Improving productivity of automated tissue converting lines: an empirical model and a case study

Abstract

This study is focused on minor stoppages as sources of variance within automated production lines in industrial environments, and it suggests the handling of the problem through a combined phenomenon–mechanism analysis and simulation approach. The resulting seven-step methodological pattern has been applied to a real-life case study of a tissue converting line: the product type and the machine speed have been identified as causal factors for minor stoppages and the wrapper machine has been chosen to exemplify the methodology.

 In turn, it consists of four sub-steps: (i) identifying operating principles; (ii) identifying operating standards; (iii) identifying interacting elements; (iv) quantifying physical changes involved.

Results point out that the speed of the wrapping machine–which allows the daily throughput of line to be maximized–changes when products change, thus highlighting a trade off between minor stoppages and wrapper speed. However, in some other cases, minor stoppages are more detrimental than the machine speed is useful.

Keywords:

• Productivity
• simulation
• automated systems

Acknowledgements

Dr Eng. A. Calderaro, former student of Politecnico di Milano and Prof. W. Kersten of Technische Universität Hamburg-Harburg are gratefully acknowledged for having provided many helpful suggestions.

ROBERTO CIGOLINI is Associate Professor at the Department of Management, Economics and Industrial Engineering of Politecnico di Milano and lecturer in Production & Logistics Management. He graduated cum laude in Production and Management Engineering at Politecnico di Milano in 1994. From 1999 to 2002 he was Co-Director of the MBA programme at the MIP Business School and now he is Co-Director of the Facility & Property Management Master Course. He is also a founding member (2001) of the Technical Committee on Semiconductor Factory Automation (IEEE Robotics and Automation Society) and a member of the National Maintenance Commission of UNI (the Italian branch of ISO). His main research interests are primarily related to the production planning and control techniques and supply-chain management, to the evaluation of technology-related intangible resources, to the intellectual property strategies, to the facilities, property and assets management and to the project management technique, mainly in the area of modularization.

TOMMASO ROSSI graduated in Production and Management Engineering at Politecnico di Milano in 2000. From 2001 to March 2004 he attended the PhD course in Industrial Engineering at the Politecnico di Milano. Since October 2002 he has been Researcher at the Department of Industrial Engineering of Universitá Cattaneo, LIUC di Castellanza, where he runs the courses of Operations Management and Supply-chain Design. He is a member of the National Association for Industrial Plants and the National Association for Quality. His research interests concern production planning, network design, simulation and hybrid production systems.

Notes

 In turn, it consists of four sub-steps: (i) identifying operating principles; (ii) identifying operating standards; (iii) identifying interacting elements; (iv) quantifying physical changes involved.

The maximization of the likelihood function can be conducted by means of specific econometric software tools (e.g. Rats™), or through the Excel™ solver tool, which has been employed to obtain the results presented in section 4.

That is: (i) observation and comprehension of the phenomenon; (ii) physical analysis; (iii) identification of the conditions for the abnormal event; (iv) cause-and-effect relationships between conditions and production inputs.

Arena™ contains also a statistic toolbox which allows several probability functions (including the Weibull one) to be very easily modelled.

The effects of the output-conveyor-full status (i.e. the additional constituent condition) have been taken into account directly in the structure of the simulation model; see Appendix 2 for further details.

A sensor detects a roll turned over on the wrapper-bearing plate and it stops production.