An overview of design and operational issues of kanban systems M. S. AKTÜRK and F. ERHUN Presented by: Y. Levent KOÇAĞA

CONTENT Introduction to JIT and kanban Literature Overview A model for sequencing production kanbans Conclusion

Introduction to JIT JIT is a manuf. policy with a very simple goal: produce the required items at the required quality in the required quantities at the precise time they are required

JIT An ideal of having the necessary amount of material available where it is needed and when it is needed A pull system Effective in environments of high process reliability, low demand variability and setups

JIT: benefits Reduced WIP and FGI Reduced lead times Higher quality, reduced scrap and rework Ability to keep schedules Increased flexibility Easier automation Higher utilization

Limitations of JIT Applicable mostly to repetetive manufacturing Final assembly schedule must be very level and stable Large information lead times

Just in Time JIT philosophy JIT techniques JIT shop floor control systems

Kanban Dual-card production kanban & transportation kanban Single-card a schedule instead of production kanban Instantenous vs Periodic review Periodic review: fixed quantity or fixed withdrawal cycle

Literature review Mathematical programming Markov Chain Simulation Other approaches

Solution methodology Solution approach is either exact or heuristic Exact approaches include dynamic programming, LP, IP, MIP or NIP

Model details (analytical) Decision variables are mainly kanban sizes number of kanbans withdrawal cycle length safety stock Objective is to minimize cost or inventories (maximizing throughput for stochastic models)

Model details (simulation) Performance measures used: number of kanbans machine utilizations inventor holding cost backorder cost fill rate (probability that an order will be satisfied through inventory)

Settings of the models Production settings include layout number of time periods number of items number of stages capacity

Kanban system Single–card or dual-card

Assumptions Kanban size (empty cell for decision variable) Nature of the system deterministic vs stochastic Production cycle continuous vs fixed intervals Material handling instantaneous vs periodic Backorders and reliability

Determining kanban sequences FAS determines prod’n orders for all stages Once assembly line is scheduled it is assumed that the sequences propagate back Rest of kanbans scheduled by FCFS Some studies use simple dispatching rules

Determining kanban sequences Production levelling through scheduling is crucial Sequencing more complex because kanbans may not have specific due dates kanban controlled shops can have station blocking Sophisticated scheduling rules needed

Computational analysis Close interaction between design parameters such as: number of kanbans kanban sizes kanban sequences

Computational analysis Thus an experimental design developed to determine the withdrawal cycle length number of kanbans kanban sizes and kanban sequences at each stage simultaneously for aperiodic review multi-item, multi-stage, multi-period kanban system

Computational analysis Objective is to minimize total production cost that is the sum of inventory holding and backorder costs over all stages Impact of operating issues such as sequencing and lead times on design parameters: four sequencincing rules considered (SPT, SPT-F,FCFS,FCFS-F) Family based rules of FCFS andSPT/LATE

Model

Algorithms

Experimental factors

Toyota formula maximum inventory level=na=DL(1+s) Lead time is not an attribute of the part Rather it is dependent on the shop floor Work-in-queue rule used for lead time estimation As lead times are estimated the maximum inventory level at each stage will change Thus the solution space increases

Results Effects of kanban sizes and number of kanbans and their interaction significant Therefore they are chosen so that MINV ijm remains constant There decision variables withdrawal cycle lenth, T number of kanbans for part i of family j, n ij T kanban size, a ij T Six alternatives for T from {8,4,1,0.5,0.25} in hours or {480,240,60,30,15} in minutes number of kanbans as powers of two, thus kanban sizes given by:

Results Therfore each sequencing rule evaluates 36 alternatives and finds the kanban sequences at each stage with minimum sum of inventory holding and backorder costs

Results: comparison of the number of instances of best withdrawal cycle lengths

Results: comparison of the maximum inventory levels of sequencing rules

Results comparison of inventory holding costs of sequencing rules

Results Smaller setup to processing time ratio results in withdrawal shorter cycle lengths Thus FCFS produces longer cycles Withdrawal cycle length not robust to scheduling rules Item based rules perform well when withdrawal cycles are long FCFS-F prefers shorter cycles compared to FCFS

Results Minimum value for maximum inventory via SPT/LATE Highest for FCFS Maximum value for all rules given by Minimum avg. inv. Holding cost by SPT-F 55.88% of inventories full for SPT/LATE All these point to the necessity of sophisticated scheduling algorithms

Conclusions About existing studies:  very few sizes consider kanban sizes explicitly (but # of kanbans depends on it)  the scheduling algorithms should go beyond the scope of smoothing  Periodic review systems should be considered

Conclusions About the experimental study:  Withdrawal cycle lenghts not robust to scheduling algorithms  Item-based rules outperform family-based ones if system load is loose (opposite if system loaded)  When setups increase system performance decreases  For high setups family-based rules perform better  Finally, more sophisticated scheduling algorithms must be cosidered

Q & A