Multidisciplinary Senior Design ConferenceKate Gleason College of Engineering

Rochester Institute of TechnologyRochester, New York 14623

Project Number: 16702

FABRIC SHREDDING PROCESS & FACILITY DESIGN

Alexis LeshkoIndustrial Engineer

Kristen HennesseyIndustrial Engineer

Burake TayeIndustrial Engineer

Chris HaluszczakIndustrial Engineer

Matt BabcockIndustrial Engineer

Steven WaltonIndustrial Engineer

AbstractCurrently, Goodwill of the Finger Lakes (GFL) collects their unusable or unsold textiles and sells them to other companies for revenue that goes directly to other GFL projects and technology. This eliminates the possibility of generating additional revenue by processing the textiles in house, and then selling the upcycled material to companies for higher revenue. Unfortunately, there is currently no GFL-owned facility or process in place where this is done.

The overall goal of this project was to increase the revenue stream of Goodwill of the Finger Lakes by developing a facility layout and an upcycling process that would allow GFL to rework textiles and sell them to other companies for higher profit. The end result was the handoff of an accurate simulation model(s) of the overall system, which included a facility design(s), and may eventually turn into reality. Additionally, a cost analysis was presented that enabled GFL to understand the costs that go into this type of venture in greater detail. The key restraint was that the new process is monetarily efficient; meaning that it there is a high return on investment and short payback period.

MSD IOverviewApart from the necessary ethical and safety requirements, the main customer requirements centered a variety of categories: profitability, resources, capability, and decision making. More specifically they included, a quick return on investment, a process that would maximize profit per pound of donated material, a variety of product opportunities to increase the value of the shredded fabric, a specification on personnel (including staffing per shifts, # of shifts, etc.), and a specification on projected initial investment and return on investment. The most critical requirement was that the upcycling process be more profitable than selling the material to a secondary company, which is the current process. There were many other requirements that were listed, but were ranked as highly on criticality and importance.

Despite a fairly comprehensive set of customer requirements, the initial struggle that the team faced was an unclear set of deliverables. How would those customer requirements be met? To overcome this, the first few weeks were dedicated to deciding team member roles, project deadlines, project scope, customer requirements, engineering metrics, and risks associated with the project. This was accomplished by group discussions as well as meetings with the customer every three weeks. These meetings were invaluable to

1

Proceedings of the Multidisciplinary Senior Design Conference Page 2

the pinpointing of project scope and deliverables. Throughout this “feeling out” process the team did their due diligence in building a familiarity with textile upcycling processes. What did they include? Were there companies doing this already? Who could we contact to gain information? What are the inputs? And what are the outputs?

After the project scope was more accurately defined and the group established a more concrete set of deliverables, the team was able to move forward in a more uniform and deliberate direction. Through initial research the team found that many textile upcycling processes are distinct to the organization, therefore it was critical that the team reach out to as many companies and points of contact that we could in order to understand those unknowns. A point of contacts list was created that included all the people/companies being contacted, their contact information, reasons for contacting them, information gained, and whether or not they were a helpful resource. This would eventually turn into a deliverable to the customer at the conclusion of the project, giving them a starting point if they needed/wanted to research the topic further in the future. Through carefully crafted emails and phone calls, the team was able to extract valuable information from a variety of sources.

Through this research we were able to clearly define process steps in textile upcycling: sorting, shredding, mixing, and baling. In order to make the research a more targeted effort the six person team was split into smaller teams of two, focusing on specific parts of the line. However, at this point, the team was still lacking a definitive output of the process. Armed with weeks of research and a couple of recommended paths forward, the team had a meeting with the customer and decided on selecting cotton based insulation as a the primary result of the upcycling line.

What are these processes, what do they do and how?Sorting: Separation of incoming textiles according to a variety of conditions such as color, cotton content, and type. This can be done either by hand or by near-infrared (NIR) technology-based machinery.Shredding: Ripping and tearing of textiles, changing them from full articles to shredded strips.Mixing: Pulling apart the strips, reducing them to a more fine granularity (more of a cotton fluff material)Insulation Process: This includes many processes, but it was not researched in depth. It takes the fibrous fluffy material and sends it through a variety of steps that eventually turn it into a final product of insulation. Baling: No matter the material it must be put into a shippable form. Baling compresses/condenses material into squares for easier transport.

