The Power of Data Management in Driving Smart Manufacturing Success
Tags: iiot, data, manufacturing
Published: 2023-05-25 05:00:00

[Smart Manufacturing ](https://www.hivemq.com/solutions/manufacturing/)encompasses a diverse range of priorities and objectives for companies in the industry. For some, it’s about innovating customer service to stay ahead of emerging competitors, while others focus on improving quality and cost performance. Despite the differing objectives, every smart manufacturing pursuit emphasizes the vital role of data in realizing their desired outcomes.

However, many businesses embark on their smart manufacturing or industry 4.0 journey with isolated digital projects, lacking a comprehensive data management strategy. While these projects may produce positive results, they eventually give rise to various challenges that necessitate reevaluating the approach. Issues include a complex network of digital technologies, an array of disparate solutions that are challenging to scale, an uncoordinated and frequently inefficient digital infrastructure, and escalating costs associated with digital investments arise.

**_This article serves as the first installment in a six-part series that will explore how to establish a well-thought-out strategy for harnessing the power of data in smart manufacturing. By providing an overview of the importance of data management and its role in driving success, we lay the groundwork for the subsequent articles in this series, which will delve deeper into specific data management techniques._**

## Aligning Data Management with Business Objectives

It is essential to emphasize that the key to successfully implementing a comprehensive data management strategy is ensuring that it aligns with and supports the overarching business strategy. In many manufacturing businesses, there’s a continuous effort to create consistency in both business and operational methods. This process started by introducing a shared ERP system at the company level and has since expanded to include process and manufacturing operations.

A significant obstacle in promoting uniformity across operations lies in the vast diversity present among physical production facilities. Consequently, numerous companies are identifying clusters of similar operating technologies as a starting point for establishing a degree of consistency. Then they employ data-driven key performance indicators (KPIs) like Overall Equipment Effectiveness (OEE), Lead Time, Time to Resolution (TTR) for quality issues, Product Development Costs, and Supply Chain Cycle Time, among others, to measure the level of achievement for established objectives. 


However, it is important to recognize that implementing this measurement system can be more complex than initially anticipated. It goes way beyond developing a weekly or monthly dashboard. Rather, it involves bringing measurement closer to real-time and proactively driving actions that impact business performance. As previously highlighted, the data used for calculating and summarizing KPIs are often intricate, relying on information from numerous sources that do not normally talk to each other. Therefore, a well-orchestrated data management approach is essential in facilitating the automated calculation and aggregation of such KPIs across the organization. 

As you will discover in later parts of this series, it is critical that performance calculations utilize trusted data that represents the single source of the truth and, whenever possible, not created in a manual process that is subject to individual bias. The enables autonomous calculation and aggregation of data into a higher-level enterprise data structure, a Unified Namespace.

## Addressing the Challenges of Plant-Floor Data

As already ascertained, plant-floor data can potentially drive significant business outcomes, but it often presents challenges due to its raw and unstructured nature. Originally designed for process control, this data can appear in various formats, such as machine, transactional, or time series data, and it lacks context and standardization. Simply, plant-floor data is not immediately compatible with cloud-based enterprise applications. As a result, managing the integration of this complex data to derive meaningful insights from it can be daunting. It requires a data management approach that enforces standardization and repeatability across a manufacturing enterprise.

In addition, data quality remains the foremost challenge for manufacturing companies’ analytics initiatives, with teams often dedicating a significant portion of their time to data preparation and cleaning. Common data quality emanating from manufacturing operations includes issues such as missing or incorrect data, inconsistent data, unsuitable formats, duplicated data, etc. These can be addressed by implementing standardized governance processes.

In addition, legacy industrial systems pose several challenges for smart manufacturing analytics due to their outdated technologies, lack of connectivity, and resistance to change. Some of the key challenges include:

**Integration difficulties:** Integrating legacy systems with modern smart manufacturing technologies can be complex, time-consuming, and costly. These systems often lack standardized interfaces and use proprietary communication protocols, which is challenging when integrating newer technologies.

**Data access and compatibility:** Legacy systems often have limited data storage and retrieval capabilities. Extracting, processing, and integrating data from these systems into smart manufacturing platforms may require extensive data manipulation and transformation, which can be time-consuming and error-prone.

**Resistance to change:** Organizations that rely on legacy systems may face resistance from employees accustomed to using the old systems. This can slow down the adoption of smart manufacturing technologies and hinder overall progress.

A well-defined data management strategy can help alleviate these challenges by developing a systematic approach to integrating data from legacy systems with newer smart manufacturing technologies. This may involve using middleware, data connectors, or custom-built APIs to bridge the communication gap between systems and enable seamless data exchange. Further, data standardization and transformation can be enforced by establishing standardized data formats and structures to facilitate smooth data exchange and processing across different systems. 

