THE SIMULATION MODEL FOR THE PROPOSED INJECTIONS
4.4 THE INPUT PARAMETERS
4.4.1 THE INPUT SHEET AND LOGIC OF THE SIMULATION MODEL
To change key parameters in order to create different scenarios, an input sheet was developed for the simulation model. An example of the complete input sheet is shown in table 10. The most important input parameters and also the most important conceptual logic of the simulation model will be discussed in the following narrative.
Attributes:
In order to ensure total randomness in the simulation of the proposed management model, a probability distribution for a possible score (0-3) was assigned to the applicable attributes for any given arriving opportunity. Throughout the simulation, a relative weight representing the importance of each attribute can then be used as a multiple at each decision filter. The weighted scores for all the attributes are then added for a specific opportunity.
The value of an opportunity is then compared to the cut-off limit for the specific review filter (cut-off limits are discussed later in this section). The score of the value opportunity will determine whether it will move into the next value buffer or be discarded. Table 10 shows all the attributes used during the simulation and an example of the weight each attribute could have at each filter review.
Time between ideas and idea value:
The input sheet has the option to assign different triangular distributions to the arriving rate of opportunities and the value of the opportunities at the start of the simulation run. Hours were used as constant time metric throughout the simulation process.
As was highlighted in chapter 3, the frequency of key events upstream of the value buffers will have a significant impact on the time it takes for the value buffers to replenish lost value. During a simulation run the model will manipulate replenishment time according to the status of the value buffers.
0 1 2 3 Weight
filter 1
Weight filter 2
Weight filter 3 Prelim
1 Technical relevance 0.05 0.1 0.2 0.65 0.2 0.14 0.33
2 Research direction and balance 0.05 0.15 0.2 0.6 0.2
3 Timing 0.1 0.6 0.25 0.05 0.5 0.14 0.33
4 Stability 0.35 0.3 0.2 0.15 0.14
5 Position factor 0.15 0.3 0.5 0.05 0.14
6 Market growth 0.1 0.1 0.75 0.05 0.14
7 Marketability 0.1 0.3 0.5 0.1 0.2 0.14
8 Producibility 0.05 0.15 0.65 0.15 0.2 0.5 0.33
9 Financial 0.1 0.6 0.25 0.05 0.2 0.14
min mode max
Initial time between ideas 8 24 145 Changes time between ideas Yes
Idea value 0 70000 100000
Filter review 1 cut-off limit 2.3 Filter review 2 cut-off limit 1.8 Filter review 3 cut-off limit 1.5
As proportion of maximum idea value
Final buffer - critical limit 0.5
Final buffer - lower limit 1
Final buffer - upper limit 2
min mode maks
Filter 3 prep activity delays 80 100 160
Filter 2 & 3 meeting frequency 170 176 200
Review meeting 1 frequency 170 176 200
Review meeting 2 frequency 170 176 200
Execution delay 1080 2160 3240 90%
Attributes
Percentage active resource participation
10%
Table 10: The input sheet developed for the simulation model.
Cut-off limits at decision filter meetings:
Each decision filter has a cut-off limit assigned to it. Any given opportunity’s score will be judged against the assigned cut-off limit for that specific filter review. If the value of the opportunity is less than the chosen cut-off limit, the opportunity will be discarded. If the value of the opportunity is greater than that of the cut-off limit, the opportunity will proceed onto the next value buffer and stage. This process will continue until the opportunity is given the final Go decision for project development, where it will move into the final value buffer waiting to be pulled into the value delivery process by a key resource.
The value of the cut-off limits during all the simulation runs were set in such a way that the waste stream at the initial filter review is very high (not less than 80%) as suggested by Wheelwright and Clark [52] when they discussed their development funnel concept. At the next filter review, the waste stream decreases dramatically. At the final filter, the cut-off limit is such that the failure of an opportunity would be the exception. The value of the cut-off limits in this study will be kept the same throughout the analysis process.
Buffer sizing and buffer management:
The importance of proper buffer sizing and the management of these buffers for a productive management system came to the fore during the discussion of the process view and system’s view for the management model in chapter 3.
The simulation model is developed in such a way that the final value buffer can be sized as a proportion of the maximum value for an arriving opportunity. It is important to do active value buffer sizing (over various simulation runs) in order to achieve the optimum value buffer size, as the size of the final value buffer will most certainly have an influence on the lead time of the projects in the management system and also play a role in the reliability of production. It is therefore a very important parameter to change.
The areas for buffer management (red/amber/green zone) can also be sized by the simulator. The management of the value buffers is a built-in feature of the simulation model.
It should be noted that the reprioritization of projects within the value buffers are not part of this simulation model and could form part of a future study. In this simulation model projects are pulled from the downstream value buffer and then immediately released from the upstream value buffer on a first-in-first-out basis, as a result of the pull trigger as described in chapter 3. In the simulation model, once a project is pulled from the final value buffer, the value in the buffer will only drop once the specific project has been realized at the end of the value delivery process.
The replenishment time of the value buffers is a function of the frequency and speed of key events upstream of the value buffers. Short lead times for events and a high frequency of decision filters upstream of the value buffer, shorten the time to replenish the lost value of the value buffer. This will have the effect that a smaller value buffer would be appropriate. This concept was also discussed in chapter 3.
Frequency of key meetings:
This is one of the most crucial parameters, as the frequency of events taking place can have a significant influence on the replenishment time of the value buffer and the availability of project work in the value buffers, to ensure the key resources are not starved for work.
Resource scheduling - Percentage active participation:
The percentage active limited resource participation in the value acquisition and value delivery phase will hold the key for increasing the system’s productivity as discussed in chapter 3. Therefore it is important that this parameter can also be changed to create different scenarios.
The most important resource to monitor during a simulation run, is the constraining (key) resource.
In this study, the development engineer has been identified as the constraining resource and will therefore determine the capacity of the whole system. The resources utilized in both the value acquisition and value delivery process and their tasks are shown in table 11.
ASSIGNED TASKS TO RESOURCES
Resources Value acquisition phase Value delivery phase Development engineers Technical feasibility Production development Divisional manager 1, 2, 3, 4, 5 Filter review 1 & 2 1st & 2nd review meetings
Management Filter review 1 & 2 1st & 2nd review meetings Manufacturing engineers Competitor analysis, financial
viability
Conceptual and final designs
Marketing personnel Pre-lim marketing Marketing
Table 11: Limited resources working on both the value acquisition and value delivery process.
After the input parameters have been defined, it is crucial that the data obtained from the steady state simulation run is accurate and representative of what truly happens during the project management process. To ensure this, the warm up and run length of the steady state simulation should be characterized in such a way that the output analyzer of Arena will only contain relevant data of the simulation run.