Labour cost reporting and analysis in global automotive refers to the process of tracking and analysing the costs associated with employee compensation, including wages, salaries, benefits, and other related expenses. Today, more than ever, with the global market changing rapidly, this process is crucial for automakers to stand out from competition.
In this blog, we explore how labour cost reporting has evolved over the years, the impact of new tech on this process, and what we can expect in the future.
3 Technologies Impacting Labour Cost Management
In recent years, technology has been playing an increasingly important role in this process. Automation and Artificial Intelligence (AI), for instance, has been aiding in the rapid reduction of indirect costs. Data analytics is being used to identify patterns and trends in labour costs. And, automation and robotics is being used to reduce the need for human labour.
Let’s look at these in more detail.
Until a few years ago, automakers relied on traditional cost optimisation approaches to manage labour costs. While the approach worked in mid-2000s, with G&A (general & administrative) expenses growing more slowly than revenues, the approach didn’t hold for long. Since then, the trend reversed. G&A expenses rose faster – at 15.4% – compared to only a 6% revenue growth. This demanded automakers to look at labour cost management from a different perspective.
In 2019, McKinsey observed this trend and proposed a solution for a better, faster approach to reducing G&A costs – that is, through tech-enabled reduction of indirect costs. Indirect costs are those that are separate from the direct process of manufacturing goods or offering services. These costs are shared across the company and are largely fixed, like finance, procurement, HR, marketing, and IT.
So, when reduction of indirect costs is aided with tech like automation, artificial intelligence (AI), and other technologies, companies will be able to find new opportunities in areas like capacity reallocation, spending effectiveness, and accounts receivable. The result? Based on McKinsey’s experiences with 24 industrial companies, they found that this tech-enabled approach can cut indirect costs by as much as 15 to 20% in 12 to 18 months.
A second approach to managing labour costs is through data analytics.
A third approach to achieving cost effectiveness and efficiency is by automating assembly lines and using robots for tasks like welding. This can help reduce the need for human labour, which can lower costs while also increasing efficiency and quality. According to a report by Seraph, the use of robots in the automotive industry is expected to increase by 10% annually over the next five years.
4 Approaches to Labour Cost Reporting & Analysis
Aside from technological aids, there are several methods that global automobile companies can use to do labour cost reporting and analysis. Here are some of them:
- Activity-Based Costing (ABC): This method involves identifying the activities that drive labour costs and assigning costs to each activity. By doing so, companies can identify areas where they can reduce costs and improve efficiency. For example, a company might find that it is spending too much on overtime pay and decide to hire more workers to reduce the need for overtime.
- Benchmarking: This method involves comparing a company’s labour costs to those of its competitors. By doing so, companies can identify areas where they are overpaying or underpaying their workers. For example, a company might find that it is paying its workers more than its competitors and decide to reduce wages to stay competitive.
- Process Mapping: This method involves mapping out the steps involved in a particular process and identifying areas where labour costs can be reduced. For example, a company might find that it is spending too much time on a particular task and decide to automate that task to reduce labour costs.
- Data Analytics: This method involves using data to identify patterns and trends in labour costs. For example, a company might use data analytics to identify which workers are the most productive and which ones are the least productive. By doing so, the company can identify areas where it can improve efficiency and reduce costs.
Tech Adoption Will Continue to Rise
With technology only set to transform the auto industry even further in the coming years, it’s wise for companies to leverage new technologies to remain competitive and achieve optimal revenue growth. According to several reports, we’re only going to see higher adoption of like big data, AI, advanced analytics, data visualisation and process-mining tools to reduce indirect costs and improve efficiency. Clearly, whether it’s manufacturing, supply chain, demand forecasting, or cost management, for automakers, tech is the way ahead.
Merit’s Expertise in Data Aggregation & Harvesting for the Global Automotive Sector
Merit Data and Technology excels in aggregating and harvesting automotive data using AI, ML, and human expertise. Our capabilities include:
- Crafting end-to-end data pipelines and scalable data warehouses
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- Extracting retail product attributes and audience data
- Aggregating industry-specific data points for informed decision-making
Trusted by leading automotive brands, Merit drives innovation and efficiency by delivering refined, actionable insights.
- Tech-Driven Efficiency: The automotive industry is leveraging automation, AI, and data analytics to optimise labour costs, identifying patterns and trends for enhanced efficiency.
- Indirect Cost Reduction: Technology-enabled approaches, such as automation and AI, are proving instrumental in reducing indirect costs by 15 to 20%, leading to significant savings within 12 to 18 months.
- Strategic Cost Management: Employing methods like Activity-Based Costing, Benchmarking, Process Mapping, and Data Analytics allows global auto companies to strategically manage labour costs, improving overall operational effectiveness.
- Continuous Tech Adoption: The future of labour cost reporting in the automotive sector will witness increased adoption of big data, AI, advanced analytics, and process-mining tools, ensuring sustained competitiveness and optimal revenue growth.
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