As early as a few decades ago, Toyota Motor pioneered the concept of just in time to alleviate the lack of cash, land and large inventory. Through a precise management system, the supplier's response time is shortened and the investment in process inventory is reduced.
But the basis for timely production is that all parts and raw materials must arrive on time. In the automotive assembly line, the lack of any one component will affect the normal production of the production line. If parts are delayed, it can lead to product quality problems or even downtime; if the shipment is too early, the factory is not ready to receive, which will increase inventory management costs.
To meet the production needs of large manufacturers, OEM foundries have strict shipment response times to ensure that parts arrive at the supplier on time. However, this will bring great production pressure to suppliers. On the one hand, suppliers will invest a lot of money for expedited transportation. At the same time, in order to avoid the penalty for downtime caused by delayed transportation, suppliers must also maintain a certain amount of zero. Parts inventory. So it always request industry 4.0 computer system.
Therefore, timely production can increase the productivity of OEMs, but it will lead to inefficient suppliers, which will increase the cost of components.
This requires future digital supply chains with machine learning capabilities that are predictable in real-time, enabling predictive quality management, predictive manufacturing, and predictive maintenance through digital connectivity, increasing production and distribution efficiency, and reducing costs.