Advanced Techniques in Capital Goods Industry Research

1. Research Overview Christian Hicks http://www.staff.ncl.ac.uk/chris.hicks/presentations/presin.htm

2. Research Interests Main areas: • Simulation / modelling of manufacturing systems • Scheduling / planning and control • Supply chain management • Manufacturing Layout Other areas include: • IT implementation • Company classification • Manufacturing strategy • Web-based teaching • Data / statistical analysis • Business Process Analysis • Benchmarking in the semiconductor industry

3. Capital Goods Companies: Generic Issues • Products are highly customised and are produced on a make, or engineer to order basis. • Production facilities include jobbing, batch, flow and assembly systems as well as construction. • Lead time reduction increasingly important. • International competition: effective and efficient use of resources is very important. • Complex and dynamic supply chains. • Product offering broadening to include service elements. • Involves civil, mechanical and electrical engineering.

6. Simulation of Manufacturing Systems

8. Key Features • Large scale model allows whole manufacturing facilities to be represented. • Models facilities, products, processes, layout and planning and control systems. • Many product families can be represented with shallow, medium or deep product structure. • Hierarchical description of products and resources. • Allows variety of planning and control methods to meet local requirements. • Integrated with scheduling and layout optimisation tools. • Comprehensive stochastic modelling.

9. Schedule Optimisation

10. Schedule optimisation using Genetic Algorithms

11. Component Product 1st Operation Assembly Initial Schedule

13. New schedule from GA

14. Stochastic Planning Methods • Developed methods that either meet a service target or minimise the combination of earliness and tardiness costs. • Investigated approaches for infinite capacity, finite capacity and dynamic scheduling cases. • Planning methods investigated and validated through simulation modelling.

15. Manufacturing Layout • Clustering • Matrix-based methods • Similarity coefficient methods • Optimisation • Genetic Algorithm • Simulated Annealing

16. Dendogram based upon Similarity Coefficients

17. Genetic Algorithm Procedure

18. Placement Algorithm

19. Initial Layout

20. Resultant Brown-field layout

21. Total rectilinear distance travelled vs. generation (brown field)

22. Supply Chain Management

23. Supply Chain Management • Modelled business processes using SSADM • Company structures range from vertically integrated to project integrators that outsource all manufacturing. • Important factors include: available capital, risk, potential utilisation of plant, capabilities, flexibility. • Three stages of interaction with customers: marketing, tendering and contract execution • “Normal” / “radical” design • Functional vs. technical specifications • Procurement decisions made by: customers, designers, procurement departments

24. Summary of contributions • Planning, control and layout problems in capital goods companies. Outcome: first large-scale simulation model of manufacturing in capital goods companies • Scheduling complex products in deterministic and stochastic environments. Developed first optimisation techniques. • Layout analysis and optimisation. Developed integrated tool. • Supply chain management in capital goods companies. New models proposed and linked to strategic issues.

25. Classification

27. Simulation

30. Representation of Resources

31. Representation of Products

32. Stochastic Simulation • Several random number generators: Knuth, Wichman & Hill, SunOs. • Normal [polar form of Box-Muller (Marsaglia and Bray 1964); Beta (Press, et al. 1989, p188), Gamma (Press, et al. 1989, p228), Poisson (Press, et al. 1989, p230) as well as Log normal, Multi-modal, Exponential, and empirical (based on historical data). • Full / fractional factorial designs • ANOVA / Regression analysis

33. Scheduling

34. Chromosome representation

35. Crossover Operations

36. Mutation Operations

37. Fitness function Minimise :  Pe(Ec+Ep) +  Pt(Tp) Where Ec = max (0, Dc - Fc) Ep = max (0, Dp - Fp) Tp = max (0, Fp - Dp)

38. An Example of Production Plan

39. Layout

40. Rank Order Clustering (King 1980)

41. Case Study • 52 Machine tools • 3408 complex components • 734 part types • Complex product structures • Total distance travelled • Directdistance 232Km • Rectilinear distance 642Km

42. Total rectilinear distance travelled vs. generation (green field)

43. Resultant green field layout Note that brown field constraints, such as walls have been ignored.

44. Supply Chain Management

45. Collaborating Companies • NEI Parsons • AMEC Offshore • NEI International Combustion • Clarke Chapman • Wellman Booth • Control Systems • Reyrolle (VA Tech)

46. Supply Chain Management • Identified the characteristics of the companies in terms of products, processes, markets, level of outsourcing etc. • Investigated buyer/supplier relationships in terms of supplier base, strategic alliances, partnership and single sourcing agreements etc. • 3 stages: marketing, tendering, contract execution • Physical / non-physical processes, • Differing levels of vertical integration • Procurement often reactive rather than strategic

47. SCM (continued) • Majority of controllable cost committed at the design stage. • Normal / Radical design • Established / ad-hoc business processes • Product offering broadening – shift from just hardware to retrofit, service and operations. • There are high levels of uncertainty and sparse knowledge, particularly at the tendering stage. • Tendering is often subject to severe time pressure and resource constraints.

49. Company X - Context Diagram a a Customer Customer Contract ITT Awarded Progress Tender Report Company X ITT Quote Order b b Supplier Supplier