Six Keys to Link STARC to Big Data

Pursuing data has always been part of the manufacturing process. Remedially it all began with part counts, takt time, and parts shipped. Nowadays, the quest for interpretable, actionable data is on uber speed in every manufacturing plant across the globe. Knowing that data for the sake of collecting data can be non-value added, a new acronym has entered the landscape – STARC meaning Sensors, Thermal Control, Actuators, Robotics, and Computations. Manufacturing enterprise systems have made relevant data readily available using prominent displays and triggers to alert non-harmonious functionality.

Six keys for linking the STARC measurements to systemic data analytics within almost any advanced technology implementation include the following:

  1. The network system – wired or wireless data-collection networks acquire precision data from measurement and error-proofing devices.
  2. Capture the data – automatically or manually compiling the data. Stopping work to record data manually will significantly influence overall productivity. People may make mistakes that have consequences downstream. While automatically capturing manufacturing data is more efficient, it can build inflexibility into the process.
  3. No one-size-fits-all-automation solutions must be both scalable and secure to enable the system to communicate with additional consumer devices such as smartphones and tablets that can hold the data.
  4. Reports – legible reports of relevant data must include statistical readings that allow engineers to make decisions about the manufacturing process. Strong and robust software is the backbone of advanced manufacturing execution systems.
  5. Ease of use and flexibility – intuitive programming is mandatory from user-friendly software environments. Ease of use software empowers both operators and engineers to calibrate the manufacturing pulse within the designed process.
  6. Baseline comparisons – check multiple inputs against known limits so incorrect data can be promptly identified. Without a check & balance methodology, suspect information increases the risk of a process failure and severe crashes causing expensive repairs.

Critical data must be shareable inside the car factory

IoT-enabled vehicle traceability systems, such as (Esfi) or (Eiphis), track the performance of the processes and status of the produced cars, thereby reducing subjective decision making and reducing manual operations significantly. Digital transformation is now commonplace within the automotive industry. It will rapidly expand to all aspects of the supply chain where tasks need monitoring or parts inspected for compliance. To ensure authentic data analytics, companies will begin to institute blockchain technology protecting critical part traceability data and guard automated systems from malicious conditions that may cause downtime.

Digitalization in automotive manufacturing will only grow in the future. Industry leading companies have made it a priority to adopt blockchain technology for part traceability, build completely automated manufacturing plants and invest in manufacturing execution systems.

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