The Path to Predictive Quality

How data-based quality optimization in production can directly improve a company’s performance

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Reducing Costs with Predictive Quality

Poor quality alone accounts for 10-15% of production costs (source PWC study). Poor quality leads to delays in the process, to losses in production capacity, increased expenditure for testing, a reduction in the quality classification of the products and, as a result, to return costs, reputational damage, lost customers and other downstream costs.

We help you to break down the complex interfaces in production on the basis of data and thus to stabilize quality and processes. Predictive Quality allows you to identify problems before they arise. This saves costs and increases productivity.

„Quality is when the customer comes back, not the goods“

Hermann Tietz, German entrepreneur.

Objectives
Causes of the Quality Problems
Approaching the Problem

Put out the fire before it spreads

Deviations in quality are often noticed late or not at all. Through predictive analyzes quality patterns are recognized in the data so that critical events can be identified in advance. With our domain expertise, we provide you with specific support to react at an early stage or to inform and adapt the following processes and supply chains in good time.

High degree of complexity meets the human factor

In the chemical industry in particular, demand for specialty products is increasing. The pressure to innovate increases production complexity and thus the potential for quality problems. In addition, reliance on the human factor and a low level of automation often lead to deviations. On top of that, 20% of the workforce will retire in the next 5 years and their knowledge will retire along with them.

Unlocking people and data from the silos

To be able to reduce costs through predictive measures, these measures must first be recognized and revealed. New insights about quality parameters can be gained by linking quality-relevant data from different sources, including the process control system, laboratory information and management system, from the ERP system and from sensors and actuators. We help you to apply this knowledge appropriately.

View our webinar and get all the details on predictive quality

Stream our recorded webinar to learn more about predictive quality.

In this webinar, you will learn:

  • Identifying the cost drivers
  • Using predictive quality to prevent quality issues before they occur
  • Implementing the use of predictive quality throughout your organization

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BCAP: The comprehensive digitalization solution for data-driven quality optimization

Using our IoT solution Bilfinger Connected Asset Performance (BCAP) as a basis, data can be combined in a targeted manner and enable quality-oriented process optimization and predictive analytics.

In addition to the technology and domain expertise applied, the key factor in digital innovations is proactive change management. A new solution must ensure that all stakeholders are on board. BCAP offers role-based solutions for all relevant levels.

Imagination for the Management

  • Deliver objective data on quality parameters and reveal potentials
  • Uncover impact on contribution margin
  • Real-time access to neutral information

Empower the Process Engineer

  • Find causes and possible solutions independently
  • Share findings
  • Reduce deviations that arise from shift to shift

Build for the Operator

  • Allow for data-driven decisions
  • Provide specific recommendations
  • Solve problems quickly and correctly

BCAP Applications for greater quality assurance

  • Virtual Sensor

    The Virtual Sensor delivers cost-effective quality information when there are no physical sensors available. Quality-relevant production properties such as the residual moisture of a belt filter or the degree of grinding of a mill, which are otherwise only measured manually at irregular intervals in the laboratory, can be calculated every minute from the data of a process control system with the help of data science. This real-time information provides quality stability and thus also stabilizes the production process. Waste can be reduced, raw material and energy can be saved and time-consuming manual analyses can be reduced.

    Virtual Sensor

Business Cases