Data Strategy with AI in the Manufacturing Sector

Artificial intelligence (AI) emerges as a technology with the potential to revolutionize the manufacturing sector. However, embarking on an AI journey can be daunting for many manufacturers. To fully leverage the opportunities AI offers, it's crucial for manufacturers to establish a robust data strategy. With 96% of business leaders in the manufacturing sector believing they need to extract more value from their data, now is the time to create a production data strategy that prepares your organization for AI success. Here are four steps to help you do just that.

1. Evaluate Your Current Systems and Operational Processe

Before starting an AI strategy, conduct a comprehensive evaluation of your current systems and operational processes. While you may rely on multiple enterprise resource planning (ERP) systems and other siloed technologies, your new production data strategy will require centralized data. This creates complexities that hinder consolidation.

Evaluation Process:

• Number of Systems: How many systems do you currently have?
• Data Overlaps: How many of these contain overlapping data? Are there conflicting pieces of information in these overlaps?
• Manual Methods: Is anyone in your organization using spreadsheets or other manual methods to store information?
• Information Access: How many different places do your teams need to look to find answers to simple questions?

Regularly review each system. Identify which data you can easily access and which are challenging to find. Consult your frontline services and sales representatives to understand the obstacles they face daily. Learn how much time they spend navigating multiple systems to find the information they need. Outline what's working well and document areas or processes that may require improvements. This can form the foundation of your production data strategy.

2. Integrate Your Systems to Improve Data Quality

After assessing the current state, the next step for your production data strategy is considering how to bring all your systems together. Integrating your technology offers many advantages, allowing you to maintain competitiveness by enabling more efficient operations and effective forecasting. All of this is made possible by consolidating your data (customer, product, asset, channel, and back-office).

Integration Process:

• Use of APIs: For companies with data complexity, a solution is to integrate these systems by adding APIs to each application, creating an information network. This network enables businesses to unlock data from every application, device, and asset they have.
• Central Data Repository: Integrated systems provide better data access and analysis by creating a central data repository.

This information can help you proactively understand how long a customer has been using a specific asset and automate alerts for necessary maintenance or provide discounts for nearing end-of-life assets replacement. Such interactions can earn high scores in customer satisfaction and increase loyalty.

3. Ensure Your Data Is Clean by Validating It

Bringing data together from different systems may reveal duplicate or conflicting information. Therefore, it's crucial to ensure that the data you have is clean. Take necessary steps to remove duplicate or irrelevant information, clean data, correct structural errors, identify unwanted outliers, and input missing values.

Data Cleansing Process:

• Removal of Duplicate Data: Ensure data consistency by identifying and removing overlapping or duplicate data.
• Data Validation: Ensure data accuracy, currency, and completeness.
• Quality Control: Regularly analyze your data to correct errors and inconsistencies.
• Clean data will enhance your organization's decision-making capabilities. You can better identify trends and patterns that you can use to improve your products, services, and operational processes with a more comprehensive view of the customer journey.

4. Start Small and Experiment with Tested Applications

Once your systems are integrated and your data is cleaned, your organization will be ready to test AI models. AI in manufacturing offers a wide range of opportunities to improve various tasks or processes.

Initial Steps:

• Small Trials: Test AI applications in small areas, such as using automation workflows or chatbots.
• Pilot Projects: Start with small-scale pilot projects to test the integration of AI into your business processes.
• Feedback and Adjustments: Evaluate feedback from initial trials to optimize systems and processes.

AI and Innovation

• Encouraging Innovation: AI fosters innovation in the manufacturing sector, offering significant advantages in new product development, process improvement, and rapid response to customer demands.

• New Product Development with AI: AI plays a significant role in the development and launch of new products. Product design and prototyping processes can be accelerated with AI.

Smart predictive models can review your data and uncover insights across the entire production value chain much faster and more efficiently than manual data review. This information can help provide pricing and discount recommendations, review sales contracts and compliance, increase awareness of product warranty usage, and proactively reach out to customers.

AI presents significant opportunities in the manufacturing sector, but establishing a solid data strategy is necessary to capitalize on these opportunities. By evaluating your current systems and operational processes, integrating and improving the quality of your data, and starting with small experiments, you can maximize the potential offered by AI. Following these four steps can optimize your production processes, increase efficiency, and gain a competitive advantage. A data strategy developed with AI in the manufacturing sector will play a crucial role in the future success of your business.