Digital transformation key to advancing auto supply chains
Patrick van Hull explores the opportunity for supply chains to turn data into insights that inform decision-making
A universal solution cannot solve all of today’s global supply chain shortages. The circumstances are so complex that even seemingly minor disruptions can create a domino effect of volatility in supply and demand.
Anyone reading the news will see numerous causes of supply chain disturbances, from global politics to unpredictable weather events. Disruption is an all-too-common occurrence, leaving supply chain leaders in a difficult position to understand and overcome the effects on their supply chains.
This problem quickly grew in the automotive industry due to the increased need for semiconductor chips caused by automotive digitisation. Inflation and economic instability also cause volatility in the automotive business, as do growing transportation costs, trade disputes, outmoded production facilities, and environmental and regulatory issues. Furthermore, customer behaviour is evolving with electrification, alternative purchasing channels, and the possibility of subscription-based services.
These difficulties seldom happen in isolation. And given the persistent climate crisis and increased geopolitical tension, the situation will likely worsen before it gets better for the automotive industry. The challenges in the industry have become the ‘new normal’ and will continue for the foreseeable future, meaning that today’s leaders must plan for both the present and the future.
Transform supply chains with new and improved capabilities
In today’s context, it is more challenging than ever for automakers to predict customer demand. As a result, the forecasts they send to their suppliers fluctuate considerably. The rapid and sometimes sizable changes whip through the tiers of the supply chain, increasing lead times for components and raw materials and driving down supplier reliability.
Proactive automotive suppliers know they must transform their supply chain planning to stay competitive under these circumstances. That transformation means building the critical capabilities to visualise their entire business, anticipate disruptions, and respond with agility. In doing so, they must transition to a digital operating model that will be the foundation for their future success.
This path forward for many leaders is a step toward higher granularity supply chain models that include and analyse a wide array of internal and external factors. When converted to knowledge, that data becomes the insights that shape tangible enterprise-wide value, even during times of uncertainty and complexity.
Using digital operating models to analyse data
Automotive supply chains that rely on archaic systems regularly fail to gather the relevant data required for successful decision-making. Even if they have access to the data, it is generally impossible to comprehensively analyse it to understand what is happening now and create forecasts of what will happen next.
With more data readily available, the potential is emerging to improve the accuracy of planning and predictions with artificial intelligence and machine learning
Transitioning to digital operating models helps leaders to link this critical data between products, channels, customers, and markets. It enables suppliers and OEMs to understand their markets and consumer demand drivers better by ingesting external factors like geographic car registration, mobility data, dealer sales patterns, economic activity, and more.
With more data readily available, the potential is emerging to improve the accuracy of planning and predictions with artificial intelligence and machine learning (AI/ML) forecasting. For example, OEMs and suppliers now use automated workflows and cutting-edge analytics to plan for new equipment and aftermarket opportunities. Also, by building plans for volume and value, today’s automakers can deploy real-time scenario analysis to better react to the constantly changing global and local environments.
Flexible end-to-end plans, deviations, and gap-closing actions are all featured in the next-generation automotive supply chain planning process. These types of advanced planning integrate demand and supply to optimise inventories and maximise revenue and profits for the foreseeable future.
The opportunity to create enterprise-wide value is immense when automotive supply chains can turn data into insights that inform impactful decision-making.
About the author: Patrick van Hull isSupply Chain Storyteller at o9 Solutions