Leverage manufacturing analytics to optimize processes and increase profitability

Why

Manufacturing analytics is a powerful data-driven methodology that can help you uncover the cost drivers in your operations. In this blog you can learn more about how manufacturing analytics can help you uncover these cost drivers.

In today’s manufacturing environment, challenges like rising operational costs, unpredictable energy prices, and increasing regulatory demands are constant barriers to efficiency. Scaling operations efficiently while maintaining high product quality is becoming harder than ever. At the same time, reducing waste, minimizing downtime, and maximizing the productivity of both labor and machinery remain critical goals. But how do you extract actionable insights from the vast amounts of data your systems generate? You may have the raw data, but do you have the right tools and strategies to leverage it for measurable improvements?

Many manufacturing leaders express a desire to become more data-driven but struggle with knowing where to start and how to continue. Others have invested in analytics technologies but are unsure how to apply the insights effectively to their processes, resulting in missed opportunities for cost reduction and operational improvement. You might also be aiming to future-proof your business and get ready for Industry 4.0 but feel overwhelmed by the complexity of integrating digital technologies into your operations. These challenges are not uncommon. However, understanding and addressing them through a structured, human-centric approach to manufacturing analytics can be the key to unlocking operational excellence.

How

At Bright Cape, we believe that a successful data transformation is more than just implementing new technologies—it’s about embedding data-driven decision-making into the heart of your operations. Success 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, read more on this in the following blog.

Project understanding

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.

Use case prioritization

With a clear idea of your challenges, define a specific objective with evident added value.

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.

Identify potential cost drivers

Dive deep into your operations to identify all the potential, actionable cost drivers you can control. The outcome of this phase is a well-defined suitable target function.

Collect relevant data

Gather 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.

Model cost drivers

With the data at your fingertips, you can begin to model and quantify the impact of your cost drivers on total costs. The goal? To build a predictive model capable of identifying and quantifying the most influential (cost) drivers impacting your financial performance. Read more about cost drivers in this blog.

Refine model

The key is to inject domain knowledge to guide and refine the modeling effort towards actionable parameters. For example, in the Holland Malt use case (Use case 2), the temperature of the kilning process was identified as the top contributing factor to energy consumption.

Integrate and drive change

Integrate analysis insights into operations to drive improvement. Use field tests to validate models, and leverage dashboards for data-driven decisions, making manufacturing analytics a key strategic tool.

Ready to leverage your data to increase profitability and sustainability with manufacturing analytics?

In our new guide, you'll find a practical roadmap for operational cost optimization - a blueprint to help you harness the full potential of manufacturing analytics within your organization. With real-world examples and practical insights, we show you how to drive cost optimization and revolutionize your operations from the ground up.

Get access now

Our Process

From Discovery Workshop to Work Package Rollout

Our approach to manufacturing analytics is grounded in a deep understanding of your specific needs and goals. We don’t believe in one-size-fits-all solutions. Instead, we have several tailored solutions:

Discovery workshop

In this workshop, our experts will collaborate with your operations and data teams to gain an in-depth understanding of your business processes, challenges, and opportunities. This is where we identify the most pressing pain points and high-value use cases that can deliver immediate results. By focusing on your unique operational landscape, we ensure that the solutions we develop are directly aligned with your business objectives.

Discovery workshop

One-on-one consulting

Discuss your business case & next steps in a one-on-one consultation with our experts to discuss your ideas, challenges, and goals. Drawing on our wealth of experience and expertise, we’ll help you refine your vision and chart a course for success.

Contact us

Case studies

Optimize outbound supply chain design and processes to reduce transportation costs

Curium is a global supplier of medical products. They specialize in the production and distribution of radioactive tracers used in nuclear medicine for diagnosis and treatment of various diseases. Curium was struggling with rising transportation costs due to fuel prices and staff shortages, while maintaining service levels. Together with Bright Cape, a data-driven approach was implemented, using a digital twin to reduce transportation costs, improve efficiency and reduce CO2 emissions.

Potential reduction of energy consumption based on data-driven predictions

Vereijken - a leading Dutch greenhouse horticulture player - was looking for a solution to reduce energy costs to heat and light their greenhouses. They faced challenges in balancing gas and electricity consumption costs with revenue from surplus electricity. Currently, decision-making is time-consuming and manual, lacking assurance of optimality.

Creating a predictive framework for solar energy production sites

Covolt is a renewable energy company, where they deployed their intelligent energy management system. They had limited historical data and no data for new sites, making accurate forecasting of energy production difficult. Bright Cape developed machine learning models and a robust data infrastructure, cutting prediction errors in half and making energy trading more effective, leading to higher profit margins and new customers.

Create insight into machine performance and enable fault analysis

Settels Savenije is a group of companies serving an international clientele in various high-tech markets. Settels Savenije's client had problems with unpredictable machine component life. As a solution, large amounts of sensor data were processed and dashboards were developed for performance and failure analysis, making it possible to extend equipment life and predict failures.

Want to learn more about Manufacturing Analytics?

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Ask Ionut, our Data Science team lead!