Decisions impacting the manufacturing process must always be grounded in factual information, rather than relying on guesses, wishes, theories, or opinions. The advancement of technology today has made it possible for both individuals and machines to gather and analyse the necessary data to achieve improved outcomes. The rapid adoption of cost-effective sensors connected to the internet has generated a great deal of excitement about the future of manufacturing. The integration of the Internet of Things (IoT) with big data and analytics has ushered in a new era of manufacturing with Industry4.0, Digital Twins and Artificial Intellegence. This involves leveraging data to drive cost reduction through modern sales and operations planning, significantly increased productivity, optimization of supply chain and distribution, and the development of innovative after-sales services. The future of manufacturing lies in data-driven operations, which are poised to elevate efficiency and responsiveness in production systems. Manufacturers now have the opportunity to integrate data into their decision-making processes effectively and productively.

Benefits of Data-driven Manufacturing

Data-driven operations have the potential to enhance efficiency and responsiveness within production systems. Manufacturers can now effectively integrate data into their decision-making processes, leading to increased productivity. The following are some key areas:

Increased Visibility

Utilizing data-driven manufacturing allows operations leaders on the shop floor to gain a deeper understanding of performance through the collection of valuable data metrics across the organization. By having access to accurate data, decision-makers can analyze individual asset performance as well as overall manufacturing operations. This enables them to identify areas for improvement, such as underperforming shifts, frequent machine downtime, and production bottlenecks.

IoT Technologies and Analytics

With an abundance of data available on the shop floor, manufacturers can leverage machine learning algorithms to tackle complex challenges. Through the integration of Artificial Intelligence and Machine Learning-driven analytics, manufacturers can implement advanced practices like predictive maintenance. By harnessing the processing power and data accessibility provided by these technologies, manufacturers can make informed, data-driven decisions.

Automation

Automation comes in two forms when utilizing a data-driven approach. The initial form involves the automated gathering of data, where specific devices collect data through software for processing without the need for manual intervention. The second aspect of automation involves utilizing data for automated decision-making. By employing predictive analytics, manufacturers are able to harness data to not only understand past and current events, but also predict future occurrences and take proactive measures autonomously.

Reduction in Operations Costs

The combination of data and lean manufacturing enables manufacturers to streamline production processes and reduce waste. Real-time data is essential in accurately measuring production enhancements and confirming that changes have indeed resulted in cost savings.

Challenges of Data-driven Manufacturing

In order to effectively implement a data-driven manufacturing strategy, it is essential to address several key challenges. Listed below are some of the primary obstacles that must be overcome:

Fragmented Data and Outdated Systems

When different departments use separate operating systems that are not integrated and lack cohesive documentation and communication, it can pose a significant hurdle. The inability to consolidate data from these disparate systems may result in decreased value derived from the collected information. One effective solution to address this problem is the implementation of an Internet of Things (IoT) platform. This platform can seamlessly connect various levels of traditional systems and bring legacy equipment online, facilitating improved data aggregation and utilization.

Security Concerns

As devices become more complex and interconnected, there is a heightened risk of potential security threats and data breaches. Prior to this technological advancement, security measures were not typically implemented at the machine level, leading to a lack of robust data protection standards or protocols.

Secure Data Storage

The growing volume of data generated by numerous connected devices within a data-driven manufacturing environment presents a significant challenge for data storage. Managing this increasing data flow requires a centralized repository for collection and processing, which can be costly for on-premises storage solutions.

Transitioning from Time-triggered to Event-triggered Manufacturing

Presently, the majority of manufacturing companies operate using the time-triggered manufacturing approach. Raw materials are inputted into the ERP system, which then transforms them into final products according to a predetermined schedule. However, as data-driven manufacturing becomes more commonplace, machinery will adopt the event-triggered manufacturing method. This shift represents a change in the existing manufacturing model and production mindset for manufacturers.

Legacy System Integration

In industrial automation, the incorporation of new technologies is vital for progress. However, it is equally important to seamlessly integrate these innovations with existing legacy systems. A modern factory operates on multiple system levels, presenting a challenge when the original developers of the legacy systems lack sufficient documentation to interface with new-age technologies. It is imperative to recognize the need for efficient integration within the current design and manufacturing environment, rather than starting from scratch.

Challenges in System Security

The interconnection of distributed control systems through the internet poses a risk of unauthorized access by malicious individuals. The growing number of IoT devices connected through gateways also extends the opportunity for remote control and access. Traditional manufacturing system gateways require significant reinforcement to address modern security threats faced by IT services. This entails enhancing computing power to effectively manage networking and security operations.

Moving from Data Exchange to Data Sharing

Developing a cohesive data model and integrating disparate systems in manufacturing can present challenges. The seamless mapping and sharing of this data across all business units is essential to reduce inefficiencies in resource and material usage. Employing IoT-driven sensors to identify equipment failures can help mitigate issues in data exchange.

Inconsistencies in Data

When manufacturing data is inaccurate or incomplete, it can significantly hinder decision-making processes, particularly in crucial projects where accurate data is essential for success. This often results in the need to dedicate considerable time, effort, and resources to rectifying and ensuring the accuracy and authenticity of the data records.

How Data-Driven Manufacturing can be Beneficial?

Research shows that data-driven companies are experiencing a steady 30% increase in annual growth, while also achieving profitability and successfully attracting and retaining a larger customer base.

