9 Data Challenges and Solutions Facing the Manufacturing Industry

Manufacturing companies have a lot to manage in the lifecycle of a product. It requires a concerted effort to step back from providing a product and managing a supply chain to assess and solve data challenges that may be preventing future growth and success. The full power of your manufacturing data can easily be lost in the shuffle.

The Impact of Data Challenges on Business Operations

Data challenges can pose real problems for manufacturing operations, leading to decreased productivity, reduced profitability, and missed opportunities related to new innovations. If you don’t take the time to evaluate your data, you may lose your competitive edge to other manufacturing companies that takes meaningful, decisive action on gathered insights. You may also miss issues related to quality control or experience unexpected downtime from an equipment failure that might otherwise have been identified.

The Importance of Data Security in Manufacturing

Data security issues can also expose sensitive information, contribute to downtime, and potentially damage a company’s reputation. To prevent and mitigate risks associated with cyberattacks, manufacturers need to implement strong access controls, regular security audits, employee training on security best practices, data encryption practices, and incident response plans.

Complexities of Data Management in Manufacturing Environments

As manufacturing operations and technologies advance, the amount of data coming in from various sources continues to grow and spread, increasing the chances for data fragmentation and less valuable insights. Here are some key types of data manufacturers can expect to generate and manage.

Types of Data in Manufacturing

• Operational data: Data related to the operations of a manufacturing facility can include energy consumption, facility maintenance logs, and equipment maintenance records. Some equipment may include smart sensors that inform plant managers when routine preventive maintenance is needed. Operational data could also include information like employee timesheets and environmental sensor readings.
• Production data: On the production line, manufacturing companies can generate data about production rates, product attributes, and machine downtime. This information can help prevent bottlenecks or plan production time to meet an important deadline.
• Supply chain data: Any information about suppliers, inventory levels, customer demand, or logistics can be part of supply chain data. This can help manufacturers make better connections between various points of the post-production journey.
• Quality control data: Customer feedback, defect rates in the production process, and inspection results of the plant itself could all be information collected as part of quality control data.

Common Challenges with Data Modernization Processes

During the data modernization process, manufacturing organizations can encounter several different technical and organizational challenges that prevent the optimal use and management of data.

Technical Challenges

• Data integration: It can be difficult to integrate data from diverse sources, especially those that come with varying formats and structures.
• Data volume and velocity: More devices are collecting data than ever, and with real-time data collection becoming more prevalent, the volume and velocity of data can make management much more challenging.
• Data quality and consistency: Data needs to be accurate, consistent, and complete across various sources for the insights to be reliable and useful.
• Data security and privacy: As data is produced, processed, and stored in more places, protecting it from unauthorized access and breaches becomes more complex and critical.
• Regulatory and compliance strategies: Businesses need to be compliant with the latest data privacy regulations, such as GDPR and CCPA. This can be a time-consuming and confusing task.
• Legacy systems and inoperability: For businesses that have been operating for decades, migrating data from legacy systems can lead to issues relating to interoperability.

 Organizational Challenges

• Skill gaps: There is still a general shortage of skilled data professionals. If businesses are unable to hire the right people, this can hinder data modernization efforts.
• Change management: Sometimes, team members can be stuck in their ways and resistant to change, making the adoption of new data modernization efforts harder to achieve.
• Effective adoption by employees and leadership: Leadership and key employees need to understand the benefits of data modernization for the rest of the team to eventually get on board.

Looking Ahead: Technological Solutions and Innovations

Technical advancements will drive more transformations as the manufacturing industry continues to evolve. Organizations that are primed and ready for these solutions can remain efficient, competitive, and cost-effective in the years to come.

Advanced Analytics

Advanced analytics techniques include artificial intelligence and machine learning (AI/ML). Manufacturers who can extract insights and train learning models on vast amounts of data will be able to perform predictive maintenance on equipment, improve quality control processes, conduct more accurate demand forecasting, and optimize manufacturing processes automatically.

IoT and Smart Manufacturing

Internet of Things (IoT) devices and edge computing allow for greater connectivity and insight into daily operations. Environmental sensors and IoT-enabled machinery can shed light on real-time performance and aid in predictive maintenance. Remote monitoring from IoT devices can also offer greater control of equipment to improve efficiencies and reduce downtime, as well as give manufacturers the ability to monitor product quality in real time.

Cloud Computing

Cloud computing provides scalable and flexible computing resources that can enable manufacturers to store and process large data sets that can enable advanced data analytics, including AI/ML and other new technologies. Cloud platforms can also improve collaboration between members and facilitate quick deployment of new applications and services.

Benefits of Choosing a Data Storage-as-a-Service (STaaS) Solution

Data modernization projects, including implementing AI tools, almost inevitably come with the need to store more data, and fast. A storage-as-a-service (STaaS) solution means that manufacturers won’t get throttled by data storage limits. Instead, the service can scale seamlessly with your needs while offering ransomware protection and subscription-based billing.

Protect Your Company’s Data with Ease

If you’re looking for high-availability, high-performance, resilient storage services that help you reach the next level of data management, learn more about TierPoint’s Storage-as-a-Service solution. Future-proof your data infrastructure and remove the complexity associated with data storage management.