Machine learning (ML) as a technology has been gaining traction across multiple business segments (e.g., consumer behavior, e-commerce, financial services, health care sector, logistics) because of its ability to solve problems in a practical business setting. It, therefore, becomes necessary for the executives across the businesses to understand fundamentally what ML is about and how it can be applied to make timely decisions and improve the performance. This article focused on attaining these objectives.
Background
A core purpose of any new technology is to disrupt the status quo positively, and ML is no different. In combination with the Internet of Things (IoT), big data analytics, etc., with ML, several solutions are delivered, that paves the way for increasing the revenues of your businesses. For instance, in the manufacturing industries, smart systems offer the ability to predict ‘machine failures’ by sending warning signals before they take place.
The marketing analytics enables the marketing team to become more intelligent by facilitating them to map the customers’ spending habits. View it from any angle, the rationale for the application of ML is justified, be it from the point of incorporating the intelligence or introducing the smartness to get tangible (e.g., products) or intangible (experiences of clients/customers) outputs that would better your performance outcomes.
What is Machine Learning (ML)?
It forms the basis for ‘cognitive computing.’ It does this by providing the hands of the businesses the power of ‘real-time,’ ‘evidence-based’ decision-making in an automated environment. Companies can navigate mere data to practical wisdom through accessing IoT and other sources of data from a cloud-based data store. Automate the analytical process by deploying machine-learning techniques to find patterns, which can be used to make predictions and moving towards solutions.
It is possible to refine them through an iterative run of the algorithms and or models. The generation of ‘pointed knowledge’ in logical sequences made possible, which would enable you to visualize the complete picture by piecing them together. When done so, the solutions emerge, which you can apply to improve your business operations.
Why does your business need Machine Learning (ML)?
Here are some of them:
- As a decision-maker, to aid the right decisions, you need practical tools. ML serves this purpose as a design tool to chalk out and implement business strategies.
- Predicting outcomes is one of the strategic functions that determine your business success. When there is a reliable system that would predict the behavior of the customers, you can make better decisions based on the reports and dashboards in real-time.
- Again, someone who is looking for solutions, you want to go beyond predictions and go deeper to understand as ‘why’ certain things are happening and ‘what’ you have to do if corrective actions are needed. It brings out yet another salient feature of M.L., which is prescriptive.
- That’s, it is now possible to apply specific elements to get particular outcomes. ML makes this possible through the application of stimulation. The prescriptive analytics provide insights, recommendations for optimization.
You are now armed to go a step further, taking a ‘human-like’ action, with ‘cognitive analytics.’ It tests, learns, and adapts over a period and bridges the gap between ML, Big Data, in which you can make practical decisions in real-time in a digitally connected world.
Machine Learning (ML) & Supply Chain Management (SCM)
Manufacturing organizations get the ability to control their supply chain operations. All the more efficiently as ML makes it possible to collect data, analyze them, and proceed at each level through an automated system. As a result, it is possible to:
- Produce consistent, reliable, productivity and output
- Assess and act by your workforce to know when a part for equipment needs repairs/modifications and take appropriate action.
- In the supply-chain logistics, to connect the vehicles with sensors to monitor the temperature. Which would not only avoid wastage in perishable but also make them safe
In the public transit system, to minimize the accidents by connecting smart infrastructure with other inter-connected systems (e.g., traffic signals). Besides, it is also possible to reduce traffic congestion, save fuel, reduce carbon emissions, etc.
Key take away of this article
Even though the business strategies made in the board room. It is the tools that are available for practical applications. At the ground level that determines the success or otherwise of any business. ML is one such crucial technological enabler. Which contributes to the making of a product and also in its marketing through a physical or virtual SCM. Industries would have to fine-tune their supply chain systems to respond to suppliers and customers promptly. While ML plays a decisive role in the realization of this, it is necessary to sound a caution as well. Which is ‘one-size does not fit all.’ Before developing ML-based products/services, you need to assess.
Plan your business strategies and determine the priorities. Otherwise, you would be risking your investments. When done in the right manner, it has enormous potential. And vast applicability in a variety of industries and services, like manufacturing, transportation, health care, retail, etc.
Keywords: BUSINESS, MACHINE, LEARN, Machine Learning, DATA, PRACTICAL, ANALYTICS, DECISION, SOLUTION, Supply Chain