Deriving Surprising Insights to Inform Decision Making

Utilizing advanced analytics to uncover unexpected insights can provide manufacturers with valuable opportunities for making informed decisions promptly and accurately. By utilizing the right data, manufacturers can effectively prioritize key issues and opportunities within their operations. Establishing Key Performance Indicators (KPIs) to measure and track relevant metrics will enable manufacturers to efficiently address and resolve any identified challenges.

Enhanced Understanding of Manufacturing Processes

Utilizing advanced analytics can enable manufacturers to uncover untapped opportunities for increasing production yields. While it may be common to assume that all potential process improvements have already been made, leveraging data can allow for a more thorough exploration of areas for enhancement. By delving into these data-driven insights, long-standing issues can be addressed, ultimately expanding operational capabilities without the need for additional resources.

Efficient Cost Management

By incorporating real-time shop floor data and sophisticated statistical analyses, a manufacturing organization can transform disparate data sets into valuable insights that can drive cost reduction and expedite decision-making processes. This approach not only helps to streamline operations but also aids in achieving significant cost savings.

Predict Market Trends

Utilizing business intelligence platforms, data-driven manufacturers can improve their ability to predict customization demands by identifying changing patterns and trends in customer behaviour. With data analytics, manufacturers can gain a detailed understanding of their production processes, leading to more informed and precise production decisions guided by predictive analysis.

Artificial Intelligence for Data-driven Manufacturing

In the manufacturing industry, achieving high levels of accuracy, constantly improving production quality, and maintaining top-notch processes are essential. Artificial Intelligence (AI) plays a crucial role in delivering these results effectively. By leveraging AI, manufacturers can shift towards a more data-oriented approach, enabling them to boost productivity and profitability. AI-driven analytical applications such as smart maintenance, Industry 4.0, predictive intelligence, and human-robot collaboration are key tools that help manufacturers stay ahead and drive continued growth.

Best Practices to Implement Data-Driven Manufacturing

Evaluation of the benefits that technology provides to businesses is crucial, rather than simply concentrating on its advanced functionalities. The idea of data-driven manufacturing is supported by many, and the introduction of technologies like IoT and Big Data solutions is gradually bringing this concept to fruition. This offers a favourable prospect for industry decision-makers, but it will soon become the norm across the sector. Manufacturers transition to being data-driven only after going through a methodical transformation. Here are the recommended steps to follow:

Identify Time Constraints

Analysing lead time can pinpoint underlying issues within the supply chain. Identify bottleneck operations and delve into the root causes and effects. Enhancing manufacturing operations management necessitates meticulous adjustments, prioritizing the elimination of ongoing delays and time wastage.

Utilize Existing Data

Manufacturers should leverage the data they currently possess to start analyzing and gaining valuable insights. This data may include financial, operational, or physical data, all of which can provide relevant information for process engineers and continuous improvement experts. By working with the data that is readily available, companies can enhance their ability to manage the larger volume of data that will be acquired in the future.

Leveraging AI for Insightful Data Analysis

The initial obstacle in data collection is followed by the task of uncovering valuable insights within that data. Utilizing AI can greatly assist in this endeavour due to its superior speed and intelligence compared to humans. AI-powered analytics have demonstrated enhancements in order-to-delivery cycle times and overall supply chain efficiency. When comparing options, integrating AI into supply chain operations simplifies the process of effectively harnessing analytics.

Uncover the Unseen

A large portion of information regarding operational processes remains hidden, even in the most advanced factories globally. It is imperative to gather data from reliable sources that can shed light on these unknowns. Implementing connected sensors proves to be a valuable method for gaining insights into previously obscure procedures.

Maintain a Balanced View

As companies gain proficiency in handling data, there may be a strong inclination to rely heavily on technology. While fully automated manufacturing can be advantageous for certain companies with consistent demand, it may not be as beneficial for those with fluctuating demand. It is important to assess the specific needs of each company before implementing automation processes.

Why Nirmalya Enterprise Platform for Data-driven Manufacturing

Nirmalya's Supply Chain Management with Business Intelligence utilizes AI technology to analyse real-time industrial data, detecting and addressing operational bottlenecks to increase efficiency and save on costs. By making data-driven decisions, you can overcome productivity obstacles and achieve growth and profitability. It offers improved efficiency, enhanced quality, minimized scrap, and timely deliveries through digital transformation and Smart Factory initiatives. Below are some key features that assist enterprises in becoming agile and intelligent:

• Streamline and automate various processes, eliminating unnecessary manual tasks and reducing production bottlenecks.
• Free up workforce to focus on higher-value activities by automating repetitive tasks.
• Enable to track and analyse material usage, energy consumption, and other resources in real-time, helping them identify areas of inefficiency and reduce waste.
• Minimize waste to become more sustainable and reduce their environmental impact.
• Enable real-time monitoring of critical parameters, ensuring that any deviations from the specified standards are detected early.
• Take corrective actions, thereby improving product quality and customer satisfaction with early detection.
• Provide real-time visibility into equipment performance and health, enabling manufacturers to detect and address issues before they escalate.
• Real-time shop floor visibility to anticipate and solve production problems before they happen, leading to smoother operations and improved overall performance.
• Get accurate and timely information at fingertips, optimize production processes, eliminate bottlenecks, and minimize downtime.
• Enable to maintain consistent quality standards and improve customer satisfaction.
• Identify and resolve quality issues before they impact the final product with real-time monitoring and data analysis.

Transform your plant data into actionable insights with our innovative MES platform and achieve unparalleled success in your industry. Contact us to learn more about how Nirmalya's Enterprise platform can enhance your data-driven manufacturing processes.