1. © C.Hicks, University of Newcastle IGLS06/1 Laissez-faire or full control? An evaluation of various control strategies for companies that produce complex products with stochastic processing times Christian Hicks, Fouzi Hossen and Art Pongcharoen http://www.staff.ncl.ac.uk/chris.hicks/presindex.htm

2. © C.Hicks, University of Newcastle IGLS06/2 Dispatching rule literature Majority of work has focused upon small problems. Work has focused upon the production of components, mostly in job shops. Minimum set-up, machining and transfer times have been neglected. Deterministic process times have been assumed. The same rules have been used for all resources

3. © C.Hicks, University of Newcastle IGLS06/3 Capital goods companies Design, manufacture and construction of large products such as turbine generators, cranes and boilers. Complex product structures with many levels of assembly. Highly customised and produced in low volume on an engineer-to-order basis.

4. © C.Hicks, University of Newcastle IGLS06/4 Typical product

5. © C.Hicks, University of Newcastle IGLS06/5

6. © C.Hicks, University of Newcastle IGLS06/6 Case Study 52 Machine tools Three product families competing for resource (main product, spares and subcontract) Complex product structures

7. © C.Hicks, University of Newcastle IGLS06/7

8. Load on machines in rank order

10. © C.Hicks, University of Newcastle IGLS06/10 Experimental design

11. © C.Hicks, University of Newcastle IGLS06/11

12. © C.Hicks, University of Newcastle IGLS06/12

13. © C.Hicks, University of Newcastle IGLS06/13 Tardiness (T) = completion time – due time (for completion time > due time) Tardiness (T) = 0 (for completion time  due time) Performance Metric

14. © C.Hicks, University of Newcastle IGLS06/14 Figure 3 Mean tardiness (days) for products vs. number of resources under close control (LSF dispatching rule)

15. © C.Hicks, University of Newcastle IGLS06/15 Figure 4 Mean tardiness (days) for products vs. number of resources under close control (LSF dispatching rule

16. © C.Hicks, University of Newcastle IGLS06/16 Figure 5 Minimum mean product tardiness vs. number of resources under close control.

17. © C.Hicks, University of Newcastle IGLS06/17 Figure 6 Minimum mean component tardiness vs. number of resources under close control

18. © C.Hicks, University of Newcastle IGLS06/18 Conclusions Most dispatching rule research has focused upon job shops and has neglected other operational factors such as minimum setup, machining and transfer times and the data update period. Dispatching rule research has mainly investigated deterministic situations. This research has included complex assemblies, stochastic processing times and a multi-product environment. Mean tardiness for products reduces considerably when most highly utilised resources are placed under close control.

19. © C.Hicks, University of Newcastle IGLS06/19 Conclusions Most of the benefits arise with <= 10% of resources under close control. Mean tardiness for components relatively insensitive to number of resources under close control “Best” dispatching rule varies according to level and product family. Previous research has shown that the relative performance of dispatching rules is insensitive to the particular distribution used. This research shows that the relative performance of the dispatching rules is also quite insensitive to the number of resources under close control.