University of Michigan Supply Chain Services Department

Receiving Operations Simulation

Final Report

To: Roy Yoo, Project Manager, UMHS, royyoo@med.umich.edu

Andrew Sweeney, Industrial Engineer, UMHS, answeene@med.umich.edu

Arnold Yin, Industrial Engineer, UMHS, aryin@med.umich.edu

Mary Garves, Industrial Engineer, UMHS, belwoodm@med.umich.edu

Mark P. Van Oyen, IOE 481 Professor, UofM vanoyen@umich.edu

Mary Duck, Industrial Engineer Expert, P&OA, mgduck@umich.edu

From: IOE 481 – Team 7 – Fall 2016John LiJamie NolanNaina Singh

Date: December 13, 2016

Table of Contents

List of Tables and Figures 2Executive Summary 3Introduction 6Background 7

Key Issues/Problem Statement 8Goals and Objectives 8Project Scope 8

Methods 9Time Studies 9Measurements 9Surveys 10Literature Search 10

Findings 10Time Studies 10Measurements 11Surveys 12Literature Search 13

Process Simulation 14Assumptions 14Structure 14Findings 15

Recommendations 16Expected Impact 16

Appendix A: Data Collection Survey 17Appendix B: Hospital Blueprints 18Appendix C: Simulation Visual 19Appendix D: Simulation Code 20Appendix E: List of Assumptions 26Appendix F: Departments, Buildings, Room Numbers 27References 28

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List of Figures and Tables

Figure 1: Two-Way Matching Process 7Figure 2: Three-Way Matching Process 7

Table 1: Receiving Process Times 11Table 2: Time Taken to Travel 256 Feet 11Table 3: Distances Measured from Blueprints 12Table 4: Percentage of Packages by Room 12Table 5: Delivery Truck Arrivals 13Table 6: Elevator Wait Time Distributions 14Table 7: Simulation Results Summary 15

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Executive Summary

The University of Michigan Health System (UMHS) has thousands of patients and employees daily. However, in order for UMHS to operate smoothly, the health system receives many packages over the day. These packages include essential items such as time sensitive materials, surgical equipment, and everyday operational items. Currently, the hospital employs a two-way matching process to receive these packages. The two-way process involves the department ordering an item, the delivery service bringing the item to the dock, delivering the item to the department, and accounts payable paying once the invoice is received. The problem with this system is that there is no process to ensure that the correct package is arriving at the correct department. The hospital is spending an estimated $1.7 million annually due to departments repurchasing items that may have gotten lost, sent to another department, or never arrived at the health system [1]. In order to avoid these problems, UMHS has decided to switch to a three-way matching process, which is the industry standard.

The three-way matching process adds in a central receiving team that is responsible for verifying that the item is correct, recording the item into an ERP software called PeopleSoft, and then delivering the item to the specified department. The addition of central receiving ensures that items are not paid for until they have been properly received by the correct department. Because the hospital is such a vast system, the plan is to pilot the three-way matching process in 11 departments spanning three different buildings and six different rooms (see Appendix F).

The IOE 481 student project team from the University of Michigan Industrial and Operations Engineering department was asked by the UM Supply Chain Department to create a simulation of the future state of the central receiving portion of the three-way matching process. The goal was to give the department the capability to test different scenarios (staffing levels, number of flatbeds, optimal routes, etc.) without impacting UMHS operations and spending money.

Methods and Findings

In order to create a simulation that is as accurate as possible, the team needed to understand how central receiving would work and calculate the time of each step in the process. The team utilized four main methods of data collection: time studies, measurements, surveys, and literature studies.

Because the central receiving process does not exist yet, the team replicated the process in person and timed each of the steps. In order to get walking speed with a flatbed, the time taken for one of the members of the team to walk 256 feet with the flatbed was recorded. The result was an average speed of approximately 3.5 feet/second (see Table 2). Furthermore, the team observed and timed hospital workers during the Owens & Minor (O&M) pilot, which was a small scale

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trial of central receiving conducted by the Supply Chain Department. Using this data and the assumption that the process will take slightly longer when done for all the other delivery services, the team calculated that the amount of time to verify the package and enter it into PeopleSoft was uniformly distributed between one and five minutes (see Table 1).

In order to calculate the total distance traveled for each route, the team used blueprints of the hospital to draw out the paths and then convert the values to feet. This data was then converted to minutes with the speed of 3.5 feet/second that was calculated earlier (see Table 3).

The team utilized the Supply Chain department to get the truck arrival times, the package counts, and the package room destinations. From surveys sent out to the dock staff, the team attained the truck arrival times and calculated the number of packages on each truck (see Table 5). The team sent out surveys from 11/11/2016 to 11/23/2016 and received back data from the room staff. The UH – Operating Rooms (UH OR) data was the most complete so the team used this data as a basis for all the other room distribution data (see Table 4).

