Developing supply chain mapping in a leading automotive company for risk management and ESG purposes.

Over the last two decades, Original-Equipment-Manufacturers' supply chains have reached a critical dimension in worldwide dispersion and depth. Recent events such as the COVID-19 crisis, the Suez Canal blockage, and the world's semiconductors shortage have highlighted the critical importance of supply chain management. As the founder of lean production and Just-In-Time, Toyota has achieved the highest manufacturing efficiency. The company has identified supply chain mapping as the next step to achieve competitive advantage through better control and vision of the value chain. Moreover, continuously improving its sourcing by having high standards for a clean supply chain, Toyota Motor Europe strives to improve its due diligence by monitoring the supply chain to clear it from environmental and human rights-related issues. The following work addresses the challenges mentioned above by proposing a new supply chain mapping system. The main scopes are improving Environmental-Social-Governance and risk management activities. The project touches several dimensions: confidentiality agreements, competition policies, data management, and data protection. It also has constraints both on budget and on time to develop. The initial analysis identified three main critical points: suppliers' interaction with the system, data management, and keeping data up-to-date. Moreover, this ambitious project is set in a fast-evolving technological framework. Therefore, new technologies related to data-sharing and exploiting publicly available databases have emerged and have been critically analyzed. Consequently, reaching the optimal choice has required a rigorous approach in evaluating market offered solutions and in-house development feasibility. This evaluation was performed by establishing KPIs and balancing the weights between them, given their relationships. Considering the shortcomings of a previous mapping project and the new findings, a holistic executive program on how to solve the numerous difficulties is proposed. The optimal system maximizes the benefit-effort tradeoff and makes the most of the dimensions mentioned above. It is a hybrid solution between the off-the-shelf and the in-house developed tools. This system leverages the outsource software that uses AI to gather and filter information to map up to Tier-n and feeds the found results in the internally developed mapping tool through APIs. It also avoids having suppliers disclose their supply chain to a third party, only interacting with the internal software to validate data and add additional information. The most efficient approach is to retrieve strictly necessary information related to the geo-localization of suppliers' production sites and headquarters up to Tier-n. Once the mapping is successful, the data can be crossed with other databases to extract maximal value in various areas, from optimizing suppliers' dispersion to identifying subsuppliers at risk.