Graduate School of Business

1. Aluminium Smelting and Communities of Practice by Miles G Nicholls Graduate School of Business

2. This presentation is based on a paper delivered to the 36th Meeting of the Decision Sciences Institute in San Francisco, USA, November 19th – 22nd, 2005: “The Role of Communities of Practice in Determining Best Practice in Production Processes Involving ‘Alchemy’ – A Mixed-mode Modelling Approach” Miles G Nicholls Barbara J Cargill Graduate School of Business Faculty of Business and Enterprise RMIT University Swinburne University of Technology Graduate School of Business

3. The Portland Aluminium Smelter  Initially a consulting project  The aim was to model the entire smelter with a view to determining ‘best practice’ (commenced 1988 – finished 1998)  The smelter is an approximately $2b plant, a green-field 250 acre site near Portland, Victoria, Australia (largest smelter in Southern Hemisphere)  Unique management structures operating and providingan unusual occurrence of a ‘real’ bi-level model. Graduate School of Business

4. Graduate School of Business Problem Summary  The smelter takes in raw materials (primarily alumina, coke and pitch) and places them in a “bath” of chemicals in a special “pot”. Large quantities of electricity are then passed through the bath via anodes (blocks of carbon) suspended in the bath (the cathodes are underneath). The molten aluminium then collects at the bottom of the pot and is syphoned off.  The task was to model the entire plant in order to “maximise production of aluminium and keep ancillary plant activities to a minimum”.

5. The Site Graduate School of Business

6. Graduate School of Business The Site

7. Graduate School of Business The Process

8. The Monthly Mathematical Model Graduate School of Business

9. The Monthly Mathematical Model Only two variables, kilo Amperes (kA) and Setting Cycle (SC) (1.1) relates to all kA based raw material consumption (1.2) relates to all SC based raw material consumption (1.3) ensures the anode will not be ‘overused’ (1.4) relates to kA and SC based consumption of Coke (1.5) and (1.6) are maximum and minimum limits of ‘spent’ anodes that are used in the making of new anodes kA and SC are typically 300 and 28 respectively The multiple month model more complicated and involves more variables. Graduate School of Business

10. The ‘Behind the Scenes’ Link Theoretically, the production of aluminium is calculated thus: ATt = 0.008052 kAt PDt (2) Estimated output is arrived at as follows: AEt = 0.008052 kAt CEt-1 PDt (3) where: CEt-1 = ASt-1 / ATt-1 (4) and ASt is the actual aluminiumsiphoned from the pot Note that AEt andCEt-1 are averages over all pots and that every constraint (other than (1.2)) contains CEt-1 Graduate School of Business

11. Real World Problems The Current Efficiency (CE), a key parameter, has three major problems :  it is a lagged estimation (CEt-1)  it is variable and often highly unpredictable on a pot to pot basis (limited success in modelling pots in a ‘real world’, ‘on- line’ and in a real time mode – thus no accurate CE)  it is inaccurate because the actual production of aluminium cannot be measured (other than in a laboratory pot) Therefore the solution of the model provides a “best practice” solution that could be some distance from the “optimal” Graduate School of Business

12. The ‘Alchemy’  Pots are very individual in their behaviour. If they run too hot, or drop to too low a DC voltage they will become less productive and may take many days to recover  Pots often suffer an ‘anode effect’ which is a disaster for a pot. This effect sees molten aluminium behaving like a wave and this disrupts productivity, often for many days  Power is frequently turned off by the suppliers on agreed ‘power outages’ arrangements and this also destabilises the pot  All these problems are left to the ‘Operators’ to deal with in their own way based on their ‘experience’ and ‘shared knowledge’. This is the ‘alchemy’. Graduate School of Business

13. Communities of Practice  Aluminium smelting has been around since the late 1890’s and much “folk lore” has built up over that time as to how to deal with pots  To some extent the smelter’s Operators had already formed a ‘Community of Practice’, but it was very tenuous and fledgling  With encouragement, such a Community of Practice could potentially save millions (a 1% increase in CE could mean a $2m - $5m increase in the bottom line depending on the size of the smelter)  Communities of Practice are not only limited to ‘pot rooms’ in a smelter, but it is the point of biggest impact Graduate School of Business

14. Communities of Practice Communities of Practice (C of P) are essentially informal groups of people in a single organization (as in the case of ‘commercial in confidence’ operations of smelters and other manufacturing entities) or in more ‘common knowledge’ areas, across organizations as well. As Louis (2005), Davies et al (2003) and Burk (2000) indicate, the C of P are founded on common knowledge or common work tasks where the coming together of people to share stories and discuss work practices (i.e., sharing explicit and tacit knowledge) gives support, knowledge and a sense of belonging to people. These interactions can occur in a real or virtual sense depending on the nature of the organization and whether the C of P exists beyond one such work site. Graduate School of Business

15. Communities of Practice  Communities of Practice (C of P) in this paper are classified as a hybrid of ‘best practice’ and ‘knowledge stewarding’ (Vestal, 2003)  Membership of C of P is not necessarily fixed but may vary considerably (as will its leadership)  The aluminium smelting industry is ideally suited to the informal emergence of C of P which are essentially (in this instance) a sharing of experiences of the pot room Operators  Anecdotally, at least one C of P was in existence within the Portland smelter (Urpani, 1996) Graduate School of Business

16. Communitiesof Practice  It is clear, where there exists aspects of the production process (i.e., the sub-process) that are not well understood and for which a ‘model’ can’t be constructed (‘soft’ or ‘hard’) in order to determine the ‘best practice’ operation of the process, a combined approach of ‘hard’ modelling coupled with a C of P associated with the sub-process will provide the focus, knowledge improvement and long term better understanding of the operations  Further, it should be noted that a critical value of the C of P is the learning loop for the perpetuation of knowledge in the operators over time. Unless there is some process in place that nurtures the C of P and also attempts to bring the tacit knowledge into more explicit forms, there is a risk to the enterprise of knowledge loss over time Graduate School of Business

17. Graduate School of Business Mixed-mode Modelling

18. The Solution Methodology  The solution approach used for this problem is illustrated in Figure 3 and is termed ‘mixed-mode modelling’  Mixed-mode modelling in this application sees the combination of hard and soft models ‘solved’ by a heuristic involving C of P and non-linear bi-level programming (for the multiple month model)  Employing this approach will not only see an increase in the ‘bottom line’ of the business but also a retention of the bodies of explicit and tacit knowledge possessed by the Operators and thus the company Graduate School of Business

19. Graduate School of Business The Solution Methodology

20. Conclusion  Many problems in OR/MS appear ‘crisp’ and their parameters seem robust and meaningful  However, in many industries, the key process parameters and indeed parts of the processes themselves are not really understoodor able to be reliably estimated or codified respectively. The aluminium smelting industry is such an example  This paper suggests a mixed mode modelling approach (combining Communities of Practice and mathematical programming) to arrive at best practice and also perpetuate the existence of essential explicit and tacit knowledge within the aluminium smelter Graduate School of Business

21. Conclusion – Gains A decrease in the variability of a pot’s production An increase in the quality (purity) the aluminium produced Better behaved pots and a consequential more even temperature leading towards less build up of bath around the sides of the pot leading to a more accurate estimation of a pots production A reduction in pot disturbances leading to the greater reliability is any estimates of production obtained from it Consequential increases in current efficiency (which is also a more reliable ‘guestimated’ figure) Providing a more realistic basis for determining best practice from the solution of the macro model of the smelter Graduate School of Business