ioe481/ioe481_past_reports/17f03.docx · web viewwith an understanding of the process, the team...

University of Michigan Health SystemPerformance Improvement

Michigan Medicine Laundry Transition to Cooperative LaundryFinal Report

Submitted to:

Robert Harris Associate Hospital Director

University of Michigan Health System

Rolando Croocks, B.S., RLLD. CLMLaundry Director


Robyn RachelProject Senior Manager


Jamie MacLaren Industrial Engineer


Andi Duma Performance Improvement Fellow


Mary DuckUMHS IOE 481 Liaison


Mark Van OyenIOE 481 Faculty Instructor

University of Michigan Industrial and Operations Engineering Department

Submitted by:

IOE 481 Project Team #3Jake Biegger

Ceyda BolukbasiFilmore Walker IV

December 12, 2017

Table of Contents

Executive Summary...……………………………………………………………………..3Background and Key Issues.………………………………………………………..3Methodology…………….………………………………………………………….4Findings and Conclusions…………………………………………………………..4Recommendations…………………………………………………………………..5

Introduction………………………………………………………………………………..6Background………………………………………………………………………………...6

Key Issues………………………………………………………………………….. 7Goals and Objectives………………………………………………………………. 7Project Scope…….………………………………………………………………… 8

Data & Methods………………………………………………………………………….. 8External Laundry Delivery Process………………………………………………... 8Internal Linen Distribution Process.……………………………………………….. 10Benchmarking Analysis…………………………………………………….............11Literature Search.…………………………………………………………………...12

Findings and Conclusions……………………………………………………………….. 12External Laundry Delivery Process………………………………………………. 12Internal Linen Distribution Process.……………………………………………….. 18Benchmarking Analysis……………………………………………………..............21Literature Search.…………………………………………………………………...28

Design Methods, Constraints, and Standards……………………………………………28Summary of Conclusions……………………………………………………………….....29Recommendations………………………………………………………………………....30

External Laundry Delivery Process………………………………………………... 30Internal Linen Distribution Process.……………………………………………….. 31Benchmarking Analysis……………………………………………………..............37Timeline……………………………………………………………………………..39

Expected Impact……………………………………………………….………..................40References……………….…………………………………………….…………………....41Appendix A: Internal Linen Distribution Data Collection Sheet ………………………….42Appendix B: Survey Questions Asked to COOPs ……….………….……………………..43Appendix C: Survey Questions Asked to Hospitals………………………………………..44Appendix D: Pugh Chart for Comparison of Future State Processes..……………………..45

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List of Tables and Figures

Table 1: Observations of the Laundry Delivery Process………………………………….. 13Table 2: Average Weekday Demands for Robos…………………………………………..13Table 3: Forecasted demands calculated using Holt’s Method ……………………………14Table 4: Scenario Macro Values ……….....……………………………………………….17Table 5: Results of the Simulated Scenarios …..…………………………………………..17Table 6: Last Three Scenarios Simulation Results ……………………………………….. 17Table 7: Summary of Conclusions …..……….…………………………………………....29

Figure 1: Designed Current State Process Flow ………………………………….……..... 18 Figure 2: Fishbone Diagram for Internal Laundry Distribution………….……………….. 19 Figure 3: Current State Process Time Breakdown (Actual)……….......………………….. 20 Figure 4: Wired Carts in Use………………………………………..…………………….. 26 Figure 5: Empty Robot En-Route…………………………………... ......…………….….. 26 Figure 6: Robot Technological Aids……………………………………………..………...27 Figure 7: General Future State Process Flow…….……………………………………….. 31 Figure 8: Inputs for Distribution Model……….………………………………………….. 32 Figure 9: Comparison of Process Steps for 3 Current vs. Future State (Same Time)…….. 35 Figure 10: Comparison of Process Steps for 3 Current vs. Future State (Different Time).. 36

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Executive Summary

Michigan Medicine currently has its own laundry operation, which includes production, delivery, and distribution; however, laundry production is being phased out and will be transitioned to a Cooperative Laundry (COOP) facility in Detroit. With this upcoming change, the Laundry Director for Michigan Medicine asked an IOE 481 project team from the University of Michigan to analyze the current state to design storage, transportation, and distribution improvements, conduct a benchmark analysis, and recommend a timeline for the transition to the COOP over the next two years.

