When new technologies become cheaper and easier to use, they revolutionize industries. That’s what’s happening with Big Data right now.

Big Data analysts investigate large volumes of data to find hidden patterns, correlations, and other findings. Today’s technology allows you to analyze and respond to data almost instantly, which is can be slower and less efficient when using more traditional business analysis solutions.

Data plays a huge role in today’s production processes. Advances in robotics and increased levels of automation are fundamentally changing the face of production.

Today’s leaders in manufacturing are visionaries who are adopting more and more efficient methods of producing and moving physical goods while thinking not only about productivity but also about reducing costs and risks.

The future of survival will require manufacturers to be flexible, AI-centric organizations that minimize risk and seize opportunities through a deep understanding of operations and confident decision making.

Manufacturing analytics solutions enable them to set up production operations with minimal cost and risk while using data as an asset that helps deliver innovative services and quality products, which only possible in an interconnected economy.

Let’s look at the key ways big data and analytics are empowering and transforming manufacturers in 2020.

Photo by Annie Spratt

1. Increasing Data Accessibility, Availability, And Agility

One of the most significant advantages of using the analytical approach is the simplification of data.

Traditional production data sets are very complex and often require specialists with system knowledge and experience to obtain information from them. The vast amount of information generated by production processes every second also makes it virtually impossible to make accurate, timely, data-driven decisions without the use of business analytic models.

The first step to any analytical process is to collect data. Manufacturers are investing heavily in collecting data from internal sources, including production data collection systems (EMI) and production execution systems (MES).

Data is also collected from sensors, supervisory control and data acquisition systems (SCADA), human-machine interface software (HMI), and supply chain and enterprise resource planning applications.

After collecting and aggregating the data, various analytical methods are used to obtain information.

Analytical methods include predictive modeling, machine learning, simulation, optimization and process management, and business analytics tools.

However, performing analytics with comprehensive technology can be time-consuming and costly.

Various analytical methods are used to obtain information about the production:

1. Using data visualization, determine initial patterns (using moving averages, distribution histograms, standard deviations, and clustering) to prioritize data collection and analysis.
2. Using correlation analysis, identify the main determinants of process performance and formulate an initial hypothesis about the root causes of the decline and yield variability.
3. Using significance testing, test the underlying hypothesis for root causes of yield decline and variability and focus on the most statistically significant factors for further research.
4. Using artificial neural networks, simulate complex processes to quantify the impact and optimal ranges for the identified parameters.

These methods of analyzing large amounts of data reduce costs and help to avoid bottlenecks and identify KPIs to improve performance.

In a broad sense, some of the key functions of the analytics include:

• Production improvement (operational efficiency)
• A better understanding of plant performance and live warnings
• Corresponding capacity in several plants
• Perform predictive modeling based on production data
• Interaction with suppliers
• Understanding of customer needs
• Better and faster service/customer support

2. Boosting Collaboration Opportunities Across The Board

Business analytics enables simultaneous access to different data sets for all stakeholders – from senior management to process owners, plant managers, and assembly line operators – on multiple devices or screens.

This increased sharing of information across the organization results in a closer collaboration that can ultimately improve product quality as well as minimize production costs and time.

For example, an operator on the floor can share data with a maintenance team to understand if the recent slowdown in production was caused by a technical failure on the assembly line. This allows the organization to identify and solve operational problems before they result in significant losses.

Another example is when Mercedes-Benz initially introduced an analytics system for the after-sales environment, they noticed the direct impact of this solution on operational optimization. Reporting became faster and more accurate as data processing time was significantly reduced while understanding the context allowed for more informed decisions.

This later led to the introduction of software for other functions such as production, sales, and human resources management. With a single data architecture that controls all business analytics, improved access to information thus simplified the entire organization and led to an increased inter-departmental collaboration of data.

3. Optimizing Business Functions And Driving Efficiency

No matter how efficient automated data analytics is, algorithms are often developed to find a concrete result. This often makes it difficult for manufacturers to get a holistic view of their complex data sets.

Business analytics, on the other hand, allows multiple stakeholders to interact with data sets live and conveniently. This allows for the integration of operational and experimental data for detailed information, which in turn can help optimize processes and improve the efficiency of production.

Besides, inbound and outbound supply chains can be strengthened, as well as resource discovery and utilization can be optimized to meet current and future demand.

Business data can also help identify additional sources of revenue and business growth opportunities for manufacturers more accurately.

Supply chain visibility is one of the critical challenges faced by manufacturers. This is partly due to large volumes of data such as product manufacturing, packaging, delivery/logistics, coding, etc. – involved in supply chain management.

