The manufacturing industry is facing great transformations nowadays. Because of the rapid development of broad application and the digital world of Data Science. Modern manufacturing is often called Industry 4.0 which is the manufacturing under conditions of the 4th industrial revolution that has brought robotization, automation, and broad application of data.
The term 'Data Science' was initially coined in 2001 and it took less than two decades for it to be the trend it is today. Finance was the first industry to understand data science advantages when nobody might and then used it to sort through and analyze a lot of data and help industries reduce losses.
In the last 2 decades, manufacturers have been in a position to reduce waste and variability in the production processes and significantly improve product quality and yield (the volume of output per unit of input) by applying lean and 6 Sigma programs. However, in particular processing environments - drugs, chemical substances, and mining, for example - extreme swings in variability are a fact of life, at times even after lean methods have been applied. Because of the sheer quantity and complexity of production pursuits that influence yield in these along with other industries, manufacturers need a far more granular approach to correcting and diagnosing process flaws. Advanced analytics offers such an approach.
Today, Data Science is a force to reckon with and nearly all industries are attempting to leverage it's potential, and this number will continue to increase as data science technology gets to be more dependable and cost-effective. However, you need to understand industry-specific challenges, understand data attributes of each industry, and match market needs with custom abilities and answers.
What Data Science actually means to Manufacturing Industry
The data science jargon and advertising hype will subside sooner or later, and the manufacturing industry, among other sectors, are about to find themselves sitting with broken promises. It's thus essential that these companies recognize clearly the way they can gain from and be motivated by the challenges and data science.
Data Science has been efficient in tackling many real-world problems and is being used across industries to power much more smart and better-informed decision making. With the increased use of computer systems for personal operations and day-to-day business, there's a need for smart devices, can find out human behavior and work patterns. This creates Big data and Data Science analytics to the forefront.
In manufacturing, operations managers are able to use advanced analytics to take a significant dive into historical process data, identify relationships and patterns among discrete process steps and inputs, and then enhance the elements which prove to have probably the greatest impact on yield. Many global companies in a range of geographies and industries today have an abundance of real-time shop-floor data and the ability to conduct such complex statistical assessments. They're taking earlier isolated data sets, aggregating them, and analyzing them to disclose important insights.
Data science is believed to change the manufacturing industry dramatically. Let's take into consideration a few data science use cases in manufacturing that have already become widespread and brought advantages to the manufacturers.
· Predictive analytics
· Managing supply chain risk
Data Sourcing in Manufacturing Industry
Data can be received from plenty of sources in the manufacturing industry, but first things first. Manufacturers should start by laying the groundwork for a protected setting before they can start pulling precious data from machines and assets. Customers are already providing important data to manufacturers unbeknown to them in many situations. For example, most modern automobiles, mobile phones, and devices e.t.c have a built-in capability to track location and performance.
From buying, supply, product sales to maintenance, and so forth. Data can be collected and analyzed from social networking and visitor actions on the company's site in order to optimize and individualize customer interaction and conversion.
Even more effective is the point that inner systems of data collection and analysis like plant historians and ERP methods are a treasure trove of data. They may offer operational, process & product data. Classic methods of data collection as customer surveys, target groups, or call centers can't be ignored as they consume all-important unbiased data. I mention this because abstract data is simple to be a target of a self-fulfilling prophecy.
Machine to machine interactions and the use of IoT to gather data from assets, machines, and products machine readings could be probably the largest source of data in manufacturing scenarios. Service manuals and maintenance log sheets called dark data will also be a supply of useful data, one which calls for manual mapping into digital systems. Advancements in natural word processing are however remaining created to automate the capture of dark data, a move that would drastically support data science attempts.
Data Science and Big Data Analytics in the manufacturing industry is set to exceed $4.5 billion by 2025
The Global Big Data Analytics and Data Science in Manufacturing Industry was estimated for USD 904.65 million in 2019 and is expected to achieve USD 4.55 billion landmark by 2025, with a CAGR of 30.9% above the forecast phase, 2020-2025.
With the increased price of adoption of receptors and connected equipment and the enabling of M2M communication, there continues to be an enormous rise in the data points which are produced in the manufacturing industry. These data points might be of different types, which range from a metric detailing the time used for material to successfully pass through one process cycle or a more complicated one, as the calculation of the material stress capability in the automotive industry.
Manufacturing is among the most targeted industries by cyber attackers owing to the existence of essential data connected to government and company. According to EEF (Engineering Employers' Federation), more than 45% of the manufacturers are already subjected to a cybersecurity event.
With the growing integration of technological advances in manufacturing firms, security concerns will also be ascending at a significant speed.
The Big Data Analytics in the Manufacturing Industry Market is competitive & includes several major players. In terms of market share, there are only a few major players dominating the industry. These major players having a prominent share in the market are concentrating on growing their client base across internationally. These companies are using strategic collaborative initiatives to increase their market share and enhance their profitability.
How can AI help Manufacturing Industry?
Smart maintenance in layman terms is machines that can send data condition in real-time, and through software algorithms, predict and prevent malfunctions before they even happen. It’s about being able to pull all of this data together and also to visualize, automate, and optimize decision alternatives through software systems. That is smart maintenance.
Predictive analytics works on present data to forecast and avoid problematic situations in advance. Finding the best possible solution to hold problematic issues, overcoming difficulties, or preventing them from happening at all are some of the many opportunities for the companies using predictive analytics. Prediction models are aimed at forecasting the moment when the equipment fails to perform the task. Here are 2 major types of preventive maintenance: time-based and usage-based. The biggest strength of preventive maintenance is the focus on planning. Also the prediction concerning future situations with the right equipment, the manufacturer may plan a pause 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.
Demand forecasting and inventory management
Demand forecasting is a complex process involving analysis of data and the massive work of the accountants and specialists. There are a lot of benefits of demand forecasting for the manufacturers. Firstly, it gives the chance to control inventory better and reduce the need to store significant amounts of unnecessary products. Add-ons stay in the improvement of the supplier-manufacturer relations, as both can efficiently regulate their supply and stocks process.
The manufacturers spend a huge amount of money yearly on supporting warranty claims Modern warranty analytics solutions help manufacturers to process vast volumes of warranty-related data from various sources and to apply this knowledge to discover where the warranty issues are rising and the reasons for their occurrence.
Robots are changing the face of manufacturing. Nowadays, it is a common cause to deploy robots for performing daily routine/repetitive tasks, and those which may be difficult or dangerous for people. The AI-powered machine models help to acknowledge the ever-increasing demand. However, industrial robots largely contribute to increasing the quality of a product.
Computer Vision Applications
AI-powered advancements and computer vision applications found their role in manufacturing at the stage of quality assessment. Accordingly, object identification/detection and classification proved to be very concrete. Key advantages of the computer visions applications are:
· improved high-quality control.
· decrease in labor cost.
· high-speed processing capability.
· continuous operability 24/7.
Managing Supply Chain Risk
maintain the pace of the constantly changing tendencies the application of the
real-time data analytics has proved essential. Prediction and management of the
possible risk are important for the working of a successful manufacturing
business. Supply chains have always been complex and unpredictable. With the
help of data science, the companies may find it handy to predict potential
delays and calculate probabilities of the problematic issues. The companies may
use analytics to identify backup suppliers and successfully develop contingency