After examining the current situation through observations, interviews, time studies, research, and benchmarking, the team has developed recommendations to analyze the external linen delivery process to the hospital, improve the internal linen distribution process, and ease the transition to the COOP. Through these insights and recommendations, the IOE 481 team believes that significant improvements can be achieved.

Background and Key IssuesIn 18-24 months, the laundry operation will be moved to a COOP facility in Detroit in conjunction with three other hospitals. The shift to the COOP will alter the laundry distribution process, but the new process has yet to be clearly defined in terms of distribution and delivery. Demand is expected to increase and the containers holding the laundry are expected to change, which must be accounted for when planning laundry delivery and determining a new storage depot’s size. For internal laundry distribution, the current process is not standardized and has unnecessary intermediary steps that create unneeded additional walking. Finally, UM Laundry Services needs a comprehensive and comparative benchmark study to understand the effects and possible improvements that can result from the use of a COOP.

Goals and ObjectivesThe main goals for this project include: analyze the current transportation, distribution, and storage processes, and benchmark other COOPs and hospitals to compare against operating laundry in-house. With this analysis, the team has developed insights for process improvements, determined the best system to store and transport linen, and developed a timeline to implement these recommendations.

Project ScopeThis analysis focuses on the distribution and delivery of laundry by analyzing the main transportation routes as well as the five main Michigan Medicine hospitals. This study also includes overall linen usage and projected future demand. The production aspect of laundry, offsite locations for deliveries, and ambulatory care unit deliveries are considered out-of-scope.

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Methodology The project was broken into three main sections for data collection and analysis: external laundry delivery process, internal linen distribution operations, and benchmarking analysis.

External Laundry Delivery ProcessThe team observed deliveries from Laundry Services to the hospitals. Given historical data of driver logs and delivery contents, the team observed four deliveries over the course of six hours to confirm the data. The team also interviewed the driver to understand the process from an employee’s perspective. The team used this data to simulate the delivery process through the use of ProModel and to forecast the future demand of laundry.

Internal Linen Distribution OperationsThe team examined employees at UH Main over three days. First, the team shadowed employees and conducted interviews to learn more about the general process. With an understanding of the process, the team created a current state process map and fishbone diagram to diagnose the current issues. Finally, the team conducted time studies to determine how long each step of the distribution process takes to allow for a comparison to the projected future state.

Benchmarking AnalysisThe team conducted its analysis by completing a literature search followed by collecting data from COOPs and hospitals. After creating two different surveys for COOPs and hospitals, the team reached out to three COOPs, two hospitals in Michigan, and 14 additional hospitals from a previous benchmarking project conducted in 2016. In addition to collecting data via surveys, the team also visited St. Joseph Mercy Health System to conduct on-site observations.

Findings and Conclusions With the data collected throughout the project, that team discovered many significant findings that led to conclusions about opportunities for improvement.

External Laundry Delivery ProcessUtilizing the historical data, the team was able to forecast future demand and simulate the delivery process. The team forecasted that the demand would increase 7% by December, 2019, and also forecasted at the Laundry Director’s prediction of 4% annually. These increased demands were simulated to view how they affected the current delivery schedule. Variables such as the container size were changed as well. The team determined that a long-term 25% increase in demand (plausible after new tower construction) is not feasible with the current schedule.

Internal Linen Distribution OperationsThe team discovered that the current state process for linen distribution at the hospital operates as a non-standardized process and also contains unnecessary additional walking. Employees spend 21% of the total process time walking to and from storage rooms, an additional 13%

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counting inventory, and are only now transitioning to technological aids. The team concluded that implementing a new standard process that reduces total trips and also eliminates the need for each worker to count inventory could save about six hours per day.

