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Robots and Material Handling Equipment (MHE) can make a huge impact to biopharma productivity. But what should your fleet’s makeup and traffic flows look like to maximize efficiency and spend?
Here’s how to ensure you’re getting the most out of your robotics and MHE investment.
Whether your automation and robotics investment is driven by rising demand, quality considerations, labor constraints, or other operational pressures, data-driven simulations are an essential step for designing and vetting your system before you invest real capital. This is a low-risk, high-reward step that enables modifications to your equipment selection, fleet size, traffic routes, interaction points and wait times to maximize results and drive stronger ROI.
It wasn’t long ago that facility owners were asking, “Should we include robots?”
Today, that question feels antiquated. Robotics in general—and material handling equipment (MHE) robotic systems in particular—are common features of modern biotech and pharma manufacturing. Once seen as cost-prohibitive, robotic MHE systems are increasingly accessible, even for small and medium-sized drug manufacturers with limited capital. Manufacturers no longer ask whether to invest in these systems. Instead, the question has become: “How many MHE robots should we have?”
The problem with this question is that it’s very limiting. It treats MHE robots as standalone assets rather than as part of the broader manufacturing ecosystem. Making a significant investment on such a narrow premise is a mistake. This is how manufacturers end up with automated MHE systems that are undersized or poorly integrated, unable to fulfil the promise of lower operating costs, improved consistency and predictability, higher throughput, and enhanced safety.
Instead of asking how many MHE units you should have, here’s a better starting question: What should our MHE fleet look like?
This broader framing opens the door to a set of important follow-on questions, including:
- Which is a better fit for us: a fully automated, lights-out scenario or a hybrid cobot system?
- What types of MHE robots should we deploy?
- How many of each type are required to meet our target output?
- What are the facility and process design implications of introducing different levels of MHE automation?
- Should we implement our automated MHE fleet in phases?
- Can we integrate automated MHE throughout the full manufacturing facility? If certain areas have limitations, are those limitations driven by regulatory requirements, physical barriers, connectivity gaps, or other factors?
- What is the requirement of IT infrastructure? Is our current IT infrastructure adequate or do we need to upgrade?
- Where is the cost inflection point that maximizes the value and efficiency of MHE robots while also delivering a favorable ROI?
Thinking about your automated MHE systems in this multi-dimensional way is key to less over-spending, over-engineering, or over-automating. It requires a systemic view of automation and its broader operational impacts, as outlined in the table below.
System lens | Considerations | Cost of getting it wrong |
|---|---|---|
| Operational |
|
|
| Facility |
|
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| Manufacturing interfaces |
|
|
| Workforce |
|
|
The cost of getting automated MHE systems wrong is often much higher than the cost of more upfront analysis. That’s where simulation-based studies come in. These studies provide a systematic, structured approach to understanding how automated MHE will perform under a facility’s real constraints, and a robust stress test to determine whether CapEx investments that look good on paper will deliver real value in practice.
The cost of getting MHE systems wrong is often much higher than the cost of more upfront analysis.
Not every organization is ready for this level of analysis. If you’re in the earlier stages of digital transformation and adoption, you may need a clearer view of your operation’s current state and long-term goals before proceeding. A digital maturity assessment or a digital roadmap is a good place to start. Such an assessment will put advanced technologies into context, bring your leadership on board, and lay a stronger foundation for detailed MHE simulations.
Beyond the warehouse: Where does automated MHE fit in the modern biopharma facility?
