supply chain: increasing the efficiency of supply chain planning in the automotive industry
Some of the biggest supply chain challenges in the automotive industry are:
· Increased marginal erosion and supply issues due to high logistics and inventory costs, increased risk of product obsolescence, poor warehouse management and counterfeit parts.
· Lack of sustainable business models affects decision-making processes, hinders end-to-end supply chain visibility and impairs forecasting capabilities.
Inadequate data management, insufficient key performance indicators (KPIs) for benchmarking and poor demand forecasting due to a lack of data on inventory turnover, sales, historical sales, promotions and seasonal variations.
· Legacy and disconnected systems that operate in silos leading to poor inventory tracking, incorrect orders and lack of transparency between departments
Small planning teams managing large amounts of data, limiting their ability to make the best planning decisions
If we were to get closer, all these challenges revolve around a single aspect, “data management”. Therefore, the solution to these challenges lies in “intelligent use of data”. As our business model evolves towards a modern economy giving rise to the megatrend of a data-driven economy with large amounts of data entering the system on a continuous basis, data could be the solution to the challenges faced by providers. of the automotive aftermarket. Data can be a powerful asset when used effectively and intelligently, and it reflects our government’s macroeconomic efforts to transition to a data-driven economy. The supply chain deals with a host of stakeholders working at different levels and producing a large amount of data. Therefore, there is a strong need for data-driven insights and analytics in automotive supply chain management.
It is now up to industry players, such as automotive OEMs, to embrace the megatrend of data-driven business frameworks to meet their challenges and create smarter supply chains. Technologies such as artificial intelligence, machine learning, blockchain, cloud computing and IoT can serve as a major enabler here, providing a platform to integrate all technologies such as predictive analytics, data analysis and enable intelligent use of data.
Data Incorporation Strategies: Data Extraction and Mining
We clearly have mountains of data, and more are arriving every day, but the real question is how can automotive players use this data to their advantage. More than data, it relies on technologies that can help in the collection, extraction, segregation and use of data. For example, digitization has changed the way customers interact with automotive players – how they research, buy and maintain their vehicles. Therefore, vendors need to improve their customer connectivity, demand forecasting, market analysis, and other metrics, while studying the massive amount of data coming from multiple channels.
This data comes from both internal and external sources, such as the social media channels brands use to connect with their customers, and is frequently scattered across organizational silos. While data can greatly help aftermarket suppliers accurately forecast customer demands throughout the supply chain, there is an increased need for technology that organizes all of this data on a single platform, offering a much more holistic view. For example, by extracting and leveraging data through smart technologies such as Cloud Analytics and Artificial Intelligence, aftermarket vendors can study customer behavior analysis while collaborating across the supply chain. supply. By getting basic statistical forecasts of all auto parts based on average sales, planners can make better decisions for their products. KPIs and reports provide insight into profitability drivers and facilitate analysis of historical demand trends.
While many supply chain management technologies, such as tracking and management solutions and customer service solutions, have become mainstream, there is a need to use technologies that deal with multiple databases and frameworks to fully exploit the potential of data. Automotive players can generate anticipatory analytics to future-proof their supply chain by leveraging data through technologies such as cloud analytics, IoT analytics, and blockchain. As part of advance analytics, point-of-sale (POS) data, inventory data, and production volumes can all be analyzed in real time to identify mismatches between supply and demand. These can then be used to drive actions such as price changes, timing of promotions or adding new lines to realign things.
In a nutshell, data is only as effective as the metrics used to integrate it into the business framework and automotive supply chains. By combining logistics and supply chain management with data and tracking, integrated decision making, technology innovation and strong logistics partnerships, automotive companies and OEMs can expect to have “more of control” in the future.
The author is Senior Manager, Cloud ERP, Oracle India.