Lastly, each route involved an elevator with variable wait time. The team conducted a literature study to learn more about the distribution of elevator times. The team concluded that wait times were random and could be dependent on multiple different factors such as location of elevator, height of building, etc. [2]. In order to simplify the simulation, the team decided to base wait times on the time of day. The elevator would take the longest during lunch times and would be slightly busier than normal during shift changes. Also, since this is a hospital setting, a patient will always be prioritized. The team included a 1 in 20 possibility of a patient entering the elevator in a wheelchair or bed, causing the worker to have to wait for the next available elevator.

Building the Simulation

Once all the data was collected and the inputs of the simulation were calculated, the team began the process of building the simulation using ProModel software. The simulation is based on several assumptions, but the key assumptions are (see Appendix E for complete list):

1. This is a perfect world and all items are being delivered undamaged to the correct department

2. Each worker walks at the same speed3. Flatbeds leave docking area if they become full (30 items) or if a full hour has elapsed

since the first item was put on the flatbed4. Simulation runs from 5 AM - 1 PM (morning shift of the workers)

The basic format of the simulation includes eight locations (the dock where shipments arrive, the inspection area, and the six rooms). These are all connected by a path network which includes

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the times (including elevator wait times) it takes to get from one point to the other. Workers are resources that travel along this path network. The items are entities that arrive at the dock in several shipments and are inspected and loaded on a flatbed, which is another entity. The flatbeds are sorted by building. There are six flatbeds at the inspection area to ensure that if one flatbed is en route, another is still there to be loaded. Once the flatbed is full or an hour has elapsed, the worker moves the flatbed (with the loaded items) to the first room of whichever building the items are for. There, the flatbeds unload the items for that room and those items exit the system. The flatbeds continue, with the workers, to the second room in the building and empty the rest of their items, which also exit the system. At this point, the workers and the empty flatbed return back “home” to the inspection area (see Appendix C and D for visuals and code from simulation).

Results and Recommendations

The team focused on two main things when providing optimal inputs for the simulation: maximizing worker utilization and minimizing amount of time an item is in the system. The team ran the simulation for two to six workers. The data the team collected showed that having four workers was the middle point for both worker utilization (40.1%) and amount of time the item spent in the system (34.23 minutes) (see Table 7). The utilization of the workers is low because during their eight-hour shift, they will be doing other tasks such as clearing the work area and sorting other items not related to central receiving. The team also recommends that the items for CW are driven instead of walked. This is because even though the tasks take the same amount of time (both were simulated with the same data), driving takes less effort and reduces work for the workers in the dock.

Impact

The process of collecting data for the simulation has created a basis for similar data collection in the future. The biggest impact that this project has had on UMHS is providing the Supply Chain Department with a method to test variables and inputs without affecting daily operations of the hospital and spending money. The simulation is extremely fluid and can be adjusted very easily with the use of the detailed instructions the team provided to the Supply Chain Department. As UMHS pilots three-way matching in 11 departments in early 2017, the Supply Chain Department can continue to change data in the simulation. If this pilot is successful, UMHS will eventually expand the three-way matching process to all the other departments, in which case this simulation will prove extremely helpful.

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Introduction

The University of Michigan Health System (UMHS) is a large operation with thousands of patients and employees. In order to run smoothly and effectively, UMHS has about 1300 purchase orders delivered to its campus every week [1]. These shipments include expensive hospital equipment, temperature and time sensitive materials, or other items necessary for the hospital to function. Currently the system that the hospital employs to receive ordered packages is known as two-way matching, which involves matching the purchase order (PO) to the invoice. However, the department does not audit and enter the package into the system when it receives it. There have been a high number of costs to the hospital because of payments to vendors for untraced items. Because of the lack of a verification system, packages are delivered to the wrong department or never delivered. This usually results in the department that initially ordered them to place another purchase order. This means that the same item is being paid for twice. To eliminate this extra expense, UMHS is planning to switch to industry best practice, which is the three-way matching system. The new system adds a step to the process of receiving the ordered goods. After the good gets delivered to the Hospital’s dock, a central receiving team verifies the item, records it into a software, and then delivers it to the specific department that placed the order. UMHS needs a plan to implement the three-way matching system and ensure that operations run smoothly.

The UMHS Supply Chain Department is unsure of the exact details of the central receiving portion of the three-way matching process. They are uncertain of the time it takes to deliver items to their departments and the correct number of people that should be dedicated to the task. Because of these uncertainties, they asked an IOE 481 student project team from the University of Michigan Industrial and Operations Engineering department to create a simulation model of the future state of the central receiving portion of the three-way matching process. Modeling the future state gave the team insight into several segments such as delivery routes between the buildings, flatbed sizes, and staffing levels, which will help the Supply Chain Department pick the optimal inputs without physically testing all the variables when they expand the scope of the project to the whole hospital. The student team observed, tracked and analyzed the Owens & Minor (O&M) pilot conducted by the Supply Chain Department; conducted a literature review; surveyed key personnel; analyzed the data; and used ProModel software to simulate the future state of the central receiving portion of the UMHS three-way matching process. As a result of these tasks, the student team provided UMHS with an accurate model to simulate the new process with recommendations for staffing and resource levels. This report presents the team’s methods, findings, conclusions and recommendations.

