Management Case I
Base 3 Operators 3Ops + FD
4.5.3 Evaluation of use of the Standard Framework in Construction of the BiosynTModel
The BioSynT model was built to answer the specific capacity management question of how fast could the process be made to go and also to test the Standards Version 1 framework its ability to guide the construction of a model that meets the set requirement specifications. In order to understand and evaluate the value of using the framework to construct the BioSynT model a qualitative analysis must take place. In an ideal world, a quantitative analysis would also be performed however it is exceedingly difficult to quantify the comparison between the process of two construction methods. What can be offered is the feedback from the end users and the validation outcomes of data/trend verification, using the requirement specifications as a guide.
Intuitive to user
According to the guidelines outlined by Valentin and Verbraceck (2002), a model can be made intuitive to the end user if it is constructed in such a way that the
‘Interactions between model parts...should represent interactions in the real system’,
‘Use concepts that represent functionalities as found in reality and that can be used for visualisation purposes’ and ‘Visualise a system in such a way that complexity is reduced but the essential processes are still shown’. These statements simply mean that in order for the end user to fully understand the model within a short space of time, the system elements should be easily identifiable within the model and that any model elements which are non system specific should be hidden and ideally minimised so that the level of complexity is reduced. Following these guidelines, the BioSynT model was constructed using functional decomposition and therefore the main block constructs visible to the user are the main system operations.
Furthermore, the hierarchical nature of the Extend platform allows for model elements to be hidden away within blocks, helping to reduce visible complexity. This feature was heavily utilised in the BioSynT model.
Relevance and Ease of Data Input/Output
The BioSynT model uses the SDI link offered by the Extend platform whereby the database source sits within Excel. All of the parameters needed to run the model have been pre set into the database meaning that the user need not access the input Excel
file. The reason for limiting the access requirement is due to the fact that the Excel database would have to be manually exported and imported in order for any changes to be applied in the model database, no matter how small those changes.
Furthermore, the SDI link is designed with a rigid structure meaning that in order for Extend to be able to read the tables correctly any structural changes such as an extra row or field in any table would have to be manually noted within the Excel file before exporting. These features are deemed as shortfalls because they increase the necessity of user input where knowledge of the model and database is required. As such the major variable parameters have been placed within the model using the intuitive notebook feature so that the user can easily access them. However, the nature of the database and the limitations of the notebook function mean that the degree of variability is reduced, with parameters such as cycle time only being changeable within the relatively complex database structure.
Maximised reusability and sustainability
Table 4.7 shows those parameters easily accessible to the user. The model inputs clearly shows the limited number of parameters made truly variable, with all other parameters accessible only through the database. As stated earlier, these latter ones can be changed by the user but only if they have knowledge of the SDI function.
Although the model has been set up in such a way that the user can easily change the number of equipment and labour, further scenarios analysis would need access to this database and possible structural changes to the model. For example, a change in the process sequence would need alterations to both model and database. Although rather difficult to quantitatively score the BioSynT model in terms of reusability and sustainability, it is possible to qualitatively state that these requirements are limited.
Table 4.7 Summary of inputs and outputs to BioSynT model
Model Inputs (Notebook) Model Outputs (Excel) Resources - Number of labour available
- Number of equipment
Short Run-time
The run time of a model is important particularly when the number of runs required increases. For example, if a model were to be run only once, then even a ten minute run would not be too significant. However if 1000 runs were required (during a Monte Carlo analysis for example), even a two minute run time would result in two thousand minutes or 33 hours of simulation time. If only four scenarios were then run (as in the case of the BioSynT case) then this would mean 5.5 days of simulation run time. This would not only be draining on CPU capacity but would make the use of the model as a decisional tool somewhat inefficient. Therefore a short a run time as possible, preferably less than a minute, would be ideal. Without an equivalent BioSynT model to perform a direct comparison with, it is rather difficult to quantify any improvement in run-time. However, during the review of existing models it was found that most took several minutes to run only once. The BioSynT model takes approximately a minute and therefore it can be deemed to meet this particular requirement to a certain degree. It is important to note here that discrete event models, due to their nature, take longer to run than purely algorithmic based models and therefore, although run-times of only seconds would be preferable, the complexity of discrete event makes it more unlikely if larger systems are being modelled.
Minimised development time
The major shortfall in the construction of the BioSynT model was the development time; although in theory it should have taken a matter of months in total, the design to final documentation stage took over a year to complete. One major factor was the model rebuild which took place months into project commencement due to the realisation that the model did not quite meet the scope. Section 3.5.2.1 explored this in greater detail.
4.6 Conclusion
The BioSynT case was used to demonstrate the ability of a model, built using the standard, of being used as a decisional tool. A deterministic study was first carried out in order to determine those parameters whose variability would significantly
impact the output metric, completion time for the processing of forty batches, thus identifying the primary bottlenecks within the process. It showed that the biggest impact was seen with the frequency of freeze dryer misalignment, lengthening the process completion time when increased.
A stochastic study was then carried out using the results of the deterministic analysis, looking at the impact of different scenarios combined with process uncertainties to determine how these would affect the running of the process and to highlight any shifts in bottlenecks. A cost analysis of the scenarios was also carried out to add a further dimension to the study. The outcome of this was that in order to reasonably reduce facility operation time whilst injecting minimum investment the best scenario would be to increase the number of operators by one, giving a reduction in overall completion time of almost 3 months.
Finally the BioSynT case study was used to implement the Standard Framework 1, testing its ability to guide the construction of a manufacturing capacity management model capable of meeting the requirement specifications stated under the standard. A qualitative analysis showed that these were not sufficiently met, with rigidity in the method of model construction and the approach used, leading to a less than adequate reusability and sustainability, a relatively long run time and a far greater development time than desired (with time and cost efficiency as measures).
137