While personnel parachutes have been updated over the years, the process to pack them has remained relatively unchanged for more than half a century. The parachute itself has become more complex and the demands on workers have increased. We were tasked with improving this process to ease the burden on parachute packers and make the system more robust.
The Problem Space
The Aerial Delivery & Field Services Department (ADFSD) came to us with a problem: they wanted to automate parachute packing. Now, parachute packing is much more complicated than it initially appears. It is a long, manual procedure that involves many steps, in-process checks, and tacit knowledge. We had to pinpoint why ADFSD wanted to automate the process in order to assess their real needs.
After performing a contextual inquiry at Fort Gregg-Adams, we discovered that packing is repetitive, rigid, and tedious. Despite being fully manual, the process does not conform to the human, so the human has to conform instead. This leads to strain, repetitive stress injuries, and lost productivity. Finally, the siloed work and monotony can cause riggers to lose sight of the important work they are doing. However, behind the problems we had identified, a deeper, systemic problem lurked.
To understand our research and design decisions we made, let's delve into some context first. ADFSD is a US Army quartermaster school operating out of Fort Gregg-Adams in Virginia. It trains personnel for important jobs such as packing parachutes. The main user we have to empathize with is a rigger, who's job is to pack the parachutes. Inspectors are another stakeholder we should consider, who inspect certain stages of the parachute packign process to make sure everything is done correctly.
Riggers
Our main users—their job is to pack 15 parachutes a day.
Inspectors
Expert packers that have moved up in rank and now inspect rigger's packs during the packing process
The parachute packing process has many steps and 8 inspections total. While it is very intricate, it can be broken down into 5 main tasks:
Laying Parachute Out
Parachutes are long and slippery—they be tedious to lay out
Flaking
Folding each section of the parachute, called panels, symmetrically
Packing Parachute into Bag
A deployment sleeve is added over the parachute, and it is S-folded into the deployment bag
Stowing
Parachute lines need to be threaded through loops on the outside of the pack
Securing Bag
Various flaps and openings need to be secured, lines need to be stored, and pins need to be checked
Parachute packing is slow and painstaking work—one error could lead to someone losing their life. We have been tasked with improving this process, while maintaining a low rate of errors, so that ADFSD can produce parachutes more efficiently.
From our research, we isolated 4 main problems:
Rigger reliance on inspectors is a bottleneck for packing efficiency
Not relying completely on inspectors to move to the next step can increase rigger efficiency, as long as safety standards are maintained.
The one-size-fits-all process disregards individual ergonomics, resulting in strain and injury
A shift towards a more adaptable process can foster a healthier work environment and boost productivity
Limited variability and collaboration makes work monotonous, impacting rigger morale and job satisfaction
We can increase rigger engagement and satisfaction by addressing the monotonous nature of parachute packing
Small, inefficient tasksadd up throughout the day, wasting riggers’ time and reducing manpower
By optimizing parts of the parachute packing process, we can free up manpower and improve efficiency
From there, we identified 4 Guiding Principles:
How might we make the process less physically demanding and reduce repetitive motions?
How might we collect and analyze performance data to optimize packing techniques?
How might we develop modular packing techniques that allow for faster and/or scalable rigging?
How might we facilitate team collaboration without losing accountability?
Designs
Our designs are still in the development stage and we have a clear path that we will follow during our summer semester. We will focus on validating concepts, iterating on whichever designs work the best, and creating prototypes that can be tested on riggers and inspectors.
Our research process uncovered two kinds of problem—current issues with the rigger workspace and environment that are causing them harm, and a systemic problem: the parachute itself. Both of these identified problems are vital to solve, as riggers are currently getting harmed in their work environment, which needs to be fixed. However, the ultimate source of these harms, the parachute, needs to be addressed. The parachute packing process cannot be fully automated in its current state. We need to help ADFSD redesign the T-11 parachute so that it can be more easily packed.
Environmental Improvements
Smart Table
The long wooden tables used by riggers are the cornerstone of the entire packing process, yet they have remained the same for decades. Riggers come in all shapes and sizes, but the tables do not. Our new envisioned design accommodates different rigger needs by automatically adjusting the height to each rigger's optimal working position. This will improve form and reduce posture-related injuries, as riggers no longer have to hunch over or reach awkwardly as they pack.
