pharma manufacturing simulation example

Modeling and simulations: a guide for manufacturing projects

Building a model and using it to run detailed simulations is one of the best ways to support confident, data-driven decisions in today’s competitive marketplace.

Where are the risks in your operation? What can you do to mitigate those risks, lower your operating costs, and improve product quality? Whether you manage a warehouse, operate a contract manufacturing organization, or are planning an advanced research facility, you are a project owner—and you need to own the answers.

Often, however, the answers you seek are elusive. Pulling levers in the “real world” to see how your process or system performs under varying conditions is disruptive and risky. If that system doesn’t yet exist—as in the case of a greenfield project, for example—your task is even more difficult. In both cases, a modeling and simulation (M&S) fill the gap, providing you with an accurate visualization of the past (for validation) or of several possible futures (for what-if scenarios). It’s a technique designed to answer the broad questions we asked above, as well as the more specific questions you may be facing every day.

Modeling and simulations provide answers to R&D, manufacturing and operational questions, like:

  • Is this the right time to pull the trigger on our next CAPEX project?
  • How can we improve the efficiency of our packaging line?
  • Should we invest in new equipment, or can we tap into our unused capacity?
  • Our warehouse manager wants to add 500 new pallet spaces. Can we justify the cost?
  • How can we scale our novel lab process to commercial production?
  • How many bioreactors will we need to meet our ten-year demand forecast?

You can take the guesswork out of your next capital project or optimization exercise by answering these questions with a well planned modeling and simulation strategy.

a model vs a simulation factory operations model vs. simulation

What are modeling and simulation?

Although it’s common to use “modeling” and “simulation” interchangeably, the truth is that a model is a twin of a real-world system of interest, while a simulation is the result of running or operating a scenario within that model.

If you’re investigating a well-defined question (such as whether a certain end-of-line palletizer will meet your needs), and if you can assume that process variabilities and interactions are non-existential, a capacity model built inside of a deterministic tool like MS Excel might deliver that certainty.

Most projects are more complex than that, however. They involve intricate real-world systems with multiple dependencies, variabilities, and uncertain parameters. For those projects, we recommend a computer-generated model capable of performing both deterministic and stochastic mathematical modeling.

Different types of M&S techniques are available to answer different questions. Discrete-event simulation (DES) will help you to characterize uncertainty and prepare for the unexpected, such as a disruption in the supply chain or a sharp rise in demand for your product. Computational fluid dynamics (CFD) simulations will help you to understand how the flow of air, gas or fluids impacts your process or systems, helping you to improve your equipment and space design. Process models are capable of performing heat and mass balances and (in some cases) developing a feasible schedule and utility profile as an output.

In this article, we’ll focus on simulations related to the design and operation of your manufacturing facility. We use these types of simulations to help our clients manage their operating risks, identify opportunities to improve throughput, and position their facilities to meet ambitious business objectives now and in the future.

Modeling and simulations terms to know:

A block flow diagram, sometimes called a logic diagram, translates the individual tasks of a process into a visual sequence, giving engineers and other stakeholders a tool for efficiently documenting workflows, assumptions, and inputs. These typically form the basis of the DES models.

An end-to-end manufacturing process is made up of many constituent parts, or “units.” A detailed model drills down to the level of unit operations, giving engineers a greater opportunity to uncover and address risks within the process.

A process model is the computational environment in which engineers run a process simulation. The same model might serve as a platform for multiple simulations, allowing engineers to compare behaviors and outcomes as different variables are introduced.

Simulations are an effective way to identify rate-limiting steps in your process which can constrict capacity. These are the process “bottlenecks.” By addressing these bottlenecks early in the process design phase, project owners can ensure optimal productivity and throughput.

After building a model and before using it to run a ‘what-if’ analysis, engineers undertake a verification and/or validation step to test the model’s accuracy. In the case of an existing facility, historic data provides a necessary benchmark for this step (validation); for new facilities, engineers rely on estimates and high-level assumptions to approximate real-world behaviors (verification).

