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