manufacturing data analytics

Drawing value from data in manufacturing companies

The modern manufacturing world is a delicate dance, filled with interconnected pieces that all need to work perfectly in order to produce the goods that keep the world running. In this article, we explore the unique data and analytics challenges manufacturing companies face every day.

The world of data in modern manufacturing

With the datasphere growing exponentially to an expected volume of 175 zettabytes by 2025 , it stands to reason that manufacturing is experiencing the radical impact of this growth, just as much as other business areas.  Manufacturing companies that adopted computerization years ago are already taking the next step as they transform into smart data-driven organizations.

It’s easy to see why. Manufacturing constantly seeks ways to increase efficiency, reduce costs, and unlock productivity and profitability. Data is a critical tool for identifying where and how that can be done in any manufacturing process. Whatever your department, whether you’re concerned with production, inventory, warehousing, or transportation and other logistics, knowing precisely how your operation is running nd where it can be improved is essential to improving your bottom line.

From a practical perspective, the computerization and automation of manufacturing hugely increase the data that companies acquire. And cloud data warehouses and data lakes give companies the capability to store these vast quantities of data. However, it’s only useful if you can accurately analyze it and get the insights you need to enhance your business.

Modern factories are full of machines, sensors, and devices that make up the Internet of Things. All of them generate a trail of performance-tracking data. The challenge for manufacturers is to capture all this data in real-time and use it effectively. To achieve this, they need a BI and analytics platform that can transform the data into actionable insights for their business. And a substantial proportion of this data can be gathered and organized by analytics embedded at the network’s edge, within the manufacturing equipment itself. As a result, you can get insights faster, at the very place that they’re generated, and without the need for IT teams to gather, analyze and generate reports, which is time-consuming and uses resources that could be better applied elsewhere.

Here are three key areas where data adds value to the manufacturing process to give companies a competitive edge.

How data enhances product development

Every part of a business generates big data. Analyzing data from disparate sources to identify relationships between processes, causes, and effects is part of what helps a business hone its product development strategy, manufacturing processes, the marketing and sales of those products, and the logistics of supply chain and delivery.

We asked Christine Quan, Sisense BI Engineer in sales, how she thinks data helps product development, and she said: 

'Surveying market data enables you to have a better understanding of customer needs and can also be a way to gather feedbacj for initial product ideas'.

Indeed, data enables a company to understand its customers better. With this information, it can develop new products or improve existing products to meet customers’ needs. At the same time, data can inform a company about potential markets so it can judge how much risk an innovation carries. Consequently, this risk can be mitigated in the product development process, because the more a manufacturer knows before production, the less of a gamble it is. Furthermore, actionable insights derived from data both before and during production can be used to plan and hone the manufacturing process, and enhance many operational aspects.

Take BraunAbility, for example. The company manufactures and installs adaptations to personal and commercial vehicles that make them wheelchair accessible. Using a BI and analytics platform, BraunAbility has improved its understanding of customer preferences in different markets. Data has given the company the insights to drive production of the most in-demand products, make informed decisions about what it keeps in stock and even what product discounts should be offered to impact the sales rate positively. With this new information, BraunAbility sees better profit margins across the board.

Data improves and streamlines production quality control

The analysis of big data sets generated in the manufacturing process can minimize production defects and keep quality standards high, while at the same time increasing efficiency, wasting less time, and saving more money.

Embedded analytics are particularly valuable in terms of quality control and optimizing manufacturing efficiency. Computerized and automated monitoring systems, far more sensitive and accurate than the human eye, capture discrepancies more accurately and more cheaply, around the clock. This continuous, smart, machine-based scrutiny significantly decreases the number of tests essential to maintain quality parameters. Data can also be used to calculate the probabilities of delays, to identify, develop and implement backup plans.

Embedded analytics are also faster and more autonomous than more traditional data analysis. With embedded analytics, it’s no longer necessary for data analysts to feed the data lake to the stand-alone cloud data warehouse, then mash up the data and verify the results. Analytic technology embedded within machinery can do the job at the point at which the data is generated. So, less intervention from data analysts is necessary, decisions can be influenced directly by data and processes are accelerated, using fewer resources.

Effectively, Big Data enables manufacturers to improve and streamline their processes across production and quality control. As Christine Quan explains: 

'Setting up a comprehensive data feedback loop enables you to get real-time information about all aspects of your manufacturing processes, so you can rapidly calibrate them to boost production efficiency'.

his is particularly pertinent to asset-heavy industries such as pharmaceuticals, electronics, and aerospace parts manufacturing, in which superior asset management is critical for efficient and profitable operation.

Certain processing environments like pharmaceuticals, chemicals, and mining are prone to considerable swings in variability. Coupled with the number and complexity of elements in the production processes in these industries, such companies can find it challenging to maintain the stability and uniformity of processes. They can benefit most from advanced analytics because it provides a highly granular approach to diagnosing and correcting process flaws.

McKinsey Company gives the example of the biopharmaceutical industry that includes the manufacture of vaccines, hormones, and blood components. They are made using live, genetically engineered cells, and production often involves monitoring hundreds of variables to ensure the purity of the ingredients and the substances being made. Two batches of a particular substance, produced using the same process, can still considerably vary in yield without explanation. This can detrimentally affect capacity and product quality and can attract intensified attention from regulators.

Advanced data analytics can overcome this issue without incurring huge costs. By segmenting the manufacturing process into clusters of related production activities, gathering data about these, and analyzing the data to show interdependencies, it’s possible to identify stages in the process that influence the variability in yield. Address those and the yield can increase by a value of millions of dollars per product.

Improving the supply chain and mitigating its risk

Major manufacturing processes require a lot of raw materials and components that together form a complex supply chain. Inevitably, the larger and more complex the supply chain, the riskier and more prone to problems it is. Many supply chains struggle to gather and make sense of the huge volume of data that they generate. However, Christine points out that: 

'Having the right data can help you de-risk decisions by providing a more holistic view of your supply chain'.

That’s because big data analytics and cognitive technologies like machine learning bring visibility to supply chains, and help manufacturers manage them, mitigate the risks, offer a better customer experience, and therefore give them a competitive edge.

Analyzing data can identify where and how problems are occurring and can even predict where delays and other issues might occur. So, robust analytics allows manufacturers to develop and implement contingency plans that enable them to harmonize the supply chain with manufacturing requirements, sustain the pace of production and maintain maximal efficiency, essential for the ongoing performance of your business.

Making the changes work for manufacturing

Of course, manufacturing predates the advent of smart data analytics and in some cases, it takes time for it to catch up with emerging trends. Nevertheless, manufacturers know that to stay ahead they need to adopt new processes and technologies involving data, analytics, AI and machine learning.

These technologies can drive improvements in modern manufacturing environments that face the challenges of process complexity, variability, capacity, and speed. By applying smart data techniques to the manufacturing process, companies can meet and exceed demand and the requirements of the market, anticipate and avoid possible risks, minimize waste and reduce problems, and maintain high quality standards. Harnessing the power of big data and implementing the right analytics technology will ensure that manufacturers achieve their business goals more efficiently and cost-effectively than ever before. 

Source: Sisense

Author: Adam Murray