Modern manufacturing is often referred to as industry 4.0 that is the manufacturing under conditions of the fourth industrial revolution that has brought robotization, automation and broad application of data. The manufacturing business faces huge transformations nowadays. Due to rapid development of digital world and broad application of data science, various fields of human activity seek improvement. Hi, I am Rajkiran, GM at SeekACE Solutions, we train our new trainees with various examples of manufacturing processes. It helps them to understand different models and different algorithms and their functionalities. We are yet to work with a real client. But a huge test case data is easily available on the internet which allows us to train and fine tune our Machine Learning programs. In this blog, I am going to walk you through the several Data Science use cases in manufacturing that have already become common and brought benefits to manufacturers.
Robotization in manufacturing is not a new concept. 20 years or even prior to that, we have seen automation in large manufacturing units. It was used to perform routine tasks and speed up the manufacturing process. With modernization of technology, now we have sophisticated Robots to perform more difficult tasks and sometimes those tasks which are dangerous for human beings. Moreover, industrial robots largely contribute in increasing of quality of a product. Every year, the upgraded models come to the production floor to revolutionize the production lines. They are straightforward and manufacturing robots are becoming affordable for enterprises than ever before.
Now let us see how Data Science is helpful for Manufacturing Industries with a focus on the various areas where it is utilized and how it is utilized. The uniqueness of “Study@SeekACE” is to more focus on the application part rather than learning or mugging up theories and jargons. Here I will start with a predictive analysis for preventive maintenance and fault predictions for a manufacturing unit. So, here the obvious question is what is “Predictive Analysis”? – Predictive analytics is the analysis of present data to forecast and avoid problematic situations in advance. Manufacturers are deeply interested in monitoring the company functioning and its high performance. Finding the best possible way to hold problematic issues, overcoming difficulties or preventing them from happening at all are marvelous opportunities for the manufacturers using predictive analytics. The implementation of predictive analytics allows dealing with various issues like overproduction, idle time, logistics, inventory, etc. Therefore, let’s concentrate on the possible solutions brought by predictive analytics.
At SeekACE we deal with two models. One is Fault Prediction and the other one is Preventive Maintenance. Both these prediction models are aimed at forecasting the moment when the equipment fails to perform the task. As a result, the secondary goal may be achieved – to prevent these failures from happening or at least to reduce their number of occurrences. This becomes possible due to the numerous predictive techniques. Predictive modeling is a technique that uses mathematical and computational methods to predict an event or outcome. A mathematical approach uses an equation-based model that describes the phenomenon under consideration. The model is used to forecast an outcome at some future state or time based upon changes to the model inputs. The model parameters help explain how model inputs influence the outcome. The computational predictive modeling approach differs from the mathematical approach because it relies on models that are not easy to explain in equation form and often require simulation techniques to create a prediction. This approach is often called “black box” predictive modeling because the model structure does not provide insight into the factors that map model input to outcome. We need this approach because we do not have a huge data set which shows when actually Fault occurred. Here is the implementation of Data Science and its machine learning approaches.
Preventive maintenance is usually applied to the piece of equipment that is still working to lessen the likelihood of its failing. There are 2 major types of preventive maintenance: time-based and usage-based. The biggest strength of preventive maintenance is planning. Having at hand the prediction concerning future troubles with the equipment, the manufacturer may plan a break or a shut down for repairing. Such breaks are usually made to avoid considerable delays and failures, which are often caused by more significant problems that may arise.
Next thing that we train with is “Price Optimization” for various manufactured products. Manufacturing and selling the product involves taking into account numerous factors and criteria influencing the product price. All the elements starting with the initial price of the raw material and up to the distribution costs contribute in the final product price. And what happens when the customer finds this price too high or too low?
Price optimization is the process of finding the best possible price both for manufacturer and customer, not too high and not too low. Modern price optimization solutions can increase the profit efficiently for any given manufacturer. The Machine Learning tools aggregate and analyze pricing and cost data both from the internal sources and those of your competitors and derive optimized price variants.
At present conditions of highly-competitive market and changes in customers’ needs, price optimization has become a very useful tool for Manufacturers. We have worked with different products in our study center for our students. We have use case for FMCG products like Shampoo, Tooth Paste, we have use cases for FMCD like Refrigerators, Washing Machines. For these market segments a lot of Data Sets are available to try out a machine learning simulation. We have dealt differently with ecommerce scenario and in my next blog I will be writing about it.
Thank you for giving me time. See you in my next article where I will discuss about more uses cases in Manufacturing industry.