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The effects of lead times on system behaviors with manufacturing/remanufacturing. SNU IEFAL May, 27, 1999 Byun Myung Hee. Different forms of reuse -by Thierry et al.(1995). Direct reuse : reused directly without prior repair operations
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The effects of lead times on system behaviors with manufacturing/remanufacturing SNU IEFAL May, 27, 1999 Byun Myung Hee
Different forms of reuse-by Thierry et al.(1995) • Direct reuse : reused directly without prior repair operations • Repair : restore failed products to ‘working order’, though with a loss of quality • Recycling : material recovery without conserving any product structures • Remanufacturing : conserves the product identity and seeks to bring the product back into an ‘as new’ condition
An investigation of lead-time effects in manufacturing/remanufacturing systems under simple PUSH and PULL control strategies Erwin van der Laana ,Marc Salomonb, Rommert Dekkera a Erasmus Universiteit Rotterdam, The Netherlands b Katholieke Universiteit Btabaat, Department of Econometrics, The Netherlands European Journal of Operational Research 115(1999)195-214
Contents • Introduction : PUSH- and PULL-strategies • System assumptions • Analysis for the PUSH- and PULL-strategies • Numerical study • Conclusions
Introduction • To plan and control Manufacturing/Remanufacturing operations simultaneously, new strategy had been investigated. • Requires to offer a higher level of coordination between manufacturing and remanufacturing operations.
IntroductionPUSH-strategy (sm, Qm, Qr) PUSH-strategy • Remanufacturing starts when the inventory of remanufac- turable contains Qr. • All Qr product enters remanufacturing process. • Manufacturing starts when the serviceable inventory position drops to sm.
IntroductionPULL-strategy (sm, Qm, sr, Sr)PULL-strategy • Remanufacturing starts when the serviceable inventory position is at or below sr. • Manufacturing starts when the serviceable inventory position drops to the level sm (sr)
Previous Literatures • E. van der Kruk(1995) : a much simpler PULL system that include periodic review, zero fixed ordering costs, and equal non-stochastic lead-times. • M.Fleischmann, E. van der Laan(1997) : same author, different assumptions with respect to demand process, return process, cost structure.
System assumptions(1/3) • One product is returned per return occurrence, and that the return process is Poisson with rate R. • The remanufacturing lead-time is modeled by a discretely distributed stochastic variable Lr. • The manufacturing lead-time is modeled by a discretely distributed stochastic variable Lm. • At each demand occurrence, one product is demanded. • The demand occurrence are assumed to follow a Poisson process with rate D.
System assumptions(2/3) • Total expected operating costs under strategy (.) is
System assumptions(3/3) • manufacturing lead-time Lm : • remanufacturing lead-time Lr : • • • •
Analysis for the (sm,Qm,Qr)PUSH-strategy •Steady-state situation :
Numerical Study-Base-case scenario • Remanufacturable inventory costs=0.5 • Serviceable inventory costs=1.0 • Backordering costs=50.0 • Expected (re)manufacturing lead-time=2 • Demand intensity=1.0 • Return intensity=0.7
Numerical study-The effects of lead-time duration(1) (a) function of Lr=0 (b) function of Lm=0 An increase of manufac- turing lead-time result in much larger cost increases than equivalent increases in remanufac- turing lead-times.
The reason for previous results • Our PUSH- and PULL-strategies give priority to remanufacturing and use manufacturing as a last resort to avoid backorderings. • So, sm is lower than or equal to the sr. • During an outstanding manufacturing order, the probability of backordering’s occurrence is larger than during an remanufacturing order. Longer manufacturing lead-times require higher safety stocks.
The arguments for previous results • The cost decreasing effect of an increase in remanufacturing lead-times occurs in particular when Lr is small compared to Lm. • sm at a relatively low level to minimize holding costs. This will result in high backorder costs • sm at a relatively high level to protect against the high manufacturing lead-time. This will result in high holding costs. The above indicates that our PUSH- and PULL-strategies are not very efficient if Lr is much smaller than Lm.
The effects of lead-time variability •Assumption : Bernoulli distributed lead-times Parameter l, Pl : l-period lead time will occur(with probability Pl), or instantaneous delivery with 1-Pl .
Observation for results • An increase in the variability of manufacturing lead-times results in lower total expected costs.(in the case of R > 0) If R=0, this effect will not happen(By Song(1995)) • If R > 0 and remanufacturing lead-times are deterministic, the net costs decrease.
Sensitivity analysis(1/3) 1.Demands and returns -When R << D, the influence of changes in the duration and variability of remanufacturing lead-times on cost are limited, since most demands are satisfied by manufacturing -If R D, costs are rather insensitive to changes in the variability and duration of manufacturing lead-times, since most demands are satisfied by remanufacturing.
Sensitivity analysis(2/3) 2. Cost structure and cost parameters -Work-in-process costs have been disregarded in this study. -Cost decreasing effect of an increase in remanufacturing lead-times may be reduced, but will not affect lead-time variability. -Non-zero fixed costs may influence the total expected operating costs.(refer next figure)
Conclusions • Although these strategies are non-optimal, they have the advantage that they are easy to implement and actually used in practice. • A numerical study shows that changes in the duration of manufacturing lead-times have larger influences on total expected costs than that of remanufacturing lead-times. • The numerical study also indicates that cost increases are more sensitive to a larger variability in remanufacturing lead-times than to that in manufacturing lead-times.
References • Moritz Fleischmann, Jacqueline M. Bloemhof-Ruwaard, …, 1997, Quantitative models for reverse logistics :A review, European Journal of Operational Research 103, 1-17