During the last decades, Just-In-Time has been criticized from different authors1. Indeed, certain specific conditions – which, though, are not uncommon in manufacturing companies – can put in evidence some well-known weak points of the Japanese approach. Specifically, un-steady demand in multi-product environments where differences in processing lead times are not negligible represent a scenario where JIT would miserably fail, despite the commitment of the operations managers.
First, we have to keep in mind that one pillar of Lean Production is the “one-piece-flow” diktat. A one-piece batch would comply with the Economic Production Quantity theory2 only when order cost (i.e. setup time) is zero. Having non-negligible setup times hampers JIT implementation and makes the production leveling problem even more complicated. It is peculiar that, originally, operations researchers concentrated on finding the best jobs sequence considering negligible setups time. This bound was introduced into the mixed model kanban scheduling problem only since 2000. Setups are inevitable in the Lean Production philosophy, but are considered already optimized as well. Given that setup times are muda, TPS approach focuses on quickening the setup time, e.g. through technical interventions on workstations or on the setup process with SMED techniques, not on reducing their frequency: the increased performance gained through setups frequency reduction is not worth the flexibility loss that the system may suffer as a consequence. Indeed, the standard kanban management system, ignoring the job sequencing, does not aim at reducing setup wastes at all. Analogously, the Heijunka box was developed for leveling production and can only assure that the product mix in the very short term reproduces that in the long term; in its original application, the decision on the job sequence is left to the operator. Only in some enhanced version, the sequence is pre-defined applying some scheduling algorithm.
Given the fact that JIT is based on stock replenishment, constant production and withdrawal rates should be ensured in order to avoid either stock outs or stock proliferation. Mixed-model production requires a leveled Master Production Schedule (MPS)3, but this is not sufficient to smooth the production rate in a short time period. While it is easy to obtain a leveled production in a medium or even medium-short period, it is difficult to do it in each hour, for each workstation and each material.
Indeed, demand is typically unstable under two points of view: random frequency, which is the chance that production orders are irregularly received, and random quantities, which is related to product mix changes. Indeed, since TPS assume minimal stock levels, the only chance to cope with demand peak is to recur to extra production capacity. However, available production capacity should be higher than required as the average (as TPS requires), but for sure cannot be limitless. Thus, the JIT management system should anyway be able to consider the opportunity of varying the maintenance plan as well as the setup scheduling, in case of need. On the other hand, if the production site faces a leveled production, changes in product mix should not represent a problem; however, they increase sequencing problem complexity. Most of the operational research solutions for JIT scheduling are designed for a fixed product mix, thus its changes can greatly affect the optimality of solutions, up to make them useless.
On the contrary, kanban board mechanism is not influenced by demand randomness: as long as demand variations are contained into a certain (small) interval, kanban-managed workstations will handle their production almost without any problem. Therefore, in case of unstable demand, in order to prevent stock-outs, inventory managers can only increase the kanban number for each product: the greater are the variations, the greater is the need of kanban cards and, thus, the higher is the stock level. In order to prevent stock level raise, some authors4, 5 proposed to adopt a frozen schedule to implement JIT production in real companies, where demand may clearly be unstable. Anyway, this solution goes in the opposite direction compared to JIT foundations.
Moreover, one-piece-flow conflicts with demand variability: the batch size should be chosen as its processing time exceeds the inter-arrival time of materials requests. Thus, the leveling algorithm must find the proper sequencing policy that, at the same time, reduces the batch size and minimize the inter-arrival time of each material request. This sequence clearly depends on the total demand of each material in the planning horizon. However, JIT does not use forecasting, except during system design; thus, scheduling may be refreshed daily. From a computational point of view, this is a non-linear integer optimization problem (defined mixed-model just-in-time scheduling problem, MMJIT), which has non-polynomial complexity and it currently cannot be solved in an acceptable time. Thus, reliable suppliers and a clockwork supply chain are absolutely required to implement JIT. Toyota faced this issue using various approaches6:
- moving suppliers in the areas around the production sites, in order to minimize the supply lead time;
- collaborating with the suppliers and helping them to introduce JIT in their factories;
- always relying on two alternative suppliers for the same material, not to be put in a critical situation.
In the end, it should be noted that, considering that at each stage of the production process at least one unit of each material must be in stock, in case of a great product variety the total stock amount could be huge in JIT. This problem was known also by Toyota7, who addressed it limiting the product customization opportunities and bundling optional combinations.
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