Why the Manufacturing Industry Should Embrace Data Analytics

Manufacturing remains one of the primary driving forces of the world economy, with its market value predicted to reach $4.55 billion in 2026 from $904 million in 2020 at a CAGR of 30.9% during the forecast period.

Businesses across the board are going digital, and this increased digitization is allowing businesses to adapt to changing market needs. In these uncertain times, digital transformation is not an add-on strategy – it is a necessity to stay relevant.

The manufacturing industry is no exception. However, only a select few companies are going all-out to embrace Industry 4.0 efforts. Since they are capturing the full value of data and analytics, they can draw value from their transformational efforts and benefit from a decent ROI.

Although industries have taken to the digitized industrial revolution, most manufacturing industry players are unable to successfully leverage the full potential of data analytics and a value-driven approach to digitization.

Why is the Manufacturing Industry Still Lagging?

Combining advanced technologies like IoT, cloud, AI and ML, the manufacturing industry can address some inefficiencies in production, quality, and customer relations. Yet, only a few groups of manufacturers are using these innovative technologies to further their business goals. The manufacturing industry, as a whole, is still lagging in implementing digital strategies throughout the entire value chain.

In the face of changing times, the industry is meeting goals using techniques steeped in tradition and only seeking advanced data analytics methods when conventional practices are not working. Reluctance and resistance to investing in digital data analytics can prove costly for the industry.

Do we Need Manufacturing Data Analytics?

For any manufacturing unit, it is a challenge to keep up with demand, ensure quality, reduce operational costs, bring in profits, and optimize the supply chain. To achieve these goals, manufacturing units should leverage the benefits of data analytics.

Data is generated during every stage of the manufacturing process, and this data can be used to optimize production and identify the cause-effect relationship between operations and goals. It can help save time, money, and personnel and increase production efficiency.

How is Data Analytics Unlocking Value for the Manufacturing Sector?

Industry 4.0 is not a singular concept; it encompasses a variety of advanced technologies and tools. Some of the foundational technologies furthering Industry 4.0 efforts are:

  • Connectivity: Data connectivity and computation encompass cloud computing, blockchain, sensors, and IoT.
  • Analytics: Data analytics includes artificial intelligence, machine learning, and advanced analytics.
  • Machine-human interaction: VR/AR, robotics, chatbots, and automation tools.
  • Advanced engineering: Renewable energy, rapid prototyping, 3D printing, and ALM (additive layer manufacturing).

Data Challenges in Manufacturing

Data analytics is not only transforming the manufacturing industry, but it is also having an immense impact on several industries. However, there are a few data challenges plaguing the manufacturing industry. Let’s find out.

  • Although data is abundantly available for manufacturing units, it is often unstructured and presented in inconsistent ways. It makes it challenging for the manufacturers to analyze and use the insights from the data efficiently.
  • Companies run on legacy ERP, execution, and production systems, which make it difficult to integrate with other technologies.
  • The sheer volume and complexity of data manufacturers collect puts a burden on storage management systems.
  • As the volume grows, the need for advanced integration and visualization tools increases.
  • Furthermore, as the sensors collecting the data and the control systems managing the data increase, it puts pressure on IoT device gateways. Since manufacturers do not possess advanced networking and computing power, they become vulnerable to unauthorized access and security threats. 

How Data Analytics is Providing Significant Value to Manufacturing

According to McKinsey and Company, the value potential of using Industry 4.0 concepts in manufacturing plants includes a 15–20% reduction in inventory holding, a 30–50% reduction in downtime, 85% accuracy in forecasting, a 15–30% increase in productivity, and more.

Discrete Event Simulation

DES, or discrete event simulation method, is an approach to data analysis used to find solutions to logistic and manufacturing problems that otherwise can’t be solved using conventional methods. DES includes mathematical programming and statistics to represent complex production systems by showing the sequence and interdependence between various systems.

DES is used for factory planning, production scheduling, supply chain management, process design, and more, using real-time data gathered using sensors.

Productivity Analytics

Using data analytics, the status of a manufacturing unit’s productivity and quality can be assessed. Productivity is calculated based on the data collected on machine downtime, poor resource allocation, inefficient use of machinery and personnel, and more. With data analytics, it is possible to streamline inventory, optimize productivity, and provide credible demand forecasting.

Quality Analytics

Product quality can be improved by identifying potential pain points and downstream quality issues. The generated data can be analyzed to identify patterns, troubleshoot issues, and suggest actions that eliminate such problems.

Predictive Maintenance

With data analytics, manufacturers can improve their predictive maintenance systems, enhance equipment efficiency, increase shop floor productivity, improve safety, improve quality, reduce risks, and optimize planning.

By incorporating data analytics, machine sensor data, and statistical methods, it is possible to predict machine breakdown and repair needs.

Balancing Automation and Human Labor

Manufacturing is a unique sector that depends on automation and human involvement to undertake production. For certain procedures, human contribution is irreplaceable, and in others, automation holds the key to production. With data analytics, it is possible to work out a feasible staffing solution, streamline automation, and maintain a workable ROI. 

Lowering the Operating Costs

One of the major reasons for quality and productivity issues is the delay in getting data-backed solutions to problems. An enterprise-wide dashboard that can give constant, real-time insights into the supply chain can allow employees to address issues within seconds. 

Beefing up Security Systems

When manufacturers are collecting massive amounts of critical data, they need to invest in enterprise-wide data security systems to keep the information safe from misuse. The manufacturing unit should ensure that special authorization is needed for access, a robust security layer is provided for every dataset, data governance and management become strong, and the data insights drawn are traceable. 

Streamlining Supply Chain and Inventory Management

Data science analytics can be used to streamline the supply chain and manage the risks associated with it. Manufacturers can identify potential risks and delays to make necessary backup plans. The data can be analyzed to accurately forecast demand and manage inventory. 

Using AI and data analytics, manufacturers can understand the market, predict the availability of raw materials, and procure inventory based on future analysis.  

Data-driven Manufacturing Holds the Key to the Future.

Leveraging the benefits of data-driven insights has become a strategic necessity for the manufacturing industry, and it can achieve this only when combining internal and external datasets. 

Even though the industry generates massive amounts of data, it must use analytics and AI solutions to extract significant value and improve production efficiency and quality. In a mad dash to digitize, manufacturers are skipping critical process steps to demonstrate quick results. 

However, it is important to slow down, design a strategic and specific data analytics plan, describe the value, and develop a compelling goals-aligned roadmap before charting their Industry 4.0 transformations. 

If you are keen on going forward in your data analytics and digital transformation journey, we suggest getting in touch with our Kemsys experts to accelerate your success.