3 Important Applications of Advanced Analytics
Nick Ostdick - February 02, 2017
With all the talk around advanced analytics, it’s easy to see the concept as something of a buzzword rather than a key driver in pushing today’s automotive supply chain into tomorrow and beyond. This can be attributed to so many players across the global automotive industry failing to understand the wide-ranging applications of advanced analytics and the benefits thereof.
Understanding advanced analytics as the ability to glimpse into the future to cut the complexities of the global supply chain is key to unlocking the value of this reporting model. Operating in two distinct categories - predictive (risk management, planning) and prescriptive analytics (transportation routes, inventory management) - advanced analytics removes much of the guesswork that was at one time associated with making informed decisions about demand planning and production programs.
With this fundamental understanding in-hand, it becomes easier for planners and managers to realize the benefits of advanced analytics and the ways in which advanced analytics can be deployed for enhanced productivity and efficiency.
With this in mind, here are three of important applications of advanced analytics in today’s global automotive supply chain and the competitive advantages manufacturers can experience from applying advanced analytics in these contexts.
1). Optimized planning capabilities
We discussed before in recent entries how advanced analytics is a key driver in leveraging end-to-end (E2E) visibility and insight into a manufacturer’s overall supply situation. Applying advanced analytics to a demand planning strategy - in conjunction with other intelligent planning solutions like BOM management, Plan for Every Part, and Every Part Every Interval - allows planners and managers to optimize their planning platforms to ensure continuous production devoid of bottlenecks or breakdowns. In addition, advanced analytics gives manufacturers the necessary tools to respond and combat potential breakdowns via detailed ‘what-if’ scenarios and forecasting.
This means reductions in costs associated with weathering stoppages or shortages in production and greater control over the shifting parameters and restraints in planned production programs.
2). Automated decision-making
While some decisions in today’s integrated supply chain still rely on human intervention, more and more decisions can be completed more accurately and with more efficiency via an automation platform. For example, the need to manage inventory levels and replenish certain component parts or manage the movement of containers in a yard are prime candidates for automation through the application of advanced analytics. In addition, job allocation, management of raw materials or resources, and job scheduling on the production floor are contexts in which advanced analytics gives planners more power and control over production programs. The monitoring, data gathering, reporting, and forecasting capabilities with advanced analytics make it more viable to allow for robotic platforms to execute these tasks with greater efficacy compared with human intervention.
Not only does automated decision-making streamline processes and increase productivity, it can also significantly reduce costs and increase ROI (return on investment), which is critical for manufacturers to remain competitive in a growing marketplace.
3). Support of the Agile-Lean model
For years now supply chain planners and managers have worked to create a supply model capable of responding to complex and often unforeseen variables in the procurement and manufacturing stages to ensure continued production and on-time delivery. This push for an agile supply stream, while critical, is simply not enough in today’s global supply network where the need to reduce or even eliminate redundancies is crucial for growth and productivity. Enter the idea of the lean supply chain where processes are streamlined and operational platforms are reimagined to encourage a quicker, more efficient movement of products across the value chain. The melding of these two supply management models has emerged in recent years as an important platform for OEMs with multiple production facilities across the globe.
The agile-lean hybrid model is an ideal application for advanced analytics with its capacity for forecasting, reporting, and process enhancement throughout the supply stream. Advanced analytics bring the predictive functionality necessary for supply chain agility, while also supporting process simplification.
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