Benchmarking AnalysisThe team received COOP survey responses from Michigan Premier Laundry (MPL) and West Michigan Shared Laundry, and hospital survey responses from University of Wisconsin Health, University of Washington, and St. Joseph Mercy Health Services in Ann Arbor. After analyzing the data from literature searches, survey results, and an onsite hospital visit, the team determined the main improvements UM Laundry Services may need to make to allow for a smoother transition. These changes include the type of technological aids to use, safety stock presence, actions to take in emergency laundry needs, and number of unique items to process.

Recommendations With the findings, the team was able to make recommendations across all three areas of analysis as the operation transitions to a COOP.

External Laundry Delivery ProcessTo maintain the current delivery schedule, no change is needed for the forecasted 7% increase, while the team recommends that containers with 15% more capacity are used for the long term 25% increase. The team recommends that a safety stock equivalent to one day’s worth of clean laundry is stored at the depot. Note that more area will be needed for dirty laundry. With the 7% increase, the minimum capacity for safety stock at the depot is 663 ft2. For the 25% increase, the minimum capacity is 774 ft2.

Internal Linen Distribution OperationsThe team recommends that Laundry Services implement a future state process where inventory is counted beforehand so that every employee only has to make one trip instead of additional trips to count inventory needs. This implementation will happen in two iterations; the first of which during the 24-month transition period, and then a more significant nighttime iteration once the switch to the COOP is complete. This new process can save the hospital 6 and 13 hours per day respectively in addition to much less walking and wasted time for employees.

Benchmarking AnalysisThe team recommends two possible improvement measures that supplement external and internal process recommendations. The first set of measures involve minor changes by decreasing dead linen and having minimal safety-stock storage in-hospital. The second set of measures involves major changes like using RFIDs, weighted smart shelves, and integrating new software aids and automation for increasing efficiency with the switch to the COOP. Introduction

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Michigan Medicine currently has its own laundry operation, which includes production, delivery, and distribution; however, the production of the laundry is being phased out and will be transitioned to a Cooperative Laundry (COOP) facility. The new COOP facility will be based in Detroit and will eliminate the need for Michigan Medicine to clean its own laundry. The remaining responsibilities left to Michigan Medicine will be identifying a location for holding clean and soiled linen, maintaining current delivery schedule of clean linen to the hospital, sustaining appropriate levels of inventory at the depot, and developing a plan for handling specialty items. This transition to the COOP is expected to be completed in the next 18-24 months, by September 2019.

The change to COOP still has many undefined aspects, so it is necessary to analyze the current system and identify process improvements that can be implemented during the transition to the future state. With this upcoming change, the Laundry Director for Michigan Medicine asked an IOE 481 project team from the University of Michigan to determine the most effective way for a smooth transition by analyzing the current situation and designing storage, transportation, and distribution improvements. Furthermore, the project team was asked to identify potential impacts of being in a COOP. After completing these tasks, the IOE 481 team presented its findings and a timeline for implementing potential improvements leading to the transition to the COOP. This report presents the team’s methodology, findings, conclusions, design recommendations, and in conclusion, the expected impact of these recommendations.

Background

Laundry operations for Michigan Medicine have always been conducted by staff on the University’s campus, which includes everything from transporting soiled laundry from the hospitals to delivering clean laundry back to the hospitals. However, in 18-24 months, this operation will be moved to a cooperative laundry facility in Detroit in conjunction with three other hospitals. This transition is a result of a value margin improvement project that was undertaken by the University in 2014 to reduce costs. This new operation will lead to the eventual elimination of the majority of UM laundry production services, but certain services such as laundry transportation and a storage depot will be needed. The current laundry system, which is fully operated by Michigan Medicine, processes 138 unique items and is responsible for transportation from the laundry facility to the hospitals. For FY 2017 (through September), 9,204,168 pounds of laundry have been processed with a total expense of $8,061,443 [1]. The process for laundry operations begins with processing at an UM-owned, offsite laundry facility. Then, the laundry is transported by truck to either University Hospital South (UH) or the Children and Women’s Hospital (CW). Here, it is stored in a holding area where stock keepers move the linen to different areas of the hospital based on physical inventory counts. After

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laundry has been soiled, it is trucked back to the laundry operations facility where the process then repeats.