After 15 weeks of consultation with company representatives, online resources, and subject matter experts the team was finally ready to begin the push to complete the previously established deliverables, with some small adjustments. Initially the team and customer agreed upon three main deliverables, a simulation, and CAD layout of the future facility, as well as a cost analysis. The cost analysis was started early on in the project, but as time passed and more information was revealed through research and customer interaction it was determined that the evolution of the cost analysis would have to include far more than anyone had originally anticipated.

MSD IICost Breakdown Tool

The cost breakdown tool was not an original deliverable anticipated by the team, but rather, as the project moved forward, interactions with the customer led to the conceptualization of a tool focused around flexibility of use. Flexibility was quickly identified as one of the customer’s main concerns because the implementation of a textile upcycling process/facility was more than likely years away, and therefore

Project PXXXXX

having a tool that could be updated as new information arrives was crucial. Also, flexibility was necessary because the upcycling line could be configured in a many different ways. One of the fundamental differences was that it could use a full line to get from textile to a substance suitable for insulation production, or use individual machines that would do the same function. A full line takes the place of the shredding, mixing, and baling processes, forming them into one line of machines connected by internal conveyance. This key difference would also need to be captured in order to keep comparative options available.

Therefore, as the team continued researching what it would take to upcycle textiles from basic clothing to insulation, it was decided that a tool would need to be designed and implemented that could easily compare and contrast machine selections made by the customer. This was done with the knowledge that the machine selection of today may not be the ideal for tomorrow. That being said, the team knew that the tool could get rather complicated, and therefore in addition to the flexibility component, the tool needed to be as simple to use as possible. It was decided that Excel would the platform as it is widely used in industry and can be easily taught. In order to keep the tool functional, readable, and easily understood, a set of instructions were developed that not only guide the user through inputting the variables necessary to make the tool work, but also on how to read the outputs and optimize the system. With the input of system metrics, machine variables, and financial metrics, the tool would be able to output the recommended number of machines as well as the resulting financials outcomes.

In addition to the aforementioned inputs and outputs, the user would also include a whole slew of indirect costs associated with the facility as a whole, such as material handling equipment and machine installation fees. These costs would be separated into initial and annual costs and added into the bottom line figures.

3

Proceedings of the Multidisciplinary Senior Design Conference Page 4

Figure 1. A partial snapshot of the user interface of the cost tool.

Project PXXXXX

Along the way, the team realized that we were not the experts on all of the facts and figures that were being implemented into the tool; therefore, additional help was sought early and often. Subject matter experts, such as Qian Song, an accounting professor, and Tony DiVasta, an associated at Harris and an adjunct professor teaching facilities planning, were consulted in order to gain valuable insight on how different figures are calculated and what costs should be included that perhaps the team was overlooking. Their input made us continually rethink and fact check our results. Additionally, team members continually fact/sanity checked the numbers and formulas, constantly trying to find flaws in the logic in order to build a robust tool.

In conclusion, the tool became an incredibly powerful tool with a lot of functionality, while maintaining ease of use. Its main goal was to optimize the utilization of the line using a set of selected machines and their outlined costs, resulting in the optimization of annual profit. Data validation techniques were implemented to give the tool direct control over particular cells, not allowing the user to accidentally input numbers in places where they shouldn’t. Its diverse functionality coupled with its simplistic use made this cost breakdown tool a major deliverable of the project.

Simulation

The initial role of the simulation was to corroborate the output given by the cost tool but this thinking was dismissed for the two following reasons. Simulations give snapshots of a system at specific states which can vary drastically from the implemented state and the outputs are only as accurate as the data inputted. As the team approached the second phase of the project they began to realize that the majority of the facility’s data--specifically costs and revenue-- was more theoretical with a considerable amount of assumptions in place. The model was implemented using Simio software and it was designed to be a discrete event simulation as opposed to a system dynamic or agent based model. This modeling method was chosen given the background knowledge of the modelers as well as it being the most common method used in industry. The entire system is represented as a non-terminating system to represent only operational times. The overall process is represented in the following figure.

