How Robots and Data-Driven Insights Optimize Automotive Production

When Ford Motor Company rolled out the first large-scale moving assembly line in 1913, its impact was immediate. Productions times on the company’s Model T dropped from half a day to just 93 minutes. Ford’s production rate the following year – 308,162 vehicles – zoomed past all other automobile manufacturers combined. And Ford could offer its vehicles to consumers at a lower price point, all thanks to the assembly line.

Today, the Michigan-based company produces more than 6.4 million vehicles worldwide annually, focused on ensuring flawless production of its vehicles in a high quality, efficient, safe and sustainable manner.

But what if we showed you how, when one assembly plant wasn’t achieving required production rates, Ford turned to Jacobs for data-driven insights, robotic simulations and solutions aimed at increasing capability and uptime and maximizing value across some major body assembly components?

When production lines at one of Ford’s tier one suppliers that produce three major body assembly components supplying one of company’s assembly plants weren’t achieving the required production rates, Ford engaged Jacobs to evaluate current system capability and recommend corrective actions.

Taking a holistic approach to assessing the needs, we began by evaluating tooling capabilities, including production, maintenance and controls systems to identify all potential areas of impact to production throughput and equipment downtime. Then, once we’d identified the areas of concern, we measured current equipment and operator cycle times to establish the baseline capability of the equipment. We completed causes of equipment downtime and a complete equipment health assessment to determine root causes of lost production.

Based on the assessment, we developed and proposed detailed corrective actions, along with an estimate of the impact on the corrective actions to throughput. Once solutions were implemented, we could validate the impact of all improvements using detailed robotic simulations, which have zero risk of interrupting production or causing downtime since they are completed offline. Additionally, they allow issues and solutions to be visually communicated to the entire team prior to implementation. We also analyzed preventive maintenance programs and downtime tracking methods to determine baseline efficiencies and data correlation to lost production.

Although the facility had struggled with throughput issues on these lines for 18 months, it only took our team 14 days to assess the systems and develop solutions and implementation plans. Our team proposed several courses of action to enable the facility to continue to improve and deliver on Ford’s mission to “go further.”

The increase in throughput will reduce the need for additional shifts and overtime and will allow downtime required to perform maintenance activities. Once implementation of corrective actions is complete, the facility will realize the following jobs-per-hour (JPH) throughput improvement for the following three systems:

• Line A = 64JPH (19% improvement)
• Line B= 65.5JPH (21% improvement)
• Line C = 65.5JPH (7% improvement)