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Background

The current two-way matching system involves matching the purchase order with the invoice to validate the vendor payment. Figure 1 below shows the process and who is responsible for each task. As Figure 1 shows, the department orders the item, the delivery service ships the item to the dock and to the department, and accounts payable receives the invoice and pays the vendor.

Figure 1: Two-way matching process

The two-way matching system has resulted in a monetary loss for UMHS because sometimes packages get lost before they reach a department or packages are sent to the wrong department—resulting in having to reorder the package. Costs due to voucher discrepancy adjustments are $0.5 million and the estimated costs due to invoice discrepancies are $1.2 million. In order to avoid these costs, UMHS is switching to three-way matching [1].

Figure 2 below shows the three-way matching process. The department still orders the item and the delivery service still ships it. However, this new process has a few additional steps involving a Central Receiving Team. When the product will arrive at the dock, the central receiving team will open the package (besides exceptions that cannot be opened and resealed), check that the correct item has been sent, and put it into the Enterprise Resource Planning (ERP) software called PeopleSoft. Then the dock staff will deliver the correct packages to their respective departments. In this new system, PeopleSoft will be used to match the purchase order, invoice, and receipt of the item to validate the vendor payment. As a result of this new implementation, UMHS will only be paying for the items they receive, thus saving them an estimated $1.7 million [1].

Figure 2: Three-way matching process

The UMHS Supply Chain Department plans to pilot the three-way matching process on 11 departments in January (Appendix F). They span three buildings and six rooms (two rooms in each building). This pilot will be a gateway to fully implement the new process to all of the docks and departments in the hospital.

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Before a large-scale pilot, however, UMHS piloted the central receiving portion of the process with O&M, one of its smaller delivery services. This pilot started on 10/10/16.

Key Issues

The greatest issue that UMHS faces because of the existing system is the lack of receipts or proof of delivery of the package. This issue results in the following issues:● Valuable packages getting lost or sent to the wrong destination● No method to track packages● Overpayment of packages that are billed incorrectly or not received● $1.7 million in losses annually

Goals and Objectives

To create an accurate simulation of the future state of the central receiving portion of the three-way matching process in UMHS, the IOE 481 student project team achieved the following tasks: ● Interviewed the receiving and dock staff to understand the current two-way matching

process● Collected measurements for simulation inputs● Conducted a literature search on elevator wait times ● Conducted time studies to calculate the time spent in each portion of the process of

receiving the packages by physically replicating the tasks and recording○ Time taken in an elevator○ Time taken to verify the correct package and its quantity○ Time taken for the product to be validated in PeopleSoft○ Time taken for the product to be delivered to its department

With this information, the team assisted the UMHS Supply Chain Department in the transition to three-way matching by providing them with: ● Accurate time and distance data that can later be reapplied and expanded to the rest of the

departments ● A simulation that will enable the Supply Chain Department to test different scenarios in

the three-way matching process such as optimal staffing levels, flatbed sizes, and routes

Project Scope

This project included only the central receiving portion of three-way matching process--the package receiving and delivery process. The process begins when the package arrives at the dock through UPS, FEDEX, or O&M and ends when the package is delivered to the correct department according to the PO of the package.

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This project did not look at the processes after delivery. It did not look at the invoice nor the payment to the vendor. This will be done by a different department than the receiving team and so was not part of the project. This project only looked at the 11 departments that have been chosen and did not look at the entire hospital. The departments span three buildings—University Hospital (UH), Cardiovascular Center (CVC), and Children’s and Women’s Hospital (CW). However, if this pilot is successful, there are future plans to expand to more departments in the hospital.

Methods

The methods used in this project to understand the process and gather data for the simulation are the following: time studies of segments of the process, route distance measurements, surveys of staff at the departments, and a literature search on elevator wait times.

Time Studies

The Supply Chain Department conducted a pilot with O&M, one of the delivery services UMHS uses. It started 10/10/16 and tested the central receiving process. O&M was selected for this pilot because of smaller package orders and convenience in modeling compared to larger delivery services such as UPS and FedEx. The Supply Chain Department and the team conducted time studies of tasks completed by workers in this pilot in three stages. First, the team timed how long it would take to deliver packages from the dock to each destination. Second, the team timed the verification and delivery of packages. Lastly, the team timed the verification, input into PeopleSoft, and delivery to each destination. This pilot allowed the team to see what the future central receiving process will look like in motion.