Like a process model, a digital twin is a specific and data-based environment that mirrors the real-world facility and process down to its unit operations. This gives project owners an environment for testing assumptions and running scenarios without impacting actual operations.

3 project phases for simulation manufacturing project stages: master plan, design, optimization

Modeling and simulation offer benefits in every project phase

master plan icon

While master planning

Modeling and simulation (M&S) techniques help master planners maintain agility and responsiveness within a technological, regulatory and consumer-driven world that never stands still. With a well-designed computer model as your foundation, you can quickly see how a change in variables, a shift in demand, or emerging technologies might impact your master plan—and then you can update that plan accordingly, ensuring you’re ready for the constantly evolving future.

But what does “ready for the future” mean, and how can M&S help? You might consider running simulations or scenario analyses at the master planning stage in order to:

  • Understand your system’s sensitivities.
    • Evaluate the impact of future variability on equipment occupancy, resource utilization, throughput, inventory levels, and much more, gaining the insights necessary to de-risk the overall process and prepare your facility for future changes and opportunities.
  • Build realistic projections into your master plan.
    • Accurately scope your equipment needs and utilities consumption and estimate operating costs required to meet future demand, ensuring that you allocate appropriate budget to accommodate projected growth.
  • Implement a flexible and future-ready labor model.
    • Study the resiliency of your proposed labor model over time and understand exactly how that labor model will impact efficiencies in your manufacturing spaces, your warehouse, your quality control labs, and so on.
    • Use those insights to refine your labor model, ensuring that your workforce can grow as quickly and with as much flexibility as the future will require.
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During the design phase of a new facility or a renovation project

Whether you’re designing a new facility or optimizing an existing one, it pays to have certainty. Getting your plans right from the beginning will spare you from the time and expense of changing direction or fixing a problem mid-stream.

That’s where modeling and simulation become valuable tools. Use them to right-size a greenfield project or to get the most value from every square foot of a renovation. Simulations are also incredibly useful tools for navigating unexpected changes, which are all but inevitable during design and construction. A new piece of equipment might emerge; management might decide to change your product mix; a shift in demand might impact your projections. By developing a model and using it to run detailed simulations early in the design process, you’re able to adapt to these changes much more quickly and smoothly.

You can also rely on modeling and simulation during your design phase in order to:

  • Right-size your utility systems
    • Observe how the physical properties of your materials (such as heat capacity, density, dew-points, etc.) will impact your utility consumption profiles as you scale.
    • Avoid production delays by calculating future requirements for heating and cooling utilities, treated water, and industrial gases to support production and cleaning operations.
    • Quantify and fix the degree of “overcapacity” built into the facility’s design, ensuring an appropriate level of redundancy while trimming unnecessary elements.
  • Select the appropriate number and size of processing and supporting equipment
    • Establish the Overall Equipment Effectiveness (OEEs) of proposed equipment. Calculate the appropriate size and number of pumps, bioreactors, tanks, etc. needed to meet future production targets while maintaining appropriate redundancies and relief systems.
  • Understand your batch materials
    • Balance chemical reactions, perform mass and heat balance calculations, compute yield losses and much more using your model. This will help you to closely estimate the volume of intermediate material, final product and waste that your new or renovated facility will generate at each process step.
    • Right-size your piping systems, your conveyors, your fleet of fork trucks, and other material handling equipment according to the realities of your product’s full lifecycle.
    • Design appropriate staging spaces to accommodate current and projected local inventory storage needs and reduce or eliminate unnecessary transportation.
  • Build resiliency into your production schedule
    • Proactively solve for scheduling disruptions by calculating required headcount, setting appropriate shift schedules, anticipating inventory needs, assessing utility consumption profiles, and more.
optimizing a site plan - icon

When you believe there’s an opportunity to optimize your process

Running detailed simulations on a well-designed model will help you meet your optimization goals, whether you’re in a manufacturing facility, a QC lab, or a warehouse.

