How Advanced Analytics Can (Or Is) Replacing Human Intervention in The Automotive Supply Chain

Posted by Nick Ostdick on Jan 19, 2017 9:00:00 AM


How Advanced Analytics is Replacing Human Intervention.jpg“Is a machine going to take my job?”

In today’s increasingly digital, automated world, it’s a sentiment you hear uttered throughout a number of global industries. While this is perhaps less the case in the automotive industry where robotics and automation dominate the manufacturing stage, it’s a question many planners and managers ask when it comes to advanced analytics and its ability to significantly reduce the amount of human intervention necessary across a number of touch points of the demand planning and manufacturing chain.

The trouble with reckoning the capability of advanced analytics versus human intervention is that OEMs and manufacturers across the globe are already integrating various incarnations of analytics in place of humans at various stages in the planning and production process.

The Internet of Things, Big Data, and Industry 4.0 have emerged as critical drivers in the sourcing and interpretation of data and information, so perhaps the question of advanced analytics and human intervention needs to be reframed. Perhaps the real question is: How is advanced analytics supplementing human intervention to create a more lean, efficient, and streamline automotive supply chain?

While this version of the question assumes automotive companies are moving to integrate advanced analytics as a core driver in their demand planning and production platform — which many in fact are — it provides a window into the many benefits of advanced analytics in helping companies optimize their processes for greater efficiency. Here are just a few ways in which advanced analytics are helping companies function at higher levels by reducing the need for human intervention.

End-to-end (E2E) Visibility

The necessity for E2E visibility for manufacturers to truly understand the nuances of their overall supply situation is well-documented — however, in years past, E2E was difficult to achieve when relying on human interventions such as manual data recording, entry, and reporting. By incorporating advanced analytics into the supply chain, planners and managers can gain real-time insight into current demand planning and production variables to make more informed decisions about how to best allocate materials, resources, and manpower to ensure on-time delivery and customer satisfaction. In addition, advanced analytics allows for a more robust visualization and actualization of critical data as opposed to human or manual intervention, which can often result in long lead times before such data can be optimized to enhance supply chain efficiency.

Advanced analytics also allow organizations to extend E2E visibility upstream to suppliers and downstream to end users. As advanced analytics are shared beyond the boundaries of your organization, the supply chain achieves a new level of E2E visibility.

More Optimized, Automated Decision-making

So many processes in an optimized supply chain rely on a delicate balance of automation and human intervention, both at the conceptual and execution level. Some instances, such as partnering with suppliers or interacting with workers, may in fact benefit from human intervention. However, when it comes to issues such as replenishment or facility or machine allocation, advanced analytics provides planners with the insight required to make the most cost-effective, efficient decisions. For example, advanced analytics make it possible for planners and managers to know well ahead of time when additional inventory may be needed to maintain delivery timetables or combat production bottlenecks due to new constraints in production programs. Advanced analytic capabilities allow planners to then deploy the necessary container management strategies to ensure the right parts are on the production floor at the right time.

A Lean, Streamlined Supply Stream

In recent years, the push for a streamlined supply network based on lean production principles has been the goal for OEMs on a global scale. As OEMs expand with new production facilities in more disparate parts of the world, the need to visualize in real-time the capacity of each facility to handle a variety of production programs with unique constraints is key for enhanced productivity. Advanced analytics and the window it provides into inventory, condition of part and part families, BOM management, and other integrated planning solutions allows planners to execute a lean supply stream based on detailed modeling, forecasting, and reporting.

More Accurate Reporting

Today’s global supply network relies heavily on the accuracy and efficacy of detailed reporting based on advanced metrics and criteria. Without these optimized reporting capabilities, OEMs can find it difficult to create accurate modeling and forecasting to ensure more effective demand capacity planning and production programs — essentially, it’s difficult to see where you’re going if you can’t understand where you’ve been. Through greater production control across a wide supply stream and enhanced capabilities to gather, sort, and collaborate on complex data sets, advanced analytics allows planners and managers to generate more accurate, detailed reports that foster more effective, precise modeling in future planning.

Cost Reduction

At the end of the day, cost reduction is the name of the game for OEMs and manufacturers at each touch point of the value chain. Because advanced analytics directly improve demand capacity management by allowing real-time insights into the demand and capacity, OEMs are more likely to experience significant cost reductions in sourcing resources and materials, production programs, and even in transportation management and the movement of parts from the production floor to the customer’s door. In addition, the predictive insights gained from advanced analytics account more consistent forecasting and ‘what-if’ scenarios, which companies rely to manage planning and production strategy.

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Topics: Advanced Analytics