In order to collect the most accurate data possible, the team also conducted time studies in the dock warehouse. On 11/7/16, the team observed the workers and timed the process of finding the packing slip, verifying the contents, entering the information into PeopleSoft, and printing and stapling the summarized content data.

Furthermore, the team calculated the distance a worker travels with a flatbed to deliver the items from the dock to each destination. The team walked one of the three routes which was 256 feet with a flatbed, two times, and averaged the data to determine the amount of time an average worker takes to walk with the flatbed.

Measurements

It is necessary to know the distances of the paths taken to accurately predict the length of time a worker takes to deliver items to the correct destinations. The client provided the team with scaled hospital blueprints and the team used this information to calculate the distances of each of the

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routes that would be traveled by a worker (Appendix B). Distances were calculated by measuring the routes on the blueprints and using the scale provided to magnify the distances in the blueprint to actual distances.

Surveys

The arrival times of the different trucks were used in the simulation to understand when and how many packages were arriving at different times. The team was provided with the arrival logs from the guard shack that monitors truck arrivals and departures within the dock. The arrival log included 10/13-10/14 and 10/17-10/18.

Because the simulation is dependent on accurate input data, the team needed to know the distribution of items by delivery type (UPS Ground, FedEx Express, etc.) and destination (building and room). In order to find this distribution, the team conducted a survey for the inventory specialists at each room to fill out for every shipment that came in. This survey was based off of a similar survey used in the O&M pilot. The survey had several questions for specialists to answer, but the team focused on the total number of packages per room and the time the deliveries were made to the rooms (Appendix A).

Literature Search

A very large portion of the delivery time is spent taking an elevator because of the variability in the arrival of elevators. In any environment, an elevator’s arrival time can be due to many variables. Especially in a hospital setting, elevator wait times are highly dependent on emergencies and number of people in the hospital. For example, in the case of this project, patients always take priority over dock staff. Therefore, if patients are queued up for the elevators with equipment connected, the dock staff have to wait until there is enough space in the elevator to fit a flatbed. In order to research this variability, the team conducted literature studies on past projects involving elevators. The team looked at Worcester Polytechnic Institute’s study, “Need a Lift? An Elevator Queueing Problem,” and Columbia University’s “Elevator Scheduling” [1][2].

Findings and Conclusions

After conducting each of the four data collection methods, the team analyzed and compiled both qualitative and quantitative data to be used as input for the simulation model.

Time Studies

The time studies revealed a large distribution in the processing time of packages. Some packages had a packing slip readily available while others required a quick search to find the information.

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Furthermore, some packages were recorded one at a time while others were recorded in batches. The team learned that this was because not all packages were unique and so the workers grouped together the identical packages. Because of these variations, the team made several assumptions in order to calculate the verification time of packages as well as the time enter information into PeopleSoft. The breakdown of the receiving process is summarized in Table 1 below. The team noticed that the max time of 6 minutes and 28 seconds is an outlier and after removing this outlier and discussing with the client, the team concluded that the distribution is a uniform distribution with a mean of 3 minutes and a half range of 2 minutes.

Table 1: Receiving Process Times

Average Min Max

Find Packing Slip 00:13.74 00:05.47 00:31.54

Verify 00:35.76 00:06.43 03:12.49

Enter 00:40.90 00:21.60 02:16.52

Print 00:18.11 - -

Staple 00:10.23 - -

Total 01:58.73 01:01.84 06:28.29

During the time studies, the team noticed that with multiple people, many steps such as finding the packing slip, verifying and entering data could be done at the same time. Because of this, the team assumed one total time for all of these activities in the simulation.

The team walked from the dock to an elevator (256 feet) with a flatbed two times to find the time it takes to walk that distance. The distance was divided by the average speed to find the average walking speed of a worker with a flatbed to be 3.5 feet/second. The time taken to travel 256 feet is found in Table 2 below.

Table 2: Time Taken to Travel 256 Feet

Trial 1 2 Average

Time 1:16:99 1:12:56 1:14:78

Measurements

The distances calculated from the blueprints is summarized in Table 3 below. The team added the distances from the dock to the elevator and from the elevator to the room to calculate the distances.

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Table 3: Distances Measured from Blueprints

Building Dock to Room 1 (ft) Room 1 to Room 2 (ft) Room 2 to Dock (ft)

UH 306 506 432

CVC 553 173 628

CW 1665 313 1903

Since CW is very far from the dock, there is an option to drive from the UH dock to the CW dock. The walking route takes approximately the same time as the driving route so the team used the walking time for the simulation to model both options.