Many of the techniques discussed above, in our section on using simulations during the design phase of a new or renovated facility, apply here as well. More specifically, project owners seeking to optimize an ongoing process or system can leverage modeling and simulation in order to:

  • Identify the most appropriate optimization approach
    • From integrating new automated technologies to reducing variability and lowering intermediate inventory costs, the potential avenues towards optimization are diverse and numerous. Simulations can help you rank these options in terms of their cost and their potential for achieving the specific optimization goal.
  • Justify the cost and scope of an optimization project to stakeholders
    • An up-to-date model provides a running start when it comes to adapting equipment and operations in response to a new optimization goal.
    • Through appropriate simulations, project owners can quickly extrapolate supporting data to help drive a necessary change and move the optimization project in the right direction.
simple how to infograph - build a simulation model 6 steps to build an advanced computer model and run a simulation - infograph

How to build a model and run simulations

The best models don’t replicate every detail of your selected systems. They are selectively designed to include only what’s necessary to answer specific questions or test a certain hypothesis. The idea is to design a model with the right level of complexity to provide useful insights, without being so complex that it bottlenecks the decision-making process.

Perfecting this balance is an artform that requires specialized skills and experience. When it all comes together, your model—and the simulations it supports—will become one of your most powerful defenses against risk, and a tool for accelerating and right-sizing your future growth.

  1. Start with clear objectives. These could be questions you’re trying to answer (“Does it make sense to invest in this new piece of equipment?”) or an outcome you’re trying to reach (“We need to cut operational expenses by 15%”). 
  2. Define the metrics you’d like to report. How will you assess the results of the simulation? Based on throughput? Lead times? Headcount needs? Returns on capital spending? Aligning this measure with the objectives from the outset will help you to compare results as you complete your computations and reach meaningful conclusions.
  3. Collect the data If the goal is to simulate a system in an existing facility, that data is collected from the facility’s historic performance records. Depending on the objectives of the process simulation, it might include information from batch records, process flow diagrams, utility usage records, tank storage capacities, cycle times, headcount, and much more.
  4. Build a baseline model. A baseline model is built on the data collected in Step 3. One model might provide a platform for many simulations, helping you to explore alternative scenarios within the same environment. Alternatively, you might use several different models to understand the effects of random variation. 
  5. Perform model verification/validation. Once they’ve built the model, engineers verify and/or validate it by running test simulations and comparing the model’s performance against real-world results. In the case of a process that does not yet exist, they compare that performance against expected outcomes.  In both cases, the goal is to ensure that the model is behaving as expected. Once that’s established, you can proceed with what-if analyses, improving buy-in from the outputs.  
  6. Run your scenario analysis. Now all of the work of building the model and defining your objective and metrics pays off. You can begin inputting variables into the system, pulling certain levers, watching to see how they impact behaviors and outcomes in the simulation. By layering the results of your simulation onto the sub-problems you identified when setting your objectives, you can begin to identify opportunities for improvement and to develop a realistic and prioritized implementation plan.  

Match your approach to your objectives:  A case study 

There are many approaches to modeling and simulation, each suitable for solving different types of problems. Choosing the right approach comes down to understanding your specific objectives and using that understanding to define the scope and boundaries of your modeling and simulation exercise.  

The challenge:  

Here’s an example of how one project team matched their objectives to a specific modeling and simulation approach. In this case, the team’s overall goal was to identify bottlenecks that might impact their operations as they scale to meet their ten-year demand forecast. They broke that main objective into several sets of sub-problems within specific manufacturing domains, then matched those sets to appropriate strategies.

The objectives ranged from Process Engineering and Management to Logistics and Warehousing:

  • Determine feasibility of adopting new technologies
  • Track yield losses through the process
  • Develop campaigning strategies to meet target throughputs
  • Determine material handling and delivery strategy to ensure constant supply without increasing on-hand inventory
  • Evaluate and compare satellite vs. central warehousing options

The result:  

By integrating insights from these different process simulations, the project team was able to uncover potential bottlenecks and develop a phased plan to either replace current equipment or add more equipment and headcount over time.  

