Overcoming the Top 8 Obstacles for Data Governance in Manufacturing

Coordination between different systems and team members is essential for the smooth operation of manufacturing processes, including discrete and ETO manufacturing.

Data governance, in particular, plays a vital role in the success of manufacturing operations. Despite its importance, however, a survey from Weforum and Boston Consulting Group shows that a meager one-third of manufacturing executives have successfully scaled data-driven use cases beyond the production of a single product.

Unfortunately, this is a common problem. Manufacturing organizations manage multiple systems, including disjointed legacy systems that don’t share data. Without proper data governance, it’s increasingly difficult to make informed decisions and address modern manufacturing concerns.

Here, we’ll explore the primary data governance challenges that manufacturing companies experience and how integrated Microsoft ERP solutions will provide practical solutions that allow organizations to harness the collaboration and efficiency that enable growth and market advantage.

Note: Even though most customers, commentators, and consultants still call it Dynamics 365 Finance & Operations or D365FO, Microsoft now technically licenses it as Dynamics 365 Finance and Supply Chain Management.

What You Should Know—Data Governance in Manufacturing

First, let’s start with the basics. What is data governance, and how does it affect manufacturing operations?

Data governance refers to the management of data availability, usability, integrity, and security at the enterprise level. For manufacturing, governance is tied to the systematic control of data coming from each internal process, machine, system, or production line.

Effective data governance ensures that data across the organization is consistent, reliable, and actionable. This is particularly important in manufacturing, where data drives everything from supply chain management to quality control to consumer relationships.

By developing stronger data governance practices, manufacturing companies can improve operational efficiency, enhance product quality, and stay more competitive.

What Are the Common Data Challenges That the Manufacturing Industry Faces?

Since operational efficiency depends on data, mitigating data risks is paramount. However, because of the complexities and intricacies of running a manufacturing operation, organizations face a variety of data management challenges.

Here are the eight data obstacles most prevalent for manufacturers:

1) Finding Consistency in Master Data Management (MDM)

MDM involves creating a single and consistent view of key business entities, such as products, suppliers, and customers. Without a unified master data system, producers may encounter discrepancies and errors, affecting everything from inventory management to profit margins to customer service.

Data integration challenges often make it difficult for manufacturers to maintain accurate and up-to-date master data. A master data management system, or MDM, ensures consistency and secure data distribution across an organization in a controlled manner, which is critical for manufacturing businesses. MDM can be managed within ERP and PLM systems or by using an external system, depending on the needs of the organization.

2) Reducing Errors with High Data Quality

Data quality is another major challenge in the manufacturing industry. It can be affected by various factors, including human error, outdated information, and inconsistent data formats. However, ensuring data quality requires continuous monitoring and cleaning, which often takes up substantial time and resources.

Because high-quality data is essential for optimizing manufacturing processes, companies need to invest in automated data quality tools and establish strict data entry protocols.

3) Validating and Coordinating Data

Data validation ensures that the data used in decision-making processes is accurate and reliable. In manufacturing, validation issues can compound when there’s a lack of synchronization between different departments and systems.

Validation checks are necessary to prevent data inaccuracies from spreading throughout the organization, causing flawed analyses and delays. This is particularly true for global enterprise manufacturers that must coordinate between departments, teams, and unique geographic regions.

4) Moving Data from Outdated Legacy Systems

Often, manufacturing companies rely on older, legacy systems that were not originally designed to handle the volume and complexity of modern operations.

Moving from these outdated systems to contemporary platforms can be daunting (and expensive). To overcome this challenge, manufacturing companies can adopt phased migration strategies and use data integration tools for seamless transfer between systems.

5) Reducing Data Silos for Efficiency

Data silos occur when data is isolated within specific departments or systems, preventing it from being shared across the organization. Manufacturing organizations encounter siloed data when they bank multiple systems, such as a Warehouse Management system, a PLM system, and an ERP.

Fragmented data hinders collaboration and stops the flow of information. As a result, manufacturers might struggle with supply chain management, production planning, and customer service.

Creating a culture of data transparency and implementing an integrated, centralized business management solution is the first step toward breaking down data silos.

6) Handling Massive Data Volume

Manufacturers generate copious amounts of data from sensors, machines, and processes. Additionally, data often arrives from suppliers, third-party partners, inventory specialists, and customers.

Analyzing, classifying, and managing this data can be challenging and time-consuming.

Companies need scalable data management solutions that can handle large datasets and provide real-time insights. Cloud-based platforms and big data technologies offer viable options for managing and analyzing large volumes of data efficiently.

7) Ensuring Industry Compliance

Compliance with industry regulations and standards is a top priority for manufacturing companies that need to abide by strict data privacy and security requirements.

Regulations, however, can be complex and dynamic, which makes maintaining compliance more challenging. In addition to hefty fines, non-compliance can result in legal issues and reputational damage.

Implementing robust data governance frameworks that include compliance monitoring and reporting capabilities helps manufacturers stay compliant and avoid regulatory pitfalls.

8) Using Predictive Maintenance for Business Growth

Predictive maintenance uses data analytics and machine learning to anticipate equipment failures before they occur. A Product Lifecycle Management System (PLM) and an Enterprise Resource Planning system (ERP) are two of the main systems used for predictive manufacturing data.

