The automotive industry is struggling to meet demand as a growing supply chain shortage cripples the global economy. Chip shortages, among other components, have fueled a steep increase in car prices, as much as USD$900 above the manufacturer-suggested retail price (MSRP) for non-luxury cars and USD$1,300 above MSRP for luxury ones.
Market analysts predict the supply chain to normalize in the third quarter of 2022, which is only a few months away as of this publication. In light of this, industry experts are using analytics to streamline production and minimize waste to address these challenges. Here’s an in-depth look into analytics and its role in the automotive sector.
It’s important to know that analytics is integral to every facet of car production, not only in supply chain optimization (more on that later). Everything from knowing consumer trends to ensuring a steady stream of resources requires data—lots of it. The cars themselves are valuable sources of data, an estimated 25 GB that can help manufacturers understand trends more.
Each aspect of the automotive workflow has its respective form of analytics. However, making the most out of these analytic processes entails integrating them into cross-value chain analytics, narrowing them down to four fundamentals:
- Customer Behavior – Gauging the potential of various customer groups, catering to new customers while keeping old ones, and expanding the overall customer experience
- Marketing Management – Measuring the impact of marketing campaigns on sales and other trends and developing auto repair advertising ideas and other campaigns
- Predictive Quality – Identifying possible defects and other issues in manufacturing lots, examining the need to issue recalls, and assessing warranty claims
- Supply Chain Optimization – Anticipating trends to adjust the supply of specific parts and accessories and evaluating custom orders
Each of these is crucial in its own right, but turning tons of raw materials into a reliable and tech-laden vehicle begins with the supply chain. Car brands can study the market for trends and tailor their marketing campaigns based on them, but they won’t matter without the means to make the car that will deliver to customers.
Tackling The Issues
The lifeblood of any business or industry is its logistics, and car manufacturers are no different. Without the necessary components delivered to factories and service centers, manufacturing and repair activities can slow down, if not grind, to a stop.
Understanding how analytics will help curb the effects of supply chain issues in the automotive industry involves understanding the problems themselves. Industry experts have identified four critical issues that analytics need to address.
- Supply Chain Visibility
A typical car needs an estimated 30,000 individual components (the exact number may vary depending on the make and model). It only takes one missing part for the whole vehicle to get stuck in production limbo.
Unfortunately, many experts agree that the automotive industry struggles with supply chain visibility. Difficulties in making orders and tracking their deliveries can hamper manufacturing. Resolving them becomes even more crucial for carmakers shifting to just-in-time manufacturing practices.
Recommendations include integrating suppliers into a common analytics platform to help identify delays and other problems. Analytics hardware and software that uses Internet of Things (IoT) technology can assist with real-time tracking.
- Risk Management
The automotive industry faces numerous risks, from missed production goals to mishaps on the factory floor. Supply delays are no less significant for reasons explained earlier. Car makers should have contingencies, such as setting up secondary suppliers.
One standard method for mitigating risks is through a Failure Mode and Effect Analysis (FMEA). This method outlines how a business may suffer from failure and how it can affect the company in the short or long term. Businesses should conduct FMEA early in the design phase to have enough time to react to risks accordingly.
- Quality Control
Regardless of the source of components, effective quality control is a must. Recalls are evidence that manufacturers may have overlooked something along the workflow and have to spend to replace the faulty parts.
Analytics can provide sufficient data to conduct internal and third-party audits of parts quality. Although internal quality control may be easy, getting suppliers to adhere to quality standards can be tricky. In this case, it all boils down to effective dialogue between the manufacturer and its suppliers.
- External Factors
Risk factors like economic crashes, natural disasters, and—in this status quo—global health crises can hinder operations. While preventing them is beyond a manufacturer’s control, reducing their impact is well within.
As explained in this piece, analytics isn’t only integral for resolving the automotive industry’s many issues with its supply chain. It’s more or less being implemented significantly, helping manufacturers take careful steps as they build their products. It’s safe to say that analytics may hold the key to the more affordable and reliable cars of tomorrow.