Our current vision utilizes a wristband with RFID technology that has the rigger's height stored in it. A scanner will be embedded in the table, and scanning the wristband will automatically change the table height to an optimal ergonomic position. This also allows for data collection, such as tracking attendance or how long each rigger takes to pack.
Improved Health & Safety
The table height will be optimized for each rigger's height to reduce strain. Tools will be secured in the table for convenient & safe access
Improved Efficiency
By optimizing the working conditions and providing an ergonomic environment, rigger efficiency will improve
More Personnel available
With less riggers harmed when working, more personnel are available to pack
Metrics to Test:
1. If it actually helps riggers with pain and injury
2. If riggers are comfortable with personal data such as height being stored in a wristband
3. whether riggers can adapt to changes in their work environment and adopt a culture of improved safety, which is at odds with the current army culture
Computer Vision
Our design leverages current AI capabilities to analyze rigger movements. If they have incorrect form, the system can flag it.
In the future, our system would also be capable of real-time error detection. It will continuously scan as the rigger works and could possibly leverage laser projections to determine if everything is packed correctly, If an error is made, the rigger will know right away, allowing them to learn from their mistakes. Our solution also improves efficiency—the error is caught more quickly compared to standard packing procedures, where an error would be caught during inspection.
Improved Health & Safety
Mistakes in rigger form or packing can be caught in real-time, preventing form-related injuries and packing errors
Improved Efficiency
Errors are caught more quickly, instead of the rigger having to wait until the inspector catches the errors
Improved Learning
Riggers can learn from their mistakes immediately, quickly adopting the correct form or technique
Metrics to Test:
1. Whether automating only certain parts of the packing process is beneficial
2. If riggers respond well to using automated tools
3. whether riggers can adapt to changes in work culture
Automated Stowing Machine
Stowing can be a tedious task for riggers—they have to thread each bundle of lines through a loop one-by-one with a stow hook, twisting as they pull through. This can cause a variety of problems, from repetitive stress injuries on wrists, to wasting valuable time. Our new design automates the strenuous pulling part of the stowing process by pulling the lines through the deployment bag loops in a single action. All the rigger has to do is hook the line bundles up to the device.
Improved Health & Safety
Stowing can be hard on the wrists--automating this step will help prevent stress injuries
Improved Efficiency
Automating stowing will cut down on the typically time-consuming process
Improved Efficiency
Automating stowing will cut down on the typically time-consuming process
Metrics to Test:
1. Whether automating only certain parts of the packing process is beneficial
2. If riggers respond well to using automated tools
3. whether riggers can adapt to changes in work culture
Performance Dashboard
We designed an internal-facing dashboard that shows a rigger's metrics, such as average time per pack, time for each stage, and injuries they have sustained. This improves rigger morale by showing them how far they have come and giving them a sense of achievement that extends beyond the repetitive grind of 15 parachute packs per day. Riggers can also compare each other's stats, fostering healthy competition, which is important in a rigger's work environment.
In addition, it acts as a display for all the data collected by the Smart Table and CV system. Individually, riggers can see where they need to improve, but it can give insights into wider trends in the parachute packing process as well.
Boosts Morale
When riggers see the progress they've made and how much their hard work has contributed, they will feel more valued
Fosters Healthy Competition
Competition, banter, and camaraderie are important to riggers, so seeing other rigger's stats will help support this culture
Displays Collected Data
Displaying stats helps quantify data in ways that are tangible and digestible to riggers
Metrics to Test:
1. Rigger's feelings towards having their data collected and displayed
2. Whether it boosts morale
3. Whether it promotes friendly competition
Parachute Redesign
Vision
The current T-11 parachute design is the source behind all of the problems we identified. The design is great for people using the parachute, but it ignores the people packing them. For our project trajectory, fixing current problems riggers face like poor ergonomics and repetitive stress injuries is valuable. But what if we also worked towards fixing those problems at the source?
We Talked to Experts
So far, we have talked to two experts at CMU: Howie Choset, and Chris Harrison. Both validated the idea that the parachute needed to be redesigned: to be easier to be packed by humans, machines, or both. Many ideas were generated that we still need to explore, from using tessellated material, to semi-rigid sections with weak points that make the parachute easier to fold, to adding magnets to seams that snap into place.