Robotic material handling systems are most commonly associated with warehouse automation, but they can deliver value at any point where material movement is frequent, repetitive, time-sensitive, or where material movement poses quality and safety risks to products or operators. While adoption is rapidly expanding, today’s common uses for automated MHE in biopharma manufacturing include:
- Moving and handling samples in QC and lab environments
- Performing repetitive or precision-driven loading tasks during manufacturing (such as transferring material to and from isolators or RABS via defined handoff points)
- Supplying materials to manufacturing and packaging lines
- Managing raw material and finished‑goods logistics, particularly where traceability and temperature control are critical
- Feeding packaging and fill‑finish lines with components or moving work-in-process (WIP) materials
Why an MHE study is critical early in CapEx planning
There’s a lot at stake when manufacturers make a large investment like this, yet many MHE conversations take place with little more than average operating conditions as a north star. Those averages may provide a convenient starting point, but what happens when demand spikes or interdependent constraints throttle production? Unless you account for these realities, you may end up spending on a system that appears viable but struggles to perform in practice.
Data-driven modeling and simulation studies incorporate the true determinants of a proposed MHE system’s success or failure, including factors such as volumes, repetitive tasks, labor constraints, quality and compliance requirements, variability, interdependencies, fluctuating demand, and more. From this foundation, manufacturers can build an automation investment strategy grounded not in gut feeling or averages, but in real-world operating conditions with fluctuating peaks, wait times, charging schedules, and delivery variables. Through these studies, manufacturers can unlock several important insights:
CapEx forecasting, justification, and phasing
- Determine not only whether automation is justified, but what to automate now, what to phase in later, and how to align an automation investment with overall business priorities.
Accurate fleet sizing
- Avoid over-engineering by testing “what-if” scenarios before committing capital. For example, if pallets are queuing at the laydown space near the docks during peak hours, modeling techniques can help determine whether adding robots to move pallets is cost-justified compared with increasing manual labor.
Optimized layouts and traffic flows
- When designing new facilities, modeling and simulation can help develop layouts that improve flows, eliminate congestion points, and reduce safety risks. For existing facilities, simulation can help design teams develop optimal routing and conveyance plans.
Systems designed for scalability and flexibility
- Design for growth by aligning production volumes or inventory profiles with current and future automation investments, and by defining the triggers for re-evaluation or re-qualification as demand increases.
Early identification of hidden constraints
- Identify the potential for line blocking or starving caused by bottlenecked MHE. Using simulations, manufacturers can identify these risks and implement solutions early in the planning stages.
A clear workforce strategy for hybrid human-machine operations
- Clarify where human flexibility delivers greater value and, conversely, where the speed and precision of robotics offer the strongest return. Additionally, assess the level of workforce upskilling required to manage robotic operations.
When the MHE simulation reveals what you shouldn’t automate
Using simulations to quantify and justify MHE robotic systems isn’t about defending automation at all costs. In many cases, it surfaces nuances that may lead to unexpected, context-specific conclusions, including scenarios where “less is more” when it comes to automation.
For example, your simulation study may reveal that:
- Less automation is required than initially planned to meet the target throughput.
- Phased automation, introduced over time in step with demand, delivers a higher ROI.
- Humans outperform robots on specific steps or workflows, or when the required degree of automation is not cost-justified.
- Demand volumes, movement frequency, or non-standard workflows do not support robotic MHE applications.
- A small change, such as adding one more conveyor, could offset the need for various support equipment (or, conversely, that facility layout and infrastructure upgrades would exceed the cost of the robotic equipment itself).
- The process is still evolving, meaning it’s too soon to develop a defined MHE strategy.
How to execute a robust MHE study
While specific approaches to MHE quantification and justification studies may vary between facilities and use cases, most follow a similar progression.
First, define the business case and operating assumptions
These studies typically begin with a clear business case, such as justifying the implementation of an automated MHE system maintain competitiveness while meeting throughput targets. Depending on variables such as how materials actually move through the facility, potential improvements in safety or operating costs, and the specifics of the area under study, planning teams may then collect data such as:
- Units, pallets, or picks per defined period
- Run rates, replenishment rates, and peak-demand conditions
- Interactions between upstream and downstream processes
Next, introduce modeling and simulation tools
With these inputs defined, planning teams can use modeling and simulation to test scenarios and evaluate performance. There are several techniques available for this purpose, such as:
- Discrete event simulation (DES) models how materials, equipment, and resources interact over time. For example, a biotech manufacturer client used DES to assess how variability in delivery schedules of patient materials affected the resource required to enable rapid turnaround for autologous cell therapy products. With this insight, they were able to plan around risks and capacity constraints that weren’t visible using averages alone.
