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Building more resilient automotive supply chains with AI
It’s been a long 18 months for automotive manufacturers. From the shortage of computer chips and COVID-19-related factory shutdowns, to raw material shortages and logistical challenges, shocks to the supply chain are continuing to appear with alarming regularity.
And the real impact of this crisis is already becoming apparent: 7.7 million fewer vehicles were built this year, costing the industry over $200bn.
The problem was automotive manufacturers couldn’t predict the dramatic changes in demand, nor the devastating shortages headed their way. This meant they couldn’t accommodate them within their supply chain or operations. As one crisis generated another, there were no ‘shock absorbers’ left to protect manufacturers.
The current levels of uncertainty, where widespread shortages in materials and labour are outpaced by demand, has exposed the inherent fragility within the automotive supply chain.
What’s causing automotive supply chain fragility?
Outdated business processes: Conservative approaches to technology have stuck by the ‘Just-In-Time’ approach of the 1970s, where reliable suppliers maintained efficiency and low inventories. But these days, 75% of automotive manufacturers rely heavily on suppliers to maintain their operations. Problems with suppliers disrupt the entire downstream manufacturing process – this is especially troubling given the complexity of their supply chains.
Disconnection: Fragility is made worse by disconnects within the supply chain; the decisions and strategic imperatives set in the boardroom by senior executives rarely take into account what operational teams see ‘on the ground’. Supply chain shocks necessitate a rapid-fire response to avoid leaving money on the table. Insights from every stage of the supply chain need to be fed into decision-making in the boardroom, and the decisions made there must be effectively communicated back down to the individual teams.
Siloed Data: Organisations with complex supply chains tied up in siloes between demand, production and delivery rely heavily on manual spreadsheets to generate insights, and planning meetings to communicate them. But these processes can’t respond to the shocks fast enough, allowing decisions to quickly become outdated.
Inaccurate forecasts: When a sudden event impacts the supply chain, siloed data and poor quality software can’t take this information into account in real-time – they can only tell you what happened yesterday. That means most forecasts aren’t accurate. Low confidence in forecasting means interventions aren’t made, significant supply chain shocks can’t be predicted and therefore managed, and the resulting inaccurate sales forecasts lead to excess stock.
How can automotive manufacturers address this fragility?
Manufacturers can’t stop relying on suppliers, nor control external factors – instead, they need to find new ways to react faster and better to the next shock. The New Normal is creating a generation of supply chain and business leaders that want to move beyond reactive decisions and aim for proactive decision-making that adapts to insights from every angle of operations, all before a crisis hits.
But until recently, this was an immense challenge; the technology simply wasn’t there. It’s only recently that technology like AI can be applied to real-world business problems to improve resilience.
With the right approach, AI can build a more resilient, connected supply chain that addresses poor quality data, poor forecasts and disconnected teams. It can bridge the gap between the boardroom and operational teams.
AI can be used by operations teams to generate accurate forecasts that bring in a range of data sources, including leading indicators of demand. This means forecasts and insights can be trusted even when the supply chain is buffeted by uncertainty, or a demand indicator no longer produces accurate data. It provides a new level of understanding into the factors that are driving demand.
Clear forecasts and explanations behind spikes in demand also allow executives to convert their strategic imperatives into actionable insights that the supply chain team can implement, and it also allows them to surface their own challenges back up to senior management.
These forecasts essentially build a digital version of an organisation: it provides visibility for the boardroom right down to SKU-level, both across the supply chain horizontally and into operations teams vertically. This allows senior management to visualise trade-offs, make decisions with minimal risk, and optimise the individual decisions that fulfil KPIs more readily. A centralised view of the entire supply chain, from demand to production to delivery, is now critical as supply chain challenges are directly impacting overall business strategy.
Take the semiconductor shortage, for example. In the short term, demand forecasting can help you allocate these scarce supplies to prioritise the manufacture of your most in-demand vehicles. But it can also help management develop a longer term response, whether that’s negotiating sooner with chip manufacturers or finding alternative suppliers.
Only the companies that harness high-quality data across operations and leverage AI in supply chains will be able to survive the New Normal.
To find out more about preparing your business for the next crisis, get in contact with our team.