Predictive maintenance ties in very closely with analytics and business intelligence. In addition to reducing costs, it can help predict business challenges that may otherwise stunt revenue growth and productive goals.

While a predictive approach can significantly reduce downtime and maintenance costs, it presents several big challenges for manufacturers. Predictive maintenance requires large amounts of high-quality data, sophisticated analytics capabilities, and seamless integration with existing maintenance systems.

To overcome these challenges, companies must invest in advanced data analytics tools and foster data-driven decision-making from the top down.

How Does the PLM to ERP Journey Improve Data Governance?

For manufacturing companies seeking to streamline operations and improve data governance, transitioning from Product Lifecycle Management (PLM) to Enterprise Resource Planning (ERP) systems is a common and effective path.

PLM focuses on managing the lifecycle of a product from inception to disposal, while ERP integrates various business processes more centrally. The journey from PLM to ERP involves several stages, including data migration, system integration, and process optimization.

To ensure that the new system meets an organization’s needs and objectives, it’s necessary to plan strategically, engage stakeholders, and monitor progress continually.

A Closer Look at the Process:

Conceptualizing with Research and Development

The Research and Development team may conceptualize new products, using the PLM system to manage data and iterate before this data can move to the ERP. Then, the R&D team collaborates with other parties in the organization to fine-tune information on materials, design specifications, and performance goals. This process might include collaboration from teams like Sales and Production.

Integrating with Design and Engineering

The design phase employs Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) tools, with the PLM system facilitating communication across teams. Once the design is finalized, data moves into the ERP system.

Integrating ERP and Data Flow

If there’s ERP to PLM integration, along with integration to other essential systems, then data flow is seamless and error-free. An integration solution can help you connect your ERP, PLM, and other systems and ensure that you have a holistic view of business performance and details.

Seamless data flow between integrated PLM, ERP, and other systems prevents manual errors, providing an integrated view for Product or Supply Chain Managers. The ERP is designed to handle production, sales, and finance, so it is imperative that the data it contains is accurate, complete, valid, and secure.

Enacting Data Governance

Last but not least, it’s important to implement data governance in both PLM and ERP systems. Doing so ensures clean and accurate data that’s free of duplicates or errors. Consider using data governance tools for each system or an external master data management system despite the additional management required.

Benefits of Moving to an Integrated ERP System

For foolproof manufacturing operations, the data journey from Product Lifecycle Management (PLM) to Enterprise Resource Planning (ERP) systems is critical. Here’s what teams can experience when transforming their data governance strategy with a modern, adapted ERP for Manufacturing:

• Data Consistency: Data between PLM and ERP systems opens the door for accurate and efficient operations
• Integrated Solutions: Integrations facilitate smooth data transfer between PLM and ERP systems, preventing silos and cumbersome errors
• Continuous Monitoring: Regular data flow between PLM and ERP systems identifies and addresses issues before they swell to larger problems

Data Governance at Work in Real-World Manufacturing

Suppose a Research and Development team is based in Europe, with production facilities in India and Japan. The R&D team members need to provide alternate materials to meet market demands, so they input these conditions into the company’s Microsoft Dynamics ERP.

Local material suggestions require approval from the Engineering Manager and Supply Chain Manager before reaching the Production Manager. A data entry workflow solution simplifies and automates the entire process, ensuring a smooth transition from conceptualization to production.

The ERP’s varied fields and naming structures benefit from a data entry workflow that assigns data entry tasks and approvals to responsible data owners. Data quality rules ensure accuracy and consistency. Once approved, the data is securely distributed.

Master data, managed centrally from Europe, is distributed to local teams who can edit specific fields, triggering approval workflows when changes are made. This ERP-powered process is transparent, effective, and designed to ensure production success.

Take the Next Steps Toward Data Accuracy and Efficiency

At Encore, we’re a team of seasoned manufacturing technology and operations specialists. In helping hundreds of organizations successfully streamline processes and improve production efficiencies through modern cloud solutions, we recommend STAEDEAN’s Master Data Management suite, built for Microsoft Dynamics 365 Finance & Operations.

This integrated ERP and data solution approach allows manufacturing companies to add and write data quality rules and workflows, manage master data distribution, and help manufacturing teams mitigate common bottlenecks. By adopting this strategy, you can strengthen collaboration, minimize security risks, and take back control of your data governance processes.

Of course, we also understand there is no one-size-fits-all approach. Your ERP and data configuration needs to be adapted and tailored to your specific processes, needs, and industry. That’s why, to get started, we can consult with you on how to effectively employ a tailored solution that will better manage your organization’s data workflows, data quality, integration, and security and compliance.

Advance your Data Goals Today

Data governance is a crucial aspect of modern manufacturing, enabling companies to improve efficiency, decision-making, and compliance.

At Encore, we offer tailored solutions and expert guidance to help you overcome your data governance hurdles and derive your company’s data from a clear, single source of truth.

Ready to transform your data governance practices and learn more about Microsoft Dynamics 365? Contact us to speak with a manufacturing expert today to learn about your options.