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
Algorithms
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 MINVijm remains constant There decision variables withdrawal cycle lenth, T number of kanbans for part i of family j, nij
T
kanban size, aijT
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
Resultscomparison 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 1110111 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