By implementing a comprehensive data management strategy, organizations can overcome many of the challenges of legacy industrial systems and create a strong foundation for the successful adoption of smart manufacturing technologies.

## Leveraging Semantic Data Representation for Interoperability

Implementing capabilities to support a manufacturing company throughout its production cycle is crucial. This includes determining what products to create, the required materials, the production location, specific operations and equipment, and the quality-critical process parameters. Additionally, analyzing past performance is essential for continuous improvement.

It is vital to establish a robust data infrastructure, including accurate models for materials and product families and quality and production-specific specifications to achieve this. Furthermore, it is necessary to consider the production units themselves, ensuring a high degree of repeatability by defining machinery categories or types and setting up instances of those types. Connecting the production unit’s performance specifications with material definitions and quality, food safety, or other compliance-related factors is also important.

Having a strong data model is the foundation for building various tools and systems around it. If the data infrastructure is weak, developers might have to compensate for the shortcomings with additional code in the automation or reporting layers. However, if the data model surrounding materials, processes, production units, and personnel is well-structured, creating custom user experiences or reports becomes significantly easier due to the presence of logical structures. In summary, investing in a solid data model is essential for streamlining operations and supporting growth in the manufacturing industry.

## Enabling Seamless Exchange of Data Across the Enterprise

At the heart of smart manufacturing lies data transfer between data producers and consumers for performance analysis and occasionally back to the producers for corrective measures. Smart manufacturing inherently demands integrating data from a diverse range of enterprise components, vendors, and domains in an easily manageable manner. As a result, the effectiveness of a smart manufacturing initiative is directly linked to the openness, flexibility, and scalability of your data exchange architecture.

You can significantly enhance your smart manufacturing initiative by carefully crafting your data management strategy, particularly regarding data exchange capabilities. This will establish a strong foundation upon which you can continuously add and interchange components without constraints. In subsequent sections of this series, we will explore how the publish-subscribe architectural pattern for data exchange, in which data consumers are decoupled from data producers, simplifies data integration for smart manufacturing.

In addition, a key challenge in data integration is the disparity in data exchange methods between the Information Technology (IT) and Operations Technology (OT) domains. Messaging protocols and message formats vary between these domains. Therefore, a well-orchestrated data management strategy will enable you to choose a data transportation and messaging format that effectively spans both domains.

## Assessing Digital Capability and Maturity Assessment

Before developing a comprehensive data management strategy for smart manufacturing, it is essential to assess your present digital capability and maturity level—which may vary significantly across sites and business functions—and your desired future state. Digital capability refers to the extent of digital technology available within your company, while digital maturity represents your organization’s readiness to utilize these technologies effectively. 

It is self-evident that embarking on a smart manufacturing journey demands a growing level of digital capability and maturity throughout the organization. Therefore, comprehending digital capability and maturity is vital as your company decides on the next steps for laying a data management foundation for smart manufacturing implementation. 

When assessing your manufacturing enterprise’s digital capability and maturity, it is crucial to examine various factors, comparing the current state to the ideal target state. These factors and guidelines can help manufacturing companies understand their current position and work towards enhancing their digital capabilities.

For example, you can start by assessing whether data is being collected, stored, and shared effectively across the organization. Your target would be recording and historizing data from all devices and establishing common data definitions across plants. You can also evaluate how easily real-time and historical data can be accessed and ensure its accuracy. Aim for easy access to accurate data, minimizing manual collection or manipulation.

## Conclusion

This article explored the importance of effective data management in achieving business objectives through smart manufacturing. We delved into how it tackles the issue of diverse plant-floor data, promotes interoperability through semantic representation, and enables smooth data exchange across the entire enterprise. We also discussed methods for assessing your digital capabilities and maturity to formulate a strategic data management plan.

Check out Part 2 on [Identifying, Acquiring and Integrating Plant-Floor Data for Smart Manufacturing](https://www.hivemq.com/article/identify-acquire-integrate-plant-floor-data-smart-manufacturing/), where we take a practical approach to help you begin implementing data management for smart manufacturing. We’ll guide you in identifying data sources and walk you through the process of acquiring and aggregating data from your manufacturing operations to facilitate the implementation of smart manufacturing.

### About the Author
[Kudzai Manditereza](https://www.linkedin.com/in/kudzaimanditereza) is a Developer Advocate at [HiveMQ](https://www.hivemq.com/) and the Founder of Industry40.tv. He is the host of an IIoT Podcast and is involved in Industry4.0 research and educational efforts.