- Scenario modeling and sensitivity analysis enable teams to run “what-if” scenarios and understand how changes in demand, fleet size, or operating assumptions affect outcomes.
- Systematic layout planning and flow modeling evaluate how physical configuration, routing, and traffic patterns affect flow efficiency or create safety concerns.
If you lack robust internal data to drive these simulations, you can sometimes draw on insights and lessons from adjacent sectors such as retail and high-volume distribution, where manufacturers have already quantified and tested similar material-movement challenges. In one fully automated warehouse for an international logistics firm, for example, a simulation exercise helped evaluate AGV counts, congestion, and other variable scenarios. Pharma manufacturers can look to projects like these for proven methodologies that help replace assumptions with evidence.
Digital twins for ongoing success
The simulation model is not destined to sit on a shelf and collect dust after the study concludes. It can continue delivering value by feeding a long-term digital twin.
A digital twin is a powerful living tool for continuous quantification, justification and operational improvements. In the pharma context, digital twin technology is central to a new era of data-driven manufacturing, helping organizations anticipate change, minimize disruptions, and proactively improve outcomes.
By using a digital twin, manufacturers can work inside a dynamic, reality-based environment to:
- Test new or additional automated MHE equipment without disrupting real-world operations
- Evaluate changes to workflows, routing, or charging cycles
- Support ongoing optimization as demand and operating conditions evolve
From justification to implementation: MHE Preparedness
After establishing the business case and justifying and quantifying the MHE system, manufacturers must ensure that their facilities, operating models, and workforce are ready to support automation in practice. That shift from analytical justification to operational execution introduces a new set of challenges which further determine whether a new automated MHE system will deliver sustained value or face avoidable limitations.
MHE preparedness: Facility and structural considerations
Once you’ve confirmed that an automated MHE system makes sense on paper, success depends on what happens next. The question is whether your new or existing facility is truly prepared to support that system in practice.
Many of these considerations relate to the building structure itself. In an existing facility, is the floor level enough to support reliable robot navigation and docking? Is the load-bearing capacity adequate for automated equipment, storage systems, and material loads? Are aisles sized correctly to accommodate the automation system’s footprint and movement requirements? In a modern greenfield facility, all these details need to be accounted for in the design.
Digital infrastructure is equally critical. Is the IT system ready to support real-time data exchange and system coordination? Are there any connectivity “dead zones” that could disrupt navigation and control? Will the new automation system integrate cleanly with existing platforms?
The solution: use modeling and simulation, in conjunction with other engineering studies, to conduct a facility readiness assessment. This will help you determine whether your facility (existing or planned) can realistically support the proposed MHE strategy. If there are potential constraints, this assessment will identify them so you can develop solutions before beginning implementation.
Once you’ve confirmed that an automated MHE system makes sense on paper, success depends on what happens next.
MHE preparedness: Commissioning and validation for scalable automation
As throughput expands, layouts change, or your MHE system’s routing logic evolves, so too must the operational frameworks that govern that system. This is especially true for commissioning, qualification, and validation activities, which manufacturers must anticipate and manage well beyond initial installation. Without clear criteria to help determine when requalification or revalidation is required, even a well-designed automated MHE system can become an operational or regulatory constraint over time.
A simulation-driven digital model (or “shadow”) is particularly valuable in this context. By modeling future operating ranges, peak-demand scenarios, and failure-and-recovery SOPs in a virtual environment, manufacturers can anticipate how an automated MHE system’s behavior may need to change over time. This insight will allow you to proactively plan requalification strategies without disrupting operations. The result is a future-oriented MHE automation strategy that supports scalability without putting compliance at risk.
