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

Using big data to improve as a manufacturer

Here's how to implement manufacturing analytics today, in a world where big data, business intelligence, and artificial intelligence are steadily expanding.

Big data is everywhere, and it’s finding its way into a multitude of industries and applications. One of the most fascinating big data industries is manufacturing. In an environment of fast-paced production and competitive markets, big data helps companies rise to the top and stay efficient and relevant.

Manufacturing innovation has long been an integral piece of our economic success, and it seems that big data allows for great industry gains. Improvements in efficiency, maintenance, decision-making and supply chain management are possible with the right data tools. Anything from staff schedules to machine performance can be improved with big data.

Decreasing inefficiency with big data

Manufacturers are always looking for ways to make marginal improvements in their systems and how they operate. This type of management can be complex, and with the many different steps of the supply chain, teasing out every last detail to improve can be challenging. Thankfully, with big data, manufacturing companies can competently manage supply chain details in order to oversee any possible improvements available.

Big data allows manufacturers to look at each discrete part of a supply process. This microscopic view of the supply chain can show managers new insights into how their process can be improved or tweaked. Big data can be used in different ways to cut down on supply chain inefficiencies. Individual machines, supply chain setup, and staffing, among others, are all components of a manufacturer’s efficiency.

More and more manufacturers are closing gaps in inventory inefficiencies, too. For example, 72% of manufacturers consider real-time monitoring essential for modern inventory reconciliation.

Managing supply and customization

Taking the customer’s preferences into consideration when configuring the manufacturing processes is of extreme importance. The need for consumer customization is a challenge for supply chain managers. Cookie-cutter solutions don’t apply to consumers anymore. They want and need customized products and services. However, in most scenarios, added customization equals added costs. Big data can help bridge that gap of wanting to appease customers while making ends meet at the same time.

With advanced data analytics, manufacturers can see customer data in real-time. This reduces the time required to make necessary adjustments to the product lines, cutting down on wasted time and improving overall efficiency.

One of the largest effects of real-time monitoring in manufacturing is the ability to improve order-to-fulfillment cycle times. Building a robust data platform can transform the way manufacturers handle their customers and supplies. Not only are real-time results available, but big data can also provide demand forecasts to guide the production chain based on historical data sales trends in order to stay on top of the demand.

Predictive maintenance

One way to reduce the amount of downtime spent on fixing manufacturing machines is fixing the machines before they break. The ability to monitor manufacturing assets in order to predict necessary maintenance is another application for big data. The less time a machine is out of commission, the less money is being lost. With increased notice before a breakdown occurs, you can secure an easy win for your company’s return on investment: you’ll be able to form a strategy around those maintenance intervals and costs without having any negative surprises.

Big data means using a wired or wireless connection to track machine utilization with greater accuracy to see the variables that could impact its performance. A manager can see what or who is performing optimally, giving the information needed when making business decisions.

Improved strategic decision-making

With all of the information available today, many decisions can be driven by big data. The power of advanced data collection and monitoring systems means increasingly little guesswork when it comes to overall management strategy. A well-structured data management system can connect supply line communication. There can be many areas within a manufacturing company that may not speak to each other effectively. If big data is applied to the process, information can be gathered and analyzed across departments and locations.

With big data, there is less guessing and more data-backed action.

Deconstructing big data in manufacturing

There are several steps involved before big data can be utilized by parties within the manufacturing industry:

• Gathering and storing data: The ability to gather data is essential in the big data process. Although many systems can gather data, accurate data is much harder to find. Once the data is gathered, it must be stored. Storing data is essential for keeping quality records of important business assets as well as for overall safety and auditability.
• Cleaning and analyzing data: Gathering and storing data is not helpful when you can’t find the data you need to make decisions. Data cleaning allows the immense amount of data to become more scalable. Trends and patterns are easier to spot when the data is clean. Analyzing relevant data is what leads to strategic business decisions.
• Data mining: The ability to find information fast and easily is of extreme importance in the manufacturing industry, since each decision can have a major impact on the bottom line. Advanced data mining allows a company to find the data they need exactly when they need it.
• Data monitoring: A strong data monitoring system allows manufacturers to keep their business up to industry standards. The continual ability to monitor important data points that matter to your company is essential in having a competitive advantage.

Conclusion

Big data is certainly a buzzword within many industries, and for good reason. The ability to collect important data is priceless to a business and can easily lead them to a competitive advantage. However, the ability to use big data in an efficient and useful way in order to make business decisions is more challenging. Making sure there is a purpose behind all that data is necessary for taking advantage of all big data has to offer.

Author: Megan Ray Nichols

Source: SmartDataCollective