Surveys

Data collection ran from 11/11 to 11/23, and the team received information from 3 out of the 11 departments. The team determined the UH OR data was the most reliable because of their consistency in responses and used that information to determine the number of packages per PO. The client provided the team with historical data of average daily POs for 16 departments, including the 11 departments the team simulated. That data was used to calculate the percentage of POs to each room and then converted to number of packages with the information from UH OR. All of the data is based on the assumption that UH OR represents the typical day for all departments. The percentages are summarized in Table 4 below.

Table 4: Percentage of Packages by Room

Destination Room Number % of Total

UH Room 1 B1F244D 13%

UH Room 2 1D204 35%

CVC Room 1 2A581 13%

CVC Room 2 4747 17%

CW Room 1 6-610B 18%

CW Room 2 11-531 5%

The arrival times of UPS, FedEx Ground, FedEx Overnight, and FedEx Express were recorded separately and averaged to find a normal arrival time for each truck. UPS Overnight is not

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delivered to the dock so the team did not get arrival time information on that delivery service. In the simulation, the UPS Overnight package count is combined with the UPS Ground package count. O&M is delivered in the evening so for the simulation the team assumed the packages would be at the dock at the start of the morning at 5:00 A.M. The arrival times for the other delivery services are summarized in Table 5 below.

Table 5: Delivery Truck Arrivals

Type of Service

O&MFedEx

OvernightFedEx

GroundUPS Ground

FedEx Express

Arrival Time Night before 7:16 AM 8:22 AM 8:35 AM 9:01 AM

Number of Packages

23 17 52 56 122

Literature Search

The team conducted literature studies on how to evaluate and create an accurate elevator wait time distribution. A study conducted by the Worcester Polytechnic Institute concluded that a distribution can be created to estimate elevator wait times based on “total waiting time, number of stops, number of passengers, and highest floor” the elevator goes to [2]. An equation can then be created taking all these variables into account. In this case, another thing to include in the calculation would be time of day because the hospital elevators are most in use right before a shift starts (when people are arriving), during lunch time, and right after a shift ends (when people start leaving).

Another study by Columbia University talks about how elevator times are based on “nonhomogeneous stochastic arrivals of customers” [3]. The researchers at Columbia University talk about how customers arrive randomly and thus their wait times are random as well. Based on the information gathered from these two reports, the team gained further insight into how to model elevator wait times specific to the hospital. In order to create a simplified time distribution for elevators, the team combined the two ideas from the literature study to include the variables needed but also to account for the randomness of arrival rates. The team made a distribution based on the time of the day as shown in Table 6. The basis of the distribution was that elevators would be busiest during lunch hours and slightly busy during shift changes.

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Table 6: Elevator Wait Time Distributions

Time of Day Wait Time (sec)

7 AM-8 AM N(60,10) * 1.5

11 AM - 1 PM N(60,10) * 2.5

Other N(60,10)

The team put these distributions into the simulation and also included the possibility that the worker might have to give up his/her spot on the elevator to a patient, since patients take highest priority. The team assumed that the probability of this scenario occurring is 1 in 20.

Process Simulation

The team developed a ProModel simulation of the central receiving portion of the three-way matching process after analyzing sufficient data and gathering insight from the coordinators and client.

Assumptions

Because of the countless variables that affected the time taken for the item delivered, the simulation was created based on several assumptions. Key assumptions include (full list in Appendix E):

1. All workers travel at the same speed, which is based off of the team’s walking speed during time study trials.

2. Elevators take more time based on the time of the day. Elevators utilization is at the peak during lunch hour from 12 to 1 p.m. and is slightly higher than normal during shift changes.

3. All items are sorted correctly and go to the correct destination.4. There are no damaged items or items with incorrect P.O.s.5. The P.O. percentage is the same as the package distribution to each room.6. Package count and arrival times are based off of the UH OR survey data because of its

consistency and accuracy. This data is assumed to be the same across all of the other departments.

Structure

In the simulation, the entities are the items that arrive at the dock and travel through the hospital as well as the flatbeds that carry the items. The workers are a resource, and they are used to

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move with the entity throughout the routes. There are three routes and two locations in each route, which represent the actual routes taken to deliver to UH, CVC, and CW.

Figure 1 in appendix C is a snapshot of the finished ProModel simulation. The item is assigned an attribute named itemType on arrival. This itemType determines which route the item will take and follows a distribution centered around the data collected from surveys: 46% go to UH, 30% go to CVC, and 24% go to CW.

First, the item arrives at the dock, which is depicted as a building. When the item arrives, it will be placed on the flatbed according to its destination and a clock variable unique to the flatbed starts. The item then travels to the inspection area where it is inspected by a worker for U (3,2) minutes or a uniform distribution with a mean of 3 minutes and a half range of 2 minutes. The item sits at the inspection area until the flatbed is filled to capacity of 30 items or an hour has passed according to the clock variable. Once either condition has been met, the flatbed leaves the inspection area and travels the route and goes to each destination and unloads a proportion of the items based on a calculated distribution. Once the flatbed is empty, the flatbed and the worker go back to the inspection area.