Our Plan
Right now, we need stronger foundational knowledge on parachute design. We will dive into more research and learn all about T-11 parachutes and military parachute design history. We will also look into new technology we could leverage, like tessellated fabric or smart materials. Carnegie Mellon has a wide variety of resources that we plan to leverage, including talking to more professors. There is a possible trip to France as well, to visit a factory that has automated a different kind of military parachute. By the end of the summer, we should be able to provide ADFSD with a solid foundation that they can use as a jumping-off point for parachute redesign.
Design Process
Secondary Research
When we started our research, we knew very little about parachute packing. We had to do a deep dive into ADFSD, the US Army, parachutes, parachute packing itself, and the many subdomains within these categories. Once we had more general knowledge on our problem space, we decided that analogous domain research would be helpful. This process involves looking at similar fields to your problem and seeing how that problem is solved. Parachute packing involves folding large swaths of fabric, so we researched origami, textile manufacturing, and sail making. The packing process also involves compacting something large—the parachute—into something small—the deployment bag. For this, we looked at how airbags are packed and installed in cars, or how spacecraft deployable objects are made. We were also curious if there were processes that were already automated for folding or packing large, complex pieces of material, so we researched automated manufacturing for tent and sleeping bags. While we gained valuable insights from the analogous domain research, it made us realize that there is no automated process that closely matches parachute packing. We would have to see the process for ourselves to gain a better understanding of it.
Primary Research
Next, we drove down to Fort Gregg-Adams to perform a contextual inquiry. This is a great way to empathize with the user you are designing for—by going into their environment and watching them in action. We spent 2 days at the Fort, including touring the base and ADFSD facility, observing the riggers working, and conducting semi-structured interviews.
We used the AEIOU method, looking at Activities, Environment, Interactions, Objects, and the User. This process can involve asking questions, taking notes, and silently observing how the user does their task and how they interact with their environment. Our research produced a wealth of findings that we still had to make sense of.
Affinity Mapping
We took all of our findings and sorted them into groups based on similarities. From there, we could find common patterns and extract insights.
Some key findings were that there was a high incentive for efficiency—riggers can go home once they successfully complete 15 parachute packs. As a result, balancing efficiency with quality, especially during times of high stress like "push weeks", was very hard. The physically taxing, repetitive work often results in strain and injury. Finally, the work is hard, so boosting morale is important. They play loud music, compete with each other, and have a strong sense of camaraderie to get through the day.
Affinity mapping helped us extract insights. These took a few rounds of iteration before we solidified them. We had to summarize key findings, while also telling the client something they didn’t already know. In short, “...So What?”
From there, we could rework our insights into actionable questions, i.e "guiding principles", that acted as a base for the ideation process.
User Painpoints Map
While creating our insights, we also wanted to make a user journeymap. However, I thought it would be beneficial to map the pain points to each step of the packing process. Different physical stressors and injuries are accrued at different parts of the process, so visualizing them all would help us target pain points better for solutioning. We wrote an summarized version of the parachute packing process, then mapped physical pain points to it.
The process is broken up into stages, each with a rigger check ("RC")
Crazy 8s
Since we had principles to guide us, we could begin to think of potential solutions. We started with a modified version of “Crazy 8s”. Normally, you would have 8 minutes to draw 8 potential solutions to a problem statement. However, we did crazy “15” since we gave everyone 15 minutes per principle, and they could generate as many as they wanted. We generated 200 ideas, give or take a few.
Refining
We couldn’t show the client 200 ideas, so we took out duplicates, combined similar solutions, and sorted them into broader categories. Four main categories developed: ergonomics & physical assistance, workflow automation & tooling, in-process monitoring & data collection, and organizational changes. Now, we could sort the ideas based on how much they satisfied insights and see which categories offered the biggest gains.
Concept Sketches
Left with 4 solution categories, we wanted to visualize them in a way that could start a dialogue with the client. We opted for a sort of abridged storyboard—basically, concept sketches with added backstory. If we were to show them highly-polished, refined solutions, they naturally would be less inclined to comment negatively. This low-stakes depiction of our ideas allows the client to share their honest thoughts. I created these sketches digitally:
