Digitization in Manufacturing: Top 5 Reasons & Benefits

With Industry 4.0 changing the manufacturing vista, companies are aggressively looking to harness the power of data. At least 74% of manufacturers believe digitization can significantly streamline their manufacturing operations.

As a core element of digitization, data is the most important strategic success factor in today’s manufacturing. So, whether it’s about experimenting, or launching new products, manufacturers need to have a robust repository of relevant data.

Traditionally, sourcing data and building datasets have been tedious processes, as they require management of multiple stakeholders and cross-functional complexities. So, despite operations leaders well aware of the importance of data for achieving strategic advantages, lack of sound data processing practices makes them struggle for solutions.

An agile business runs on agile data processing. KPMG insights say that 67% of global CEOs believe agility is the new currency for business. However, to secure competitive advantages, manufacturers need well-defined data processing solutions.

In this context, let’s have a look at some of the primary reasons to adopt digital manufacturing, and the benefits it can bring to manufacturers.

 

Top 5 reasons for adopting digitization in manufacturing

Right from streamlining simulations and modeling to improving human-machine and machine-machine interaction, there are multiple irrefutable reasons for adopting digitization in manufacturing.

1. Connectivity and computational power

Today’s manufacturing chains are characterized by new-age data sources like sensors, Internet of Things (IoT), Blockchain etc. with the huge amount of incoming data volumes, powerful data processing mechanisms are needed to act as dependable conduits between the source and the destination.

A robust data processing framework streamlines sourcing - collection or extraction of data from data sources. Irrespective of the data source type, it aligns the source with the end-user – machine or human – as the context might determine. Reliable data delivery ensures consistent connectivity while maximizing the computational power of associated machines.

2. Analytics and Intelligence

Whether it be for perfect production schedules, allocating slots to fleets at loading units, or determining machine servicing points, no single activity is comprehensible without analytics. However, analytics and intelligence depend on receiving accurate data.

The way data is processed has its implications on the preprocessing stage. As a series of necessary steps, data processing streamlines data sourcing, data verification, data transformation, and data standardization. Helping data achieve uniformity, it simplifies its journey to analytics and intelligence, thus improving the outcome accuracy.

3. Human-machine interaction

Human-machine interaction is a significant part of digital manufacturing operations. And how do these interactions take place? They speak the language of data. Machines generate data and humans use it, interpret it to drive improvements, scale, and achieve higher efficiency.

But machines need not generate data directly in interpretable forms. So it requires interposing a processing framework between raw data generation and final reception by end-users. By implementing a suitable data processing approach, you can help stakeholders access the values that are critical to judging the soundness of the processes.

4. Simulations and modeling

At the heart of manufacturing digitization lie simulations. Control rooms remotely regulating the access of heavy-duty machines rely completely on data to gauge the efficiency of ongoing operational execution.

However, simulations and modeling demand data to be processed contextually across the entire lifecycle. As a core operational practice that offers key insights for real-life decision-making, having an immaculate Simulation Data Management (SDM) thus becomes important. SDM offers the power to successfully manage several thousand structures of processing data for simulation, modeling, and analysis.

5. Re-engineered performance

Digital enablement makes sense if that’s improving the performance not just by 50-60%, but it at least doubles or triples the efficiency. Finally, enhancing the existing efficiency levels by 300-400% is actually what today’s improvements denote. For processes to be re-engineered, the data around them have to be refined and used to its fullest potential.

Process re-engineering is proportional to data processing. So, to harness the power of data fully to reach next-level process performance, you need an adaptable and scalable data processing framework.

 

Benefits of digitization in manufacturing

Having touched upon the reasons for the importance of digitization in manufacturing, it’s time for us to gauge its true benefits.

  • Improved safety at work

Processing safety data correctly enables work safety intelligence to highlight processes that need attention and thus improve safety. So, data-based safety enhancement progressively enhances the entire safety mechanism, while simultaneously reducing the loss of work hours and workdays.

  • Process consistency

Quality data processing improves intra-organization as well as inter-organization coordination and communication. Technically, it improves equipment performance by reducing downtime and implementing better equipment maintenance schedules. Thus, quality data streamlines operations and ensures consistent output generation.

  • Easier execution of operations

Data does make operations easy to execute. Because real-time dashboards offer insights on operational KPIs and assist right from the mechanic to operational heads to take decisions on initiating requisite actions. So data-based operations management enables workers to quickly familiarize themselves and operate machines easily.

  • Data-driven culture

Investing efforts in building a quality data processing mechanism automatically creates a data-driven process management culture. Each stakeholder in the lifecycle becomes accustomed to using data, which paves the way for logical decisions.

 

Conclusion

Better decision-making demands smart data processing frameworks, which can easily tackle data complexities and extract relevant information.

By deriving value from data, manufacturing businesses can succeed, but they find themselves facing tough challenges. To successfully adopt digitization and get processes streamlined, you need a proper data processing partner. A strategic partner helps you resolve data overload issues, and to align operational improvement with strategy by converting silos into an integrated ecosystem.

 

About Author
Snehal Joshi spearheads the business process management vertical at Hitech BPO, an integrated data and digital solutions company. Over the last 20 years, he has successfully built and managed a diverse portfolio spanning more than 40 solutions across data processing management, research and analysis and image intelligence. Snehal drives innovation and digitalization across functions, empowering organizations to unlock and unleash the hidden potential of their data.

 

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