Because of the variability in elevator times, the item will sit at the location for a random number of minutes which will be determined by a created distribution shown in Table 6. The proportion of packages that arrive at each destination is based off surveys collected from 11/11 to 11/23. Although only 3 out of 11 departments replied, the team assumed a general proportion that would fit throughout all the destinations.

Findings

The simulation was run for eight hours and the results using different numbers of workers are summarized in Table 7 below.

Table 7: Simulation Results Summary

Number of workers Average % UtilizationItem’s average time spent

in system (min)

2 69.4 51.16

3 65.5 40.63

4 40.1 34.23

5 32.0 33.58

6 27.8 34.00

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From the table above, using only two workers had the highest worker average utilization and the highest average time in system for an item at 69.4% and 51.16 minutes respectively. The focus was to find a balanced point with the maximum worker average percent utilization and the minimum average time for an item in the system. Utilization seems much less than 100% for because workers will do other tasks during the eight-hour period such as clearing the work area and storing items in the warehouse.

Recommendations After modeling the central receiving process on ProModel, the team developed recommendations for implementation of the central receiving portion of the three-way matching process. The team focused on maximizing worker utilization and minimizing the time to deliver packages. Based on the data the team gathered, the team recommends that UMHS utilize four workers because it is the balancing point where the average time in the system for an item is not too high (34.23 minutes) and the worker utilization is not too low (40.1%). The team also recommends using six flatbeds because as one flatbed has left for a building, the other flatbed for that building can continue to be loaded. Furthermore, it is more efficient to drive the truck from the dock to Children's and Women’s (CW) hospital instead of using a flatbed to walk the distance. This is because it takes approximately the same time but is a lot less effort required to drive than for a worker to be tied up in walking.

Expected Impact

The work the student team did throughout the semester will greatly benefit the UMHS Supply Chain Department. The team used data collection methods, such as time studies and surveys, that provided a basis for collecting information through which important information can be obtained in the future. Furthermore, the fluidity of the ProModel simulation allows UMHS to test various changes without an impact on the hospital’s operations. The simulation is highly dependent on input data, so as the department gains more data, they can edit the simulation. The team also inserted comments into the code so when someone tries to change an input, the person can easily pinpoint what to change, as shown in Appendix D.

As more accurate data is collected, the Supply Chain Department can edit the simulation to get more accurate results. For now, UMHS is only going to pilot 11 departments. However, there are hundreds of more departments and this simulation will prove extremely helpful when UMHS decides to expand three-way matching.

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Appendix A: Data Collection Survey

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Appendix B: Hospital Blueprints

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Appendix C: Simulation Visual

Icon KeyBuilding = DockGirl=WorkerGray table=Inspection AreaDesks=Rooms (Top 2=UH, Middle 2=CVC, Bottom 2=CW)

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Appendix D: Simulation Code

Time Units: Minutes Distance Units: Feet

********************************************************************************* Locations *********************************************************************************

Name Cap Units Stats Rules Cost --------------- --- ----- ----------- ---------- ------------ Dock Inf 1 Time Series Oldest, , Inspection_Area Inf 1 Time Series Oldest, , UH1 Inf 1 Time Series Oldest, , UH2 Inf 1 Time Series Oldest, , CVC1 Inf 1 Time Series Oldest, , CVC2 Inf 1 Time Series Oldest, , CW1 Inf 1 Time Series Oldest, , CW2 Inf 1 Time Series Oldest, , Inspect 1 1 Time Series Oldest, , Loc1 1 1 Time Series Oldest, , Loc2 1 1 Time Series Oldest, , Loc3 1 1 Time Series Oldest, , Loc4 1 1 Time Series Oldest, ,

********************************************************************************* Entities *********************************************************************************

Name Speed (fpm) Stats Cost ----------- ------------ ----------- ------------ item 210 Time Series flatbed_UH 210 Time Series flatbed_CVC 210 Time Series flatbed_CW 210 Time Series

********************************************************************************* Path Networks *********************************************************************************

Name Type T/S From To BI Dist/Time Speed Factor -------- ----------- ---------------- -------- -------- ---- ---------------- ------------ Net1 Passing Time N1 N2 Bi 87+N(60,10) SEC N2 N3 Bi 145+N(60,10) SEC N3 N1 Bi 123+N(60,10) SEC N1 N4 Bi 158+N(60,10) SEC N4 N5 Bi 50+N(60,10) SEC N5 N1 Bi 179+N(60,10) SEC N1 N6 Bi 476+N(60,10) SEC N6 N7 Bi 89+N(60,10) SEC N7 N1 Bi 544+N(60,10) SEC

********************************************************************************* Interfaces *********************************************************************************

Net Node Location ---------- ---------- --------------- Net1 N1 Inspection_Area N2 UH1

