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Just-in-time (JIT) Logistics as a concept was developed by Toyota, a leading automaker, to revolutionise their manufacturing process. Years later, this became the norm for manufacturers beyond the automobile industry. The JIT era began around 2011 and was also picking up pace with the Logistics and supply chain industry. While the era was at its peak, the world came to a halt due to the COVID-19 pandemic.
According to reports, 64 per cent of companies during that time switched to a just-in-case model to maintain business continuity. Hence, the concept was declared a failure as it lacks resilience to an unprecedented event like the pandemic. After a few years of the pandemic, now the concept of JIT is re-emerging and is a point of discussion. Yet, industry thought leaders question the resilience of this model.
The model was deemed a failure by many. However, addressing resilience is of utmost importance. Here are some limitations that can be addressed with just-in-time.
Lacks resilience
A shock like a pandemic or even a war will halt the supply chain with the JIT model. Several companies recorded a lack of business continuity; notably, some went out of business for a short time when the pandemic-induced lockdowns were imposed.
Forecasting inaccuracy
The model is not prone to demand-supply shocks, too. Demand and supply are dynamic variables, as the cycle time varies based on the manufacturer and the vendors who supply the raw material. On the other hand, a transporter carrying the goods from the manufacturing facility to the transit point and eventually the destination is subjective as the quantity is minimal. Therefore, the accuracy of cost calculations is potentially inaccurate.
Handling large consignments
A manufacturer might have received an order for a limited quantity of goods. Unlikely, the cost of handling a customised number of consignments might be higher than usual, and the time involved will be high as it is not handled on a priority basis.
With the changing business landscape, the need for a refined just-in-time model is in place, and in such a scenario, re-emergence is inevitable. However, accurately predicting demand and a shock like the pandemic is paramount. It is a known fact that Toyota improved the model by constantly studying process patterns at all levels. Therefore, just-in-time needs a smart makeover approach.
The intervention of technology is seen as the best approach to restructure JIT. With Artificial Intelligence and Machine Learning, building predictive capabilities is possible, and this will not only study demand/supply patterns on a macro level but also at a process level. By doing so, the model will become sustainable and focus on improving supply chain efficiency.
Here are some benefits of technology intervention in JIT Logistics.
Accurate demand forecasting
As mentioned above, with its enormous predictive capabilities, JIT as a model can predict demand and prepare Logistics managers for a fall/surge. For example, the supply surge, which took place in 2021, could have been predicted if an AI-enabled JIT Logistics system was in place.
High-cost savings
JIT as a model helps manufacturers save costs on manufacturing. However, the cost will eventually increase as the delivery and other aspects get customised. Manufacturers can save costs significantly by predicting and preparing in advance with deep-tech interventions.
Optimised efficiency
Improving efficiency is the top concern for every airport and port authority worldwide today, and technology is the number one catalyst in helping them do so. However, a tech-enabled JIT Logistics system will solve all the challenges and help them thrive sustainably.
Glocalisation
Ensuring proximity of local and regional supplies is paramount in today’s world, and a refined JIT with technology intervention will ensure a seamless flow of planning, sourcing, making and delivering operations. Glocalisation ensures labour arbitrage for specific sectors and offers a competitive edge for all businesses.
The Cargo Community System is a boon to the Logistics ecosystem as it creates data lakes; therefore, using data analytics, many insights can be generated to provide decision-support solutions to stakeholders. In a complex environment and a fast-transforming world, such practices offer a competitive edge to the overall ecosystem and ensure that everybody thrives equally.
During the pandemic, the primary reason to ditch JIT was its inability to sustain the shock. Experts believe that with the right technology adoption, the model will be sustained longer and is viable for a fast-transforming world. Yet, some argue that the model will fail due to its nature. Hence, a widespread test run adoption is essential to observe the model’s resilience.