MHE preparedness: Workforce considerations
In automation conversations, it’s not unusual for teams to assume that robotics will reduce workforce risks. In reality, automation reshapes that risk.
For example, consider a manufacturer who has decided to meet their material handling needs by implementing a full-scale robotic MHE system. This investment reduces their reliance on manual labor but creates a new challenge: their new automated system requires specialized technicians who may not be locally available. New costs and dependencies emerge that weren’t apparent during the business scoping stage, leaving this manufacturer with a tradeoff they didn’t expect.
A skills-gap assessment can help planning teams avoid a similar situation by modeling these dynamics in detail and ensuring successful implementation. In particular, these studies can help define new capabilities that may be required, training and upskilling strategies (such as partnering with a local technical college), and how to handle a system failure from a personnel perspective.
Looking ahead: From enthusiasm to smart execution
Facilities evolve. Demand fluctuates. Workforce dynamics change.
To stay ahead of these shifts, manufacturers need a disciplined understanding of exactly how and where automation will deliver value, now and in the future.
That’s the role of a simulation-based study for automated MHE systems. These studies are living tools that can help to better manage uncertainty over time, particularly when embedded in a digital twin that continuously learns from actual operating data. As conditions change, manufacturers can rely on this digital environment to refine their MHE strategies and workflows with greater flexibility and insight. The result is:
- Better CapEx conversations, grounded in performance, risk, and real-world conditions rather than gut feelings and averages.
- Fewer late-stage surprises because design, facility, and workforce constraints are identified early.
- Smarter automation strategies, phased to support long-term scalability and flexibility.
- A truly continuous improvement path.
Let’s maximize your robotics and MHE investment.
Talk to our team of experts today.
MHE FAQs
While technology is constantly progressing, here is a list of some common material handling equipment:
- AMR: An autonomous mobile robot transports material horizontally, determining the optimal route or using a preloaded map for navigation.
- ASRS: Automated storage and retrieval systems are used in high-throughput operations, such as distribution centers and packaging facilities. They come in two main types:
- Pallet-level systems, for storing and retrieving full pallets.
- Tote-level systems, for smaller items, like 2×2-foot totes.
- Mobile racking: The racking moves itself. There’s only one aisle to get into, which creates higher density storage.
- Pallet shuttles: This is a deep-lane storage system that works well when there are many pallets of the same item. If you have, for example, four items with 50 pallets each, deep-lane storage can maximize density since you only need immediate access to the first batch for consumption.
While automation can reduce the need for certain manual roles, it often creates demand for specialized skills in systems operation and maintenance. This can change workforce economics in ways that aren’t always obvious during planning, especially if those skills are scarce.
Quantification and justification studies can help manufacturers anticipate these shifting dynamics by modeling workforce requirements, training needs, and contingency planning as part of an overall MHE automation strategy.
Automated MHE systems can improve safety for both the workforce and the patients who rely on a facility’s products. For employees, this type of automation reduces manual material handling and repetitive tasks, lowering the risk of ergonomic injuries and workplace accidents. For patients, automated MHE systems can reduce the risk of human error, improve consistency, and help product quality by minimizing unnecessary handling and exposure.
Quantification and justification studies help reduce risk by surfacing hidden constraints early, when they can be addressed at minimal cost. These studies achieve this outcome by modeling how automated systems interact with the facility, the workforce, and the operating environment. Through this modeling exercise, manufacturers can ensure that their facility’s layout, infrastructure, and staffing strategy are all “automation-ready” before implementation begins.
Justifying your CapEx spend on automated MHE systems means using data-driven modeling and simulation to evaluate how these systems will perform under real operating conditions in your facility before selecting or installing equipment. The result is a clearer path to implementation and greater confidence that your MHE investment will deliver the results you expect, now and in the future.
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