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N3 UH2 N4 CVC1 N5 CVC2 N6 CW1 N7 CW2

********************************************************************************* Mapping *********************************************************************************

Net From To Dest ---------- ---------- ---------- ------------ Net1 N2 N1 N3 N1 N4 N1 N5 N1 N6 N1 N7 N1 N1 N2 N3 N2 N1 N3 N2 N3 N1 N4 N5 N4 N1 N5 N4 N5 N1 N6 N7 N6 N1 N7 N6 N7

********************************************************************************* Resources *********************************************************************************

Res Ent Name Units Stats Search Search Path Motion Cost -------- ----- -------- ------- ------ ---------- ------------------- ------------ Worker 5 By Unit Closest Oldest Net1 Empty: 210 fpm Home: N1 Full: 210 fpm (Return) Pickup: 10 Seconds Deposit: 10 Seconds

********************************************************************************* Processing *********************************************************************************

Process Routing

Entity Location Operation Blk Output Destination Rule Move Logic ----------- --------------- ------------------ ---- ----------- --------------- ------- ------------ item Dock //Wait 5 min If itemType = 1 Then { numUH = numUH + 1 //number of items on the UH flatbed If numUH = 1 Then { clock1 = Clock(hr) //clock begins when the first UH item is loaded on the flatbed }

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} If itemType = 2 Then { numCVC = numCVC + 1 If numCVC = 1 Then { clock2 = Clock(hr) } } If itemType = 3 Then { numCW = numCW + 1 If numCW = 1 Then { clock3 = Clock(hr) } } 1 item Inspection_Area Load 1 flatbed_UH Inspection_Area Use Worker For U(3,2) min Free Worker Wait Until (numUH>30 Or (Clock(hr) > (clock1 + 1))) //flatbed does not leave until there are 30 items or an hour has passed Load numUH If (numUH>30 Or (Clock(hr) = clock1 + 1)) Then{ numItems=numUH numUH=0 clock1=0 //reset the item count on the flatbed and the clock to 0 } 1 flatbed_UH UH1 FIRST 1 Move With Worker flatbed_UH UH1 Int num=numItems*.27 numItems=numItems-num Unload num 1 flatbed_UH UH2 FIRST 1 Move With Worker flatbed_UH UH2 Unload numItems 1 flatbed_UH Inspection_Area FIRST 1 Free Worker item UH1 1 item EXIT FIRST 1 item UH2 1 item EXIT FIRST 1 flatbed_CVC Inspection_Area Use Worker For U(3,2) min Free Worker Wait Until (numCVC>30 Or (Clock(hr) > (clock2 + 1))) Load numCVC If (numCVC>30 Or (Clock(hr) = clock2 + 1)) Then{ numItems=numCVC numCVC=0 leftCVC=0 clock2=0

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} 1 flatbed_CVC CVC1 FIRST 1 Move with Worker flatbed_CVC CVC1 Int num=numItems*.42 numItems=numItems-num Unload num 1 flatbed_CVC CVC2 FIRST 1 Move with Worker flatbed_CVC CVC2 Unload numItems 1 flatbed_CVC Inspection_Area FIRST 1 Free Worker item CVC1 1 item EXIT FIRST 1 item CVC2 1 item EXIT FIRST 1 flatbed_CW Inspection_Area Use Worker For U(3,2) min Free Worker Wait Until (numCW>30 Or (Clock(hr) > (clock3 + 1))) Load numCW If (numCW>30 Or (Clock(hr) = clock3 + 1)) Then{ numItems=numCW numCW=0 clock3=0 } 1 flatbed_CW CW1 FIRST 1 Move with Worker flatbed_CW CW1 Int num = numItems*.79 numItems = numItems-num Unload num 1 flatbed_CW CW2 FIRST 1 Move with Worker flatbed_CW CW2 Unload numItems 1 flatbed_CW Inspection_Area FIRST 1 Free Worker item CW1 1 item EXIT FIRST 1 item CW2 1 item EXIT FIRST 1

********************************************************************************* Arrivals *********************************************************************************

Entity Location Qty Each First Time Occurrences Frequency Logic ----------- --------------- ---------- ---------- ----------- ---------- ------------ item Dock 23 0 1 // O&M delivery // begins at 5:00 AM // Assign random number to determine itemtype & destination Int x= rand(100) If x<47 Then { itemType=1 //Destination is UH } Else If x<77 Then { itemType=2 //Destination is CVC } Else { itemType=3 //Destination is CW } item Dock 17 136 min 1 // FedEx Overnight // 7:16 AM // Assign random number to determine itemtype & destination Int x= rand(100)