Figure 2. A depiction of the flow of material through the process steps (before the insulation process).

The entities arrive in the form of gaylords according to discrete distributions allowing ¾ of the shipments to be the dimensional size of a pallet truck with the last ¼ being the size of a container truck--21 gaylords and 52 gaylords respectively. They then move to inbound receiving, scanned in with a 10 minute cycle time and proceed to sorting which is at a variable rate according to the following equation.

Number Of Sorters∗Sorting Rate (kg /hr) = Sorting Flow Rate

5

Once the gaylords enter sorting, they are converted to fluid in the form of a textile entity using Simio’s flow library with a weight of 2kg per discrete entity. This “fluid” is then sent to the Whole Garment Baler--which makes a bale every 500kg of textile-- or the cutting process which feeds into the rest of the processes via a dynamic flow relationship. This meant that the flow between these two points will automatically correct itself when one avenue is at capacity. This method applies to all baler branches and is based on the assumption that the operator wouldn’t continue feeding the line when it is full.

When bales are made, they are transported to the outbound shipping area as container entity bales riding on a forklift (transporter entity). This movement purposely doesn’t take any time to account for variability in the shipping process. The shipping process is implemented using the Simio flow library in the following way. The bales are emptied and filled onto the outbound truck likened to a tap filling a container. This was done to more accurately represent the New York Department of Transportation requirements for single axle and multiple axle transporters having a maximum weight of 22,400 and 36,000 lbs respectively.

Once the architecture of the simulation was in place, experiments were created to analyze the throughput and capacity constraints of the system in the form of maximizing the number of completed shipments. A summary of the initial experimental logic is as follows, the paths to each baler will toggle on and off in random scenarios. The workforce and process parameter were varied according to Tables 1 and 2 shown below. It is worth reiterating that these parameters are the initial set and change based on further analysis.

Table 1. Initial Experimental Parameters for the workforce requirements

6

Table 2. Initial Experimental Parameters for the process

Facility Layout

From the initial proposal of the project, the facility layout was a required deliverable. However, the team wanted to identify the goals of the facility layout, the vision to get there, and the benefit to the customer. The ultimate vision of the facility layout was to provide useful information in a flexible manner. The ideal information sought by the customer was the minimum square footage of the overall facility, the specs of the machinery, and the unutilized space, the storage and movement of raw material, personnel, and finished product, and the utility requirements. By providing the minimum square footage associated with the elements of the overall facility, the team provides the customer with a baseline size for future potential locations. Further, providing the amount of unutilized or underutilized space, the customer will have a better understanding of their capacity for future expansions based on the recommended square footage. By demonstrating the storage and movement of raw material, personnel, and finished goods, the customer would be well equipped to implement the proper flow into future facilities. Lastly, through the research and compilation of the utility requirements, the customer would have a general idea of the electrical requirements needed to run the future facility. In addition to all the benefits listed for each part of the facility layout’s vision, flexibility was key and a huge benefit to the customer.

To achieve the goals of this deliverable, the team began working on the facility layout through further research and benchmarking. This research included determining the size of competitor facilities, the size requirements of the needed machinery, and the material handling systems. Initially, the team determined that there were two companies that had similar, if not identical, processes. Next, the team researched material storage and transport in addition to the machine requirements. Based on this information, an initial block layout was created to represent the machine footprint and estimates for storage for both inbound and outbound.

To further understand how to utilize the large amount of unused space, the team chose to reach out to the team’s guide and subject matter expert, John Kaemmerlen. After the meeting, the team had a list of facility elements and the estimated size associated with each to add to the layout. Some of these additions included forklift charging stations, office space, maintenance shop, and testing area. The team found

