Customer Service in Pull Production Systems

1. Customer Service in Pull Production Systems Mark L. SPEARMAN Presented By: Ahu SOYLU

2. Outline • The Comparison of Pull & Push Systems • Overview of Pull Systems • Customer Service in Pull Systems • Customer Service in CONWIP • Comparison of Pull Systems with CONWIP • Conclusion

3. The Success of Pull Systems • The success of Japanese manufacturing systems has attracted attention • The system is a set of techniques known as Just-In-Time (JIT) • An integral feature of JIT is the use of pull shop floor control systems • There are still a number of issues that require study; such as customer service.

4. Push vs. Pull • In a push system customer service is measured with well known methods like the fraction of jobs on-time and the average tardiness. • A job is on-time if the time to complete a job is less than or equal to its lead time. • Tardiness is the positive difference between the completion time and the due date of a job (Tj=max{Cj-dj,0}).

5. Push vs. Pull • In a pull system, each process is both a supermarket for downstream processes and a customer to preceding processes. • A supermarket is a place where a customer can get • What’s needed • At the time needed • In the amount needed • Service measures are the probability of stockout, the expected time to fill demand, the expected backlog of orders.

6. MRP and Master Production Schedule are used to control production Input/Output control provides capacity check Controls throughput and measures WIP “The day is not done until every job has been completed” The due date of a new job is established by considering the current production quota and the current backlog of jobs Controls WIP and measures throughput Push vs. Pull

7. Push vs. Pull • A system employs push if it schedules the release of work a priori. • A pull system authorizes the release of work based on current plant conditions • A hybrid system involves aspects of both.

8. Two-card Kanban System

9. Benefits of Pull System • All workers can immediately see what work needs to be done • Excessive WIP is not pushed to the system whenever the system capacity is overestimated • It is easier to control WIP than output • When the system works well, there is no need to schedule production • Adapting the production environment to improvements is easier

10. Kanban is not for everybody • The conditions necessary for Kanban to work well are: • “Smooth” production involving a stable product mix • Short setups • Proper machine layout • Standardization of jobs • Improvement activities • Autonomation (autonomous defect control)

11. Modeling a Pull System • A simplified -one card- version of the Kanban system is examined • Assumptions • The cards move instantly so that when a demand arrives to an idle system, production will start at each station immediately • Last station has always parts • The standard containers are small and multiples of the container size

12. Modeling a Pull System • The service measure will be the expected time required to fill the demand. • The probability of stockout is not sufficient in terms of being a service measure because in a pull system, the longer the stockout occurs, the more likely to result in disruption of downstream processes. • N single server stations in series producing a single product.

13. Modeling a Pull System • Si~Fi: The service times for station i • {Sij}: set of service times • Dj: Times that demands in the form of kanbans from an external source are received • Demands are not iid • T(i,j): The time the jth container is sent from station I • mi: number of kanbans attached to the standard containers residing in the stockpoint of station i

14. Modeling a Pull System These equations represent the relations between the completion times, service times and the demands in the kanban system.

15. Measures of Customer Service • The time to satisfy the jth demand • The expected time to satisfy demand after the nth demand • Average time to satisfy demand

16. Some Results • T(i,j) is nonincreasing in mi • T(i,j) is increasing convex in Sik, k=1,2,…,j • τjis increasing convex in Sij

17. Stochastic Ordering in Kanban Systems • Consider two kanban systems k=1,2. Then Si(1)≤stSi(2) implies U(1)≤ U(2) • Consider two systems having processing times Sij(k)=θi(k) +ξj, k=1,2; i=1,…,N; j=1,2,…, whereθi(k) is a constant and ξj are iid random variables with zero mean. Then θ(1)≤ θ(2) implies U(1)≤ U(2) • For systems with processing times whose means represent a location parameter, faster processing times imply a better service.

18. Stochastic Ordering in Kanban Systems • These results deal with variability reduction. • Consider two kanban systems j=1,2. If Si(1)≥icxSi(2) , i=1,2,…,N implies T(1) (0,j)≥icx T(2)(0,j), j=1,2,… and U(1)≥icxU(2) • If Sij(1)~F and Sij(2)~G where F and G have the same mean and where F crosses G at most once and from below, then U(1)≤ U(2) • If Sij(k) ,k=1,2 ~N((k),(k)) and (1) <(2) then U(1)≤ U(2)

19. The Effect of Increasing Inventory Levels • Consider two kanban systems, j=1,2. Then mi(1)≥ mi(2); i=1,…,N implies U(1)≤ U(2) • Tradeoff of inventory vs. service • Although extra inventory improves service, it also reduces flexibility • Also, inventory hides major sources of variability and allows us to live with problems that could be eliminated.

20. The Effect of Increasing Inventory Levels • In a make-to-order push system with fixed lead time and constant throughput rate increasing WIP levels will degrade customer service. • Little’s Law:

21. CONWIP • Controlling WIP is more robust than controlling throughput • The fact that WIP is bounded is more important than the practice of “pulling” everywhere • CONWIP maintains a constant amount of WIP on each production line and does not pull at every station. • Line production quantities are measured in terms of standard parts. • CONWIP can be used when significant setup times exist

22. CONWIP • While the pull in kanban occurs between stations and is to replenish the particular part that has just been used, the pull in CONWIP is over the entire line and for the parts having the same routing. • CONWIP is similar to a closed queueing network. The flow time and throughput rate tend to be less variable than an equivalent open network with the same output.

23. Push-Pull-CONWIP

24. Modeling CONWIP These equations represent the relations between the completion times, service times and the demands in the kanban system.

25. Results • T(i,j)- Sij≥T(i-1,j) – Si-j, for i=1,2,…,N; j=1,2,…

26. Discussion • CONWIP behaves like a closed queueing network, kanban looks like a closed queueing network with blocking. • With the same card counts, CONWIP tends to have higher WIP • CONWIP can dominate kanban in terms of both service and average WIP • A simulation study is performed and it is seen that CONWIP exhibited customer service that was statistically superior.

27. Discussion • Both CONWIP and kanban limit WIP growth. • Implementation of CONWIP is simpler than kanban. • CONWIP lines can split into many segments, then kanban is a subset of CONWIP. • So, kanban cannot be superior to CONWIP.

28. Conclusion • Customer service in both kanban and CONWIP production systems is improved by: • Faster machines • Extra WIP • Less variable processing times • CONWIP systems have better customer service than do pure kanban systems.