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If x<47 Then { itemType=1 } Else If x<77 Then { itemType=2 } Else { itemType=3 } flatbed_UH Inspection_Area 2 0 1 flatbed_CVC Inspection_Area 2 0 1 flatbed_CW Inspection_Area 2 0 1 item Dock 52 202 min 1 // FedEx Ground // 8:22 AM // Assign random number to determine itemtype & destination Int x= rand(100) If x<47 Then { itemType=1 } Else If x<77 Then { itemType=2 } Else { itemType=3 } item Dock 85 215 min 1 // UPS Ground + overnight // 8:35 AM // Assign random number to determine itemtype & destination Int x= rand(100) If x<47 Then { itemType=1 } Else If x<77 Then { itemType=2 } Else { itemType=3 } item Dock 122 241 min 1 // FedEx Express // 9:01 AM

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// Assign random number to determine itemtype & destination Int x= rand(100) If x<47 Then { itemType=1 } Else If x<77 Then { itemType=2 } Else { itemType=3 }

********************************************************************************* Attributes *********************************************************************************

ID Type Classification ----------- ------------ -------------- itemType Integer Entity numItems Integer Entity arrivalTime Integer Entity

********************************************************************************* Variables (global) *********************************************************************************

ID Type Initial value Stats ---------- ------------ ------------- ----------- numUH Integer 0 Time Series numCVC Integer 0 Time Series numCW Integer 0 Time Series timeUH Real 0 Time Series timeCVC Real 0 Time Series timeCW Real 0 Time Series leftUH Integer 0 Time Series clock1 Real 0 Time Series clock2 Real 0 Time Series clock3 Real 0 Time Series leftCVC Integer 0 Time Series leftCW Integer 0 Time Series

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Appendix E: List of Assumptions

1. All workers travel at the same speed, which is based off of the team’s walking speed during time study trials.

2. Elevators take more time based on the time of the day. Elevators utilization is at the peak during lunch hour from 12 to 1 p.m. and is slightly higher than normal during shift changes.

3. All items are sorted correctly and go to the correct destination.4. There are no damaged items or items with incorrect P.O.s5. The P.O. percentage is the same as the package distribution to each room.6. Package count and arrival times are based off of the UH OR survey data because of its

consistency and accuracy. This data is assumed to be the same across all of the other departments.

7. Picking up and dropping off packages takes workers 10 seconds8. The time it takes workers to turn corners is negligible9. The weight of the items on the flatbed does not affect the speed of the worker10. Taking the flatbeds to the destinations takes priority over verifying and recording the

items at the dock11. The number of items arriving in each delivery type is calculated by taking the

percentages of each type (gathered from survey in Appendix A) and multiplying it by the average total number of products delivered in a day (totaled from survey in Appendix A)

12. The distribution of U(3,2) for amount of time to verify and record each package comes from the time study data collected and the assumption that in the real process, it will take longer time than during the time studies

13. Worker speed is not affected by how crowded the hallways are14. Flatbeds leave docking area if they become full (30 items) or if a full hour has elapsed

since the first item was put on the flatbed15. Simulation runs from 5 AM - 1 PM (morning shift of the workers)16. The probability that a worker will have to give up a spot on the elevator for a patient is 1

in 20

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Appendix F: Departments, Buildings, Room Numbers

DeptID Dept Description Room # Location314730 CW Oper Room - Mott 6-610B CW314732 CW Oper Room-Mott-Instr 6-610B CW315490 CW Ped Cardiology - Tech CW 11-531 CW314640 UMH Oper Rooms - UH 1D204 UH314642 UMH Oper Rooms - UH - Inst 1D204 UH315979 UMH Radiology UH B1F244D UH315980 CW Radiology C & W B1F244D UH317688 UMH Operating Rooms - CVC CVC 4747 CVC315380 UMH Cardiac Cath Lab Tech 2A581 CVC314467 UMH Electrophysio Technical 2A581 CVC315910 UMH CVC Rad CVC 4747 CVC

UH → University HospitalCVC → Cardiovascular CenterCW → Children’s and Women’s Hospital

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References

[1] R. Yoo. “Three-way Match and Receiving.” University Hospital: Ann Arbor, Michigan. Presentation. 16 Sept. 2016.

[2] A. Hsu, R. LaBarre, S. Stricevic, “Need a Lift? An Elevator Queueing Problem,”Worcester Polytechnic Institute: Worcester, Massachusetts. PDF. 18 Oct. 2016

<http://www.math.wpi.edu/MPI2003/UTRC/UTRC03.pdf>.

[3] J. Dong, Q. Zafar, “Elevator Scheduling.” Columbia University: New York, New York.PDF. 20 Oct. 2016 <http://www.columbia.edu/~cs2035/courses/ieor4405.S13/p14.pdf>.

[4] “ProModel 2014 Help System.” ProModel Corporation: Orem, Utah. 11 Dec. 2016 <https://www.ProModel.com/onlinehelp/ProModel/91/C-01%20-%20Welcome%20to%20ProModel.htm>.

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