7 essential steps for a successful end-to-end path to Manufacturing Analytics implementation

Manufacturing analytics is a powerful data-driven methodology that can help you uncover the cost drivers in your operations. In one of our previous blogs, you can read more about how to uncover these cost drivers with the help of manufacturing analytics. However, practice tells us that a staggering 87% of analytics projects in the industry fail to deliver the expected value to the business. They end up in pilot purgatory.

One of the primary reasons manufacturing analytics projects fail is the misconception that simply investing money or implementing technology will guarantee success. This approach is not sufficient to achieve the desired results. The other key player is data, which is a double-edged sword – it’s what makes all these analytics and capabilities possible, but most organizations are highly siloed, plagued by insufficient collaboration and ineffective communication. A structured approach to your analytics transformation can help mitigate this risk (figure 1).

Becoming successful with manufacturing analytics starts at the top. Senior executives must be aligned with the overarching business problem to be solved and committed to investing both their own time and that of their teams in executing the analytics roadmap. This typically begins with an ideation phase to create the future vision and identify high-value use cases. Your use cases should be clearly defined. It must have a specific objective and provide clear value. Without a well-defined use case for manufacturing analytics, your efforts may lack direction and focus, potentially leading to ineffective resource allocation

Furthermore, make sure your data infrastructure can support the analytics transformation. You need to have sufficient data available, sourced from platforms such as your ERP system and production records. But quantity alone is not enough; data quality is just as critical. Remember the adage: garbage in, garbage out. If your data is unreliable or incomplete, your analysis results will be compromised. Take the time to verify the quality and completeness of your data before you begin your analytics journey.

A third dimension to consider in your manufacturing analytics journey is the operating model. Project teams will most likely be cross-functional, consisting of both domain expertise (e.g., subject matter experts) and methodology expertise (e.g., data scientists, data engineers). You’ll need to consider the optimal way to work (e.g., agile methodology), the analytics approach (e.g., root cause analysis, machine learning, etc.), and the technology stack for developing and deploying the solution.

Based on the success criteria discussed in the previous section, what would a typical process look like for applying analytics in a manufacturing environment?

1. Understanding challenges and pain points: Start by identifying the challenges and pain points in your operations. These will serve as the driving force for your analysis efforts and guide you to areas ripe for improvement.
2. Identify a high-value use case: With a clear idea of your challenges, define a specific objective with evident added value. This use case should:
   • i. address a pressing need within your organization,
   • ii. have the potential to produce tangible results, and
   • iii. be feasible to implement (e.g the required data is available)
3. Gather end-user requirements: gather overview of end user needs through interviews and day-to-day work shadowing to arrive at user journeys and impactful visualization elements.
4. Understanding potential cost drivers: Dive deep into your operations to identify all the potential, actionable cost drivers you can control. Whether it’s labor costs, raw material usage, equipment efficiency, or energy consumption, understanding these drivers is essential to gaining insight and driving cost reduction.
5. Collect relevant data: Once you’ve identified your use case and potential cost drivers, it’s time to gather the relevant data. This can include data from your ERP system, MES data, sensor data, production records, and more. The key is to ensure sufficient data availability – and that the data is of adequate quality.
6. Model and quantify cost drivers: With the data at your fingertips, you can begin to model and quantify the impact of your cost drivers on total costs. It is critical to make this a collaborative, cross-functional effort between subject matter experts (e.g., process engineers, operations managers) and analytics specialists. This is where the magic happens, as you gain insight into how changes to these drivers can lead to cost savings and efficiencies.
7. Take action and drive change: Armed with the insights from your analysis, it’s time to take action. Whether it’s optimizing processes, reallocating resources, or integrating new technologies, you can use your newfound knowledge to drive meaningful change and reduce costs throughout your operations.