Manufacturing Data Strategy

AI has taken center stage, but the question on many manufacturers’ minds is “how do I get started with generative AI?” While many manufacturers have taken advantage of predictive AI when it comes to planning and supply chain, forecasting, wallet share, and market share, generative AI presents new opportunities and challenges. To take advantage of all the promise of AI, it’s critical for manufacturers to lay a solid data strategy foundation.

With 96% of manufacturing business leaders feeling like they should be getting more value out of their data, this is the right time to create a manufacturing data strategy to set up your organization for AI success — here are four steps to help you do that.

While you might typically rely on multiple enterprise resource planning (ERP) systems and other siloed Technology, your new manufacturing data strategy will require centralized data. This creates complexities that make unification a challenge.

Start by reviewing how many systems you currently have in place.

• How many of them contain data that overlaps?
• Do the overlaps have conflicting information?
• Does anyone in your organization use spreadsheets or other manual methods to store information?
• How many different places do your teams have to look at to find answers to simple questions?

Review each system you use regularly. See what data you can easily access, and determine what data is difficult to locate. Meet with your frontline service and sales reps to get an idea of what barriers they encounter on a day-to-day basis. Find out how much time they spend navigating multiple systems to locate the information they need.

Outline what works well and document areas or processes that could use improvements. This can serve as the foundation for your manufacturing data strategy.

Now that you’ve assessed what you currently have in place, the next step to consider for your manufacturing data strategy is how you bring all of your systems together. Integrating your Technology offers many benefits that give you the ability to operate more efficiently and forecast more effectively to remain competitive. This is all thanks to bringing your data — customer, product, asset, channel, and back office — together. Doing so gives you a better understanding of how your organization runs and provides insightful information to use in decision-making.

Bringing together systems like databases, ERP systems, and custom applications can be overwhelming, however. Organizations use an average of 976 applications , yet only 28% of them are integrated. Smaller organizations may choose point-to-point integration but such solutions only work for those that don’t use multiple systems.

For companies with data complexity, the solution is to integrate these systems by attaching APIs to each application, allowing them to talk to one another and create a network of information. This network allows businesses to unlock data from each of their applications, data, devices, and assets. This information can help you proactively understand how long a customer has been using a particular asset, and you can automate alerts for necessary maintenance, or provide discounts to replace assets approaching end-of-life. Interactions like these can help you earn high marks in customer satisfaction and foster loyalty.

Many times, however, bringing data together from disparate systems can surface duplicate or conflicting information, so it’s necessary to make sure the data you do have is clean. Take steps to clean data by removing duplicate or irrelevant information, fix structural errors, find unwanted outliers, and input missing values.

Clean data will also help improve your organization’s decision-making abilities. With a more complete view of the customer journey, you can better identify trends and patterns that you can use to improve your products, services, and operational processes.

Now that you have analyzed processes, integrated your systems, and cleaned your data, you’ll have the highest quality information to inform your manufacturing data strategy and, ultimately, increase overall productivity . For example, when customers call in with questions, service agents will no longer need to toggle between multiple screens and applications to find answers, leading to shorter calls to resolve issues. These shorter calls translate to increased productivity for service agents, who can use those time savings to assist more customers. Efficiencies then cascade into cost savings by eliminating redundancies and streamlining operations.

Once your systems are integrated and your data is clean, your organization will be ready to begin testing AI models.

AI in manufacturing encompasses a broad range of tasks or processes that can augment the business in different ways such as using generative AI to compose meeting summaries or personalized emails to your customers. You might be eager to set high goals for your manufacturing data strategy and expect dramatic business outcomes from AI, but start your experiment in one or two small areas first to see what shifts you may need to make around your organizational processes or workforce.

Your first step may involve introducing automation workflows that can free up your teams to focus on more important tasks. Or you might look to chatbots to help your customers and partners find answers easily.

Intelligent predictive models can sift through your data and surface insights across the entire manufacturing value chain much faster and more efficiently than manually reviewing data. These insights can help provide pricing and rebate recommendations, review Sales Cloud agreements and compliance, raise awareness about product warranty usage, and help you reach out to customers proactively. Once you have the systems and operations in place to organize your data effectively, you’ll have the agility to evolve your business to take advantage of new innovations that will keep you on pace with the future of manufacturing.