7

through the calculations for the storage space that the highest space utilization needs to be dedicated to inbound materials because the selected machinery can process more textiles than provided by the customer. On the other hand, the outgoing material would mainly be comprised of shredded and baled cotton textiles, and would be taken out of the facility fairly quickly, requiring less storage space. The team determined that the outgoing truck would cube out before it would weigh out. In other words, the truck would reach its volume limit before its weight limit. Because of this, the outbound goods required area was not a large amount and required considerably less storage than the inbound material. Lastly, two assumptions were made by the team regarding the facility. The first was that it would have four dock doors, with one being for a trash compactor, which was later deemed unnecessary. This was to allow two incoming trucks to be there at once and one outgoing. The second assumption was that the overall insulation process, which had not been researched by the team in depth, was to be double the square footage for the shredded processing area. In order to ensure that the facility was efficient and effective, the team analyzed the flow of the material. This flow was traced from the incoming dock, to the inbound storage, to the processing area and, finally, to the outbound storage. Lastly, to guarantee flexibility for the customer, the team completed the layout and used it as the base layout for four additional designs. These facility layout options were developed with variations in the number of docks, method of sorting, and mix of incoming product.

Figure 4. Base layout (automatic sorting and processing line/all cotton textiles)

The first variation of the base layout included moving two of the dock doors to the other side of the building to separate the inbound and outbound shipments. This also created flow across the facility instead of the other options which are a U-shape flow. The second variation of the base layout, seen below in Figure 5, involved manual sorting, a fully automated processing line, and a mix variety of materials in incoming textiles. The manual sorting resulted in a larger square footage requirement because it requires more personnel. Additionally, the mix of textiles created a need for an extra baler outside of the processing line to bale the non-cotton items.

8

Figure 3. Initial revision of the layout

Figure 5. Second variation of base with manual sorting and extra baler

Like the second variation, the third variation shown above in Figure 6, utilizes manual sorting. However, because this variation had no mix in the incoming textiles, the sorting area was much smaller. The layout also did not need to support the baling and shipping of non-cotton textiles, so an additional baler is not required. Because of the small sorting area and lack of need for an additional baler, this facility’s central processing area was smaller than the other variations, allowing the insulation process to move into the center of the facility.

Figure 6. Third variation with manual sorting and no textile mix

The fourth and final variation included automatic sorting and a mix of the incoming textiles. Because of the small required area by this set-up, a path for material handling vehicles was added through the facility’s center. This allows both the central processing area and the insulation processing areas to have a direct path to and from the shipping area. After analysis of several options, the recommended facility design includes automatic sorting, a full line, and an input stream of mixed textiles (not 100% cotton).

ConclusionsFifteen weeks of diligent research set a strong foundation for the execution and successful completion of all major project deliverables. The cost comparison tool used machine and process specific data in order to provide systematic and financial outputs. The simulation model used data from subject matter experts to output realistic throughput and capacity considerations. The facility layout benchmarked both equipment and facilities to provide an accurate representation of potential process flow within a given space. Each deliverable focused on providing accurate information with ultimate flexibility to aid in future decisions regarding textile upcycling processes. Each subgroup was effective in meeting its customer deliverables. They were instrumental in getting continual feedback from the customer in order to develop tools that satisfied customer requirements. The original focus was on the actual development of a facility, but the scope of the project was shrunk, which resulted in a stronger focus on the need for comparative tools for the decision making process.

9

Follow-UpDue to the limitation of time, there were some things left out of each subgroup project. The simulation could be expanded upon by adding usage costs across the facility. The cost tool could also be expanded through the implementation of a depreciation model of machinery and an extended list of additional costs to be added into the cost library. In its current state, the facility layouts are hypothetical, and therefore identifying particular building specifications would be ideal.

Overall as a company, before moving forward with the implementation of a facility it would be critical to see the equipment in use at another facility. This would allow for cross validation of the tools as well as the process itself. As aforementioned, the tools are for comparative purposes and should be used in conjunction with real world data and market research in order to make the most comprehensive decision.

AcknowledgmentsThe team would like to acknowledge (in no particular order) the groups that contributed their time, knowledge, and/or experience. First, the team would like to thank Goodwill of the Finger Lakes VP of Business Operations, Joyel Bennett, and Director of Sales & Business Development, Joe Kells, for taking the time to meet with the team and provide information on their vision. Additionally, the team would like to thank the relevant subject matter experts that provided insight and expertise where the team lacked. Specifically, John Kaemmerlen, the team’s advisor, Qian Song, Anthony DiVasta, and Dr. Michael Kuhl for their help with each sub-team.

10