This plan could involve demolishing certain buildings, widening high-traffic corridors, installing new utility systems, and much more. With the simulation and analysis exercise behind them, the team was able to confidently prioritize these changes, minimize their impact on ongoing operations, and justify their expense. 

Q and A Q&A

Frequently asked questions about modeling and simulation 

Q: How can I trust the insights generated from the simulations I run in my model?  

A: Your model’s design is carefully controlled, validated, and updated as necessary, ensuring that the results of your simulations are aligned with real-world conditions.  

A good model isn’t a black box—in fact, it should be the opposite. It generates clarity and transparency by providing a window into your current and future operations, helping you to characterize and plan for unknowns and fine-tune your manufacturing philosophy.  

The process of achieving that level of transparency shouldn’t feel like a black box, either. Some specialized expertise will be necessary, but a good M&S team will work with you to select the right subset data and build a model that’s suitable for your unique situation. This collaboration, when done well, will help you go inside the model to truly understand its problem-solving capacity, and the relationship it bears to your real-world operations.  


Q: Will I get value from my model beyond this initial project?  

A: Your model may become one of your most valuable decision-making aides as your operation scales.   

Models aren’t static “one-and-done” tools. They’re living documents. With the right expertise working alongside you, you can extend the value of your initial investment by using your model to continuously assess and optimize your operations as certain technologies, processes, and market dynamics change over time.  

To make this happen, you will need to feed your model with up-to-date data and understand which variables to input for a simulation that accurately reflects your evolving situation. But with your existing model as your foundation, you’re already ahead of the game—and you have a valuable starting point from which to proceed with fewer risks, greater visibility, and a plan for continuous growth.  


Q: Can you run a process simulation if you don’t have historic production data?   

A: Yes. 

Although process models can be viewed as a digital twin of an existing facility—giving you a chance to extrapolate from their actual, ongoing operations—this isn’t a prerequisite. Using an initial set of assumptions and estimates (averages or range of values) in place of historic data, you can generate an accurate baseline model to guide the design of a new facility, to test a new piece of equipment, or to undertake any other type of meaningful investigation into the unknown. 


Q: How much detail should a model include to provide valuable information? 

A: An extremely detailed model isn’t always necessary. 

A successful model isn’t necessarily a one-to-one copy of the real world. Manufacturing systems are often very complicated, with hundreds of variables and underlying relationships impacting the behavior of the system as a whole. Not all of those variables are relevant to the goals of a particular simulation; incorporating and validating those extraneous details won’t add real value. Instead, a good model simplifies the real-world system, capturing essential relationships while excluding unimportant details.  


Q: I’m on a budget with a tight timeline. Are modeling and simulation still an option for my project?

A: M&S is not one-size-fits all. It’s a scalable solution for projects facing scheduling and cost constraints.  

If process simulations required a complete and highly detailed model of your entire facility or supply chain, it would not be a good option, but as we established above, a good process simulation starts with a computer model that is right-sized to fit the questions you’re trying to answer.  

Often, it helps to break a complex question or desired outcome into sub-problems, then generate sets of domain-specific objectives related to those sub-problems. This approach helps the simulation exercise move faster, because it means that you’re able to deploy specific models for specific objectives and teams, rather than spending time and money to develop and validate a single, complex model of the entire facility.  

These models aren’t static, either; you can use them dynamically, adding multiple layers in stages to see how the whole system behaves together. Often, a team may be operating several of these discrete models in parallel, solving multiple problems at once and thereby moving towards solutions much more quickly and cost-effectively.  

Final considerations

Addressing a chronic bottleneck, a contamination risk, or any other manufacturing challenge in an existing operation is an expensive and time-consuming prospect.

A modeling and simulation exercise helps project owners avoid that scenario by providing a digital environment in which to observe a process outside of the real world, giving you the opportunity to add, remove or adjust variables before taking concrete action. Whether designing a greenfield facility or optimizing an existing architecture or operation, a modeling and simulation exercise is one of the most effective ways to de-risk your capital investment.

Interested in starting a process simulation to optimize your operations and validate your next moves regarding your facility? Let’s talk.

 

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