As biotechnology organizations have successfully launched new products, the challenges of producing adequate quantities have
grown. Many companies are now dealing with multiproduct manufacturing facilities and pushing the limits of their capabilities.
One result of this complexity is a loss of production capacity due to inefficiencies.
Many factors contribute to inefficiencies, such as scheduling conflicts in production or supporting equipment, inadequate
availability of production resources (both human and machine), lack of available raw materials or subcomponents, and miscalculations
of the true capabilities of facilities. In many cases, production line inefficiencies do not result from weaknesses in day-to-day
production scheduling, but from the original assumptions built into planning models.
Often companies lack the data to support a systematic capacity analysis of all their production areas. Equipment- and labor-capacity
planning exercises usually are inaccurate because of pressure to provide next year's budget forecast. They are not geared
to provide the valuable information that will drive future planning and scheduling needs. In many cases, broad assumptions
are made about the true production bottlenecks, process efficiency figures, and process times that will be realized in manufacturing.
The result is production lines that are not appropriately sized or managed to optimize the bottlenecks, that do not account
for the variability in day-to-day production needs, and that fail to maximize the use of critical plant equipment.
AVAILABLE PLANNING TOOLS
Planning tools provide a solution, as long as you use the right tool for the right task. Capacity-modeling tools fall under
two broad categories, static and dynamic.
Static modeling is the most common and the easiest to program. Static modeling can be very effective as a first-pass analysis tool where
future manufacturing requirements are still wildly variable. The assumptions on a variety of planning metrics have a time-independent
perspective. For example, with static modeling, one uses monthly demand to calculate the labor and equipment needed to support
the required volume.
Dynamic (simulation) modeling, although more complicated to build and use, provides a more realistic tool for planning a production area. Dynamic models,
by definition, are time dependent; they analyze how systems or areas will react to changes over time. Instead of examining
the production line and resources on a monthly basis, the model simulates individual lots moving through the line based on
a realistic production schedule. This model can then provide a picture of production during the month, including achievable
cycle times, sources of delays, inadequate production staffing levels, and shifting bottlenecks.
Figure 1. Modern simulation tools allow visual representation of a production operation with reasonable effort. Animation
is standard in most available models. Imagine That, Inc. designed this model in Extend.
Newer software packages make creating dynamic models simpler than before and even offer animation as a standard feature. Also,
since this type of modeling primarily supports planning activity (and not decision-making activities impacting product quality),
it does not need to conform to 21 CFR Part 11. Figures 1 and 2 show examples of a build model as well as sample output capabilities.
IS SIMULATION RIGHT FOR ME?
When choosing between simulation modeling and a static planning tool, the first thing to consider is your needs. Simulation
models offer many benefits, including the ability to conduct time-sensitive analyses, provide for variability in workload
fluctuations, and help with bottleneck identification (including the ability to analyze shifting bottlenecks). If a company
is facing decisions that may benefit from such detailed information, then it should seriously consider using a simulation
However, if a company is simply conducting a first-pass capacity analysis in a straightforward environment where the implications
of inadequate resources are not severe, a static model may be sufficient. Of course, if you already have a static model in
place and you are not getting the information you need, this may indicate that simulation is required.
Another factor to consider is the nature of the process. Simulation requires that clear rules be in place for product routing,
resource usage, processing times, and other parameters. If the process you are modeling has too many open-ended choices (regarding
what equipment is used in the process or when certain actions are performed, for example), you should consider using a simpler
Finally, as with any model, the availability of data is critical to its success. A good simulation requires extensive and
accurate data for standard processing times, yield rates, production volumes and mix, and other input parameters. Since a
main benefit of simulation is its ability to analyze the effects of real-life variability on resource requirements, service
levels, and other performance aspects, it is important that reliable data be available to determine the averages for each
parameter, as well as the standard deviation and type of distribution. Table 1 highlights some of the differences between
static and dynamic modeling.
CREATING A SIMULATION MODEL
Simulation is a complex and time-consuming process. Management must be patient and allocate time for design because reworking
and redesigning the model after it is already in development can add significant time to the development process.
Table 1. Static vs. dynamic models
The design process should be seen as a cross-functional exercise. All key stakeholders providing inputs to the model will
be affected by it. This collaborative approach to development can help ensure that buy-in exists for the model and that the
model will accurately reflect the needs of the impacted groups.
Since simulation is a way to mimic reality, the first step to creating a simulation model is to define that reality. This
can typically be done through process mapping; process maps focus on the flow of products, people, and materials through the
operation. For each step of this flow, determine model inputs and decision criteria that impact product routing, resource
usage, yield rates, and other performance parameters. Keep in mind that during this mapping process, companies often identify
a series of improvements to their operation that can generate significant benefits; the designed model may need to account
for these constant improvements to the process map.
When mapping the process, distinguish between physical movement and logical process transitions. For example, a product may
"move" from a labeling operation to an inspection step. However, if both steps take place in the same room, there may be no
physical movement involved. Nevertheless, it is important to identify the logical transition, or movement, of the product
from one process step to the next. Whether you consider this logical transition to be the beginning of another step or part
of a physical movement will be dictated by how much of an impact that distinction has on labor and equipment resources and
the level of detail you desire fromthe model.