In recent years, the adversities triggered by the pandemic, such as the global economic crisis and health concerns, have brought to light several issues in the business scenario. Disruption in supply chains emerges as a central challenge. The pandemic has revealed the fragility of our supply chains and exposed numerous deficiencies in process integration. Faced with constant disruptions in supply chains, auto parts suppliers are now operating under new conditions. To orchestrate efficient distribution, aftermarket supply chain management must deal with managing multiple parts, varying demand patterns, multiple suppliers, ineffective legacy systems, and outsourced parties.
Critical challenges facing the supply chain in the automotive industry include:
- Rising Margins and Purchasing Challenges: Results from high logistics and inventory costs, increased risk of product obsolescence, poor warehouse management and threats of counterfeit parts.
- Lack of Sustainable Business Models: Impacts decision making, undermines transparency throughout the supply chain and undermines forecasting capacity.
- Inadequate Data Management: Lack of key performance indicators (KPIs) for benchmarking, compromised demand forecasting due to missing data on stock rotation, sales history, promotions and seasonal fluctuations.
- Outdated and Disjointed Systems: Siled operations lead to poor inventory tracking, incorrect orders, and a lack of transparency between departments.
- Overloaded Planning Teams: Small teams overwhelmed by large volumes of data, limiting their ability to make optimal planning decisions.
Looking closely, all these challenges converge on a central element – “Data Management”. Therefore, the solution to these problems lies within the “intelligent use of data”. As our economy evolves into a data-driven economy, with a continuous flow of data into our systems, leveraging data efficiently could be the answer to the challenges faced by auto parts suppliers. Data, when used effectively and strategically, can become a powerful resource, aligned with government efforts towards a data-driven economy.
The automotive supply chain involves multiple stakeholders operating at different levels, generating enormous volumes of data. Therefore, there arises a pressing need for data-based and data-driven analytics in automotive supply chain management.
Automotive industry players such as auto parts suppliers must now embrace the megatrend of data-driven business structures to solve their challenges and build smarter supply chains. Technologies such as artificial intelligence, machine learning, blockchain, cloud computing and the Internet of Things play a crucial role, offering a platform to integrate diverse technologies such as predictive analytics, data analytics and data mining.
Data Incorporation Strategies: Data Collection and Use It is clear that we are inundated with data, with volumes growing every day. However, the fundamental question is how automotive companies can capitalize on this data to their benefit. More than just data, technology plays a fundamental role in collecting, extracting, sharing and using data. For example, digitalization has profoundly altered the way customers interact with automotive companies, from researching to purchasing and maintaining vehicles. Therefore, suppliers need to improve customer communication, demand forecasting, market analysis and other areas by leveraging the vast amount of data coming from diverse sources.
This data comes from internal and external sources, such as social media channels used by brands to connect with their customers, often dispersed across multiple organizational units. While this data can be invaluable for predicting customer needs across the supply chain, technology is key to consolidating all this data into a single platform, providing a more comprehensive view. Extracting and utilizing data through intelligent technologies such as cloud analytics and artificial intelligence allows suppliers to learn about customer behavior patterns and share this information across the supply chain. With key statistical forecasts for all automotive parts based on sales averages, planners can make more informed decisions about their products. Key performance indicators (KPIs) and reports help you better understand profitability drivers and analyze historical demand trends.
While many supply chain management technologies, such as order tracking and management solutions, and customer service, have become commonplace, truly exploiting the potential of data requires technologies that work across multiple databases and platforms. Automotive players can perform predictive analytics to future-proof their supply chains by leveraging data through technologies such as cloud analytics, the Internet of Things and blockchain. As part of predictive analytics, point-of-sale (POS) data, inventory data and production volumes can be analyzed in real time to identify inconsistencies between supply and demand. This data can be used to take actions such as adjusting prices, scheduling promotions or introducing new products to reorder the landscape.
In short, data effectiveness is inextricably linked to the steps taken to integrate it into business operations and automotive supply chains. By uniting logistics and supply chain management with data and traceability, integrated decision-making, technological innovation and strong logistics collaborations, automotive companies and suppliers can expect to gain “enhanced control” as we move forward.