Business analytics brings this information to the forefront, allowing manufacturers to identify and address supply chain bottlenecks. It helps improve supply chain responsiveness, reduce risk, and lower production costs.

4. Enabling swifter and more accurate decision-making

By providing a holistic view of all data across the business ecosystem, including from multiple sources, business intelligence allows manufacturers to delve deeper into their data and identify different trends and patterns relevant to them.

This review style approach allows them to analyze data using specific models and make accurate, proactive decisions.

Users can also take into account different data points, historical precedents, anomalies, measures taken, and their degree of effectiveness, acceptability of results, etc. to make more accurate decisions.

This helps them not only in their decision-making process but also in understanding why they are making particular decisions.

5. Making Factories “Smarter” And Benefiting From IoT

Industrie 4.0 is a perfect example of what modern factories will look like. It is an initiation of the German government – a high-tech strategy to promote computerization of production, which laid the foundation for smart factories. It covers every process from product idea to development and from recycling to maintenance.

Industrie 4.0 includes:

• Interoperability: Machines and sensors connected to the network and working synchronously.
• Automation: Physical devices are able to make their own decisions and are therefore automated.

While experts believe that India is one of the most ideal countries to benefit from, the Industrie 4.0 model, Cincinnati, Ohio, has already declared itself an “Industry 4.0 Demonstration City.” They are also investing heavily in innovation and development to address any major industry challenges they may face.

Since IoT is gaining fame in the industry, the future analytics will be a mixture of IoT and big data analytics implementation.

IIoT collects data from sensors, their transmissions, and microcontrollers that can track information and help manage the data. These two components together transform production processes and management, making manufacturers smart.

However, combining these two technologies requires new infrastructure, including hardware and software, as well as an operating system. Manufacturers will have to deal with the large inflow of data that begins to arrive and analyze it live as it grows over time.

6. Optimizing Quality Checks

Intel is one of the largest companies that actively integrates BDA into its production processes. Since quality assurance is an integral part of the chip production process, as with most manufacturers, they have to perform about 19,000 tests on each chip.

However, using the power of BDA, it has been able to reduce these steps significantly. For example, Intel’s analytics system can now view historical data collected during the manufacturing process at the plate level and identify only those chips that need to be tested.

In 2012, the chipmaker saved about $3 million in manufacturing costs using the predictive analysis process implemented on the Intel Core line of processors.

7. Improving Accuracy and Quantity of Production

McKinsey gave an excellent example of how BDA can significantly improve production practices. A biopharmaceutical manufacturer that manufactures a specific category of pharmaceutical products that includes blood components, hormones, and vaccines need to monitor more than 200 variables to ensure their purity.

Surprisingly, the yield of two separate batches of the same product produced using the same process can vary from 50% to 100%. Given how expensive medical products can be, even a 10% yield difference can be costly. Fortunately, there is a simple solution.

By dividing the entire production process into smaller segments and applying data analysis to each of them, the project team can process the dependencies between them and the parameters directly responsible for the yield difference.

Therefore, by adjusting these parameters accordingly, the team can quickly increase production by as much as 50%, saving up to $10 million per year.

8. Bettering Collaboration to Promote 3D Printer Factories and MaaS

3D printers are a trend, much like BDA. The factory that manufactures 3D printers can work easily and, more effectively, on the bases of BDA.

Moreover, we can offer a new type of service – Manufacture as a service (MaaS), the same as software as a service (Software-as-a-Service), which we have today.

Manufacturers of 3D printers, such as Materialise and Shapeways, already work on MaaS.

Making about 200 000 products a month, lastly, do tremendous business utilizing the automated software and 3D-printers, which work 24 hours a day, seven days a week.

Using BDA, these factories can work in the environment with a high level of cooperation where the stream of data and information passing through engineering, operators of machine tools, quality assurance, etc., is seamless. The result is amazing efficiency and fast feedback.

To Sum Up: Is BDA the future of the manufacturing industry?

In conclusion, BDA provides us with the tools and technologies to help create a world in which automated factories produce products with maximum efficiency and cause minimal wasting of time and resources. Besides, the leading players are already aware of this and have, therefore, taken the initiative.

Of the more than 200 North American manufacturing executives that were interviewed, nearly 68% outlined their plans to invest in data analytics to become more competitive in their highly competitive business environment. This indicates that data is rapidly growing into the new gold standard in manufacturing.

Nevertheless, much smaller volumes of data have begun to provide valuable information and real business value based on available data. Developing a strategy for a particular company and implementing a carefully planned initiative in this area is a complex task that requires considerable time and effort.

Author’s bio: 

Dmitrii B. is the founder of GRIN tech – full-service agency.