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Indian OEMs are yet to adopt inventory management

NEW DELHI: No Indian car manufacturer has yet implemented just-in-time (JIT) end-to-end in its supply chain, a widely accepted inventory management practice in the auto manufacturing industry, which runs the risk of sudden shifts in orders and a inflated inventory, leading to potential business disruptions and financial strains. According to Vector Consulting Group, JIT is a pull-based inventory movement method known for significantly reducing inventory costs, improving efficiency, reducing waste and improving responsiveness. Without an inventory pull system in sync with the market, smaller players (level 2/3) in the automotive supply chain often face the bullwhip effect manifested in sudden shifts in orders and inflated inventory, leading to possible disruptions in activities and production processes. financial strain.

After Toyota pioneered JIT in the 1970s, the company’s overwhelming success inspired the widespread adoption and adaptation of JIT among other automakers around the world. The Indian auto industry also created automotive hubs to bring suppliers closer together, streamline logistics and promote collaboration. Therefore, this sector (compared to others) is best suited to fully implement JIT (including Kanban and Heijunka) in the end-to-end supply chain.

However, against expectations, the report, based on surveys and interviews, shows that even after half a century, no Indian OEM appears to have successfully implemented JIT across their end-to-end supply chain, from suppliers to dealers. .

Although most OEMs, around 75 percent, have implemented JIT practices in their manufacturing processes, their supply chain and distribution partners are not yet fully involved.

However, there is one exception that occurs with a multinational OEM that has adopted a zero inventory model at the dealer level, where actual end-consumer demand drives inventory movements.

Some have also done a demand-driven replenishment for spare parts only from their OEM warehouse to dealers.

According to Vector, most follow a mixed push-pull system, i.e. monthly/weekly forecast-based ordering from dealers, planning monthly production needs from suppliers with daily inventory movement to OEM factories based on demand. Furthermore, while many suppliers (Tier 1) are involved in ‘JIT partnerships’ by some OEMs, there are clear signs that the inventory benefits are only going to the OEMs. The study drew these conclusions after examining the inventory practices of major Indian OEMs, Tier 1/2 suppliers.

Achal Saran Pande of Vector Consulting notes that India’s auto manufacturing industry is growing rapidly and to become the world’s auto manufacturing hub, it is essential to streamline supply chain practices. The market has evolved and therefore systems like JIT, which previously delivered success, will not necessarily thrive in today’s volatile environment. At the same time, going the other way and adopting a non-pull-based approach leads to inventory build-ups, higher costs and low capital turnover, says Pande.

Therefore, we need a pull-based approach that protects the supply chain from high and varied lead times and dampens variability in the market, while at the same time ensuring high end-to-end supply chain flexibility.”

Recognizing the limitations of JIT in the way it is currently implemented in India and noting the conceptual gap that limits the implementation of the methodology in an environment of high demand variability (with the proliferation of models and variants across each OEM), as well as high and very delivery lead times (global supply chains), Vector proposes a more effective end-to-end pull system that can guarantee the benefits usually expected from JIT to OEMs, while also providing a practical win for all their partners.

Proposed solutions include completely abandoning forecasts or sales/targets for day-to-day decisions about creating inventory in production and moving it to warehouse locations. The report also highlights the need to dampen demand variability using strategically placed buffers and implementing a system of ‘buffers replenishing buffers’, based on consumption triggers from lower nodes in the supply chain. Implementing an execution-based priority to handle variability in demand and capacity so that the system gets clear signals about what to focus on when total demand across products exceeds capacity.

Introducing a method for periodically changing buffer levels by intelligently detecting any change in demand patterns, both in the recent past and in the near future.