenance.
Machine learning algorithms and big data analytics help manufacturing organizations to improve supply chain management. It enables them to continuously assess the state of the supply chain and address many of the problems in supply chain operations. Machine learning allows organizations to reduce the inflated safety-stock levels and optimize inventory by adjusting inventory positions to ensure that the right inventory is positioned at the right location to service customers better and prevent stock-outs. Moreover, machine learning helps in making Sales and Operations Planning (S&OP) process efficient with the help of insights provided by key trends, which are picked up from transactional data. Due to the exponentially rising adoption rate of machine learning powered enterprise software in manufacturing industry, it is expected to reach over $ 600 million by 2022, growing at a CAGR of close to 40% year over year.
Siemens, a German conglomerate is leveraging machine learning technology to monitor its steel plants and improve efficiencies for decades. The company claims to have invested around U.S.D 10 billion in acquiring US software companies over the past decade. In March, 2016 Siemens launched Mindsphere, which allows machine manufacturers overall the globe to monitor machine fleets for service purposes. Siemens has also integrated IBM’s Watson Analytics into their product and service offerings. Another major player Krones, a German packaging and bottling machine manufacturer is using Microsoft Azure platform in order to attain their Industry 4.0 objectives by automating aspects of their manufacturing operations.
Now, machine learning offers opportunities for industry verticals that are a probably decade ahead of what was projected before. It will be playing a vital role in transforming the legacy system to modern enterprise applications. Now, investors and businesses should invest and place new bets in machine learning technology, in order to enhance customer experience, Return on Investment (ROI), and to gain a competitive edge in business operations.