Key Issues

The following issues are the factors driving the need for this project: The shift to the COOP will alter the entire laundry process by removing the production

aspect of the current system The new COOP process has yet to be clearly defined in terms of distribution and

delivery, so this system may need to be redesigned The total number of unique Michigan Medicine linen items will need to be reduced in

order to conform with the COOP’s objective of processing linen in a highly automated fashion. Michigan Medicine has a number of items that other members of the COOP do not use and that will not be serviced by the COOP. This is an on-going process, and the Linen Committee is currently working towards reducing the number of unique items

The necessary capacity of a depot, the intermediary storage unit, needs to be determined based on future projected demand

Goals and Objectives

Assessing the current state of Michigan Medicine Laundry Services will allow for an effective transition to the COOP system. The team did this by completing the following tasks:

Analyzed the current transportation, distribution, and storage processes for comparison to future state

Benchmarked other COOPs and hospitals to determine potential impacts of being in a COOP as opposed to operating an in-house laundry operation

Assessed potential service impacts to Michigan Medicine

With this analysis, the team provided information and developed recommendations to: Implement process improvements for the future state COOP Determine best system to store and transport linens with transition to the COOP Determine possible advantages and service impacts with transition to the COOP Develop a timeline to implement the recommendations during the transition to COOP

Project Scope

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This project focuses on the distribution and delivery steps of the laundry process by analyzing the main transportation routes as well as the five main Michigan Medicine hospitals. This study includes overall linen usage, projected future demand, and in-house deliveries. Benchmarking studies are also conducted on two COOPS and three hospitals.

The production aspect of the laundry operation, which is defined as the actual process of washing laundry, is not included in this project. Also, the offsite locations for linen deliveries are not studied. Ambulatory care unit deliveries are considered out-of-scope for this project. Finally, hospital systems that vary significantly from the UM system are not studied and compared in detail.

Data and Methods

To analyze the current Michigan Medicine laundry operations, develop insights into how the transition to COOP will affect operations, and ultimately create a timeline for the changes as the laundry process is transitioned to a COOP, the team completed this project in three phases: data collection, data analysis, and recommendations.

The subsequent sections describe the data collection methods for the external laundry delivery process, internal linen distribution operations at the hospital, benchmarking analysis, and literature search.

External Laundry Delivery Process

Laundry is currently delivered in containers called robos, and the external delivery process takes place every day. Three deliveries are scheduled to UH and two deliveries are made to CW Sunday through Friday. On Saturday, there are three and one deliveries, respectively, to the different hospitals. The deliveries are made between the times of 6:00 a.m. and 3:00 p.m.

Shadowing and Time StudiesThe team performed a walkthrough of the current laundry processing at Laundry Services and gained a general understanding of the external delivery system. The team then observed three laundry deliveries from Laundry Services to the hospitals on October 17, 2017 from 6:00 a.m. to 11:00 a.m. Time studies were performed and truck utilization was recorded in terms of number of robos per truck. The time studies included:

Loading time of robos with clean laundry into the truck Travel time from Laundry Services to the hospital

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Unloading / loading time of clean laundry / empty robos Travel time from the hospital to Laundry Services

Historical DataTo verify and supplement this data collection, the team requested that historical data of deliveries be provided. This was due to the large amounts of data needed for the analysis, which was infeasible for the team to collect themselves. The Laundry Operations Staff Manager supplied the team with year-to-date (January 1 – October 17) hard copies of data. This data includes:

Driver logs that record the times for deliveries [2] Number of robos (demand) of each laundry item delivered to the hospital for each

delivery [3] Average number of laundry items per robo [4]

Forecasting Methodology The team also used a forecasting method to estimate the future demand of laundry when the transition to the COOP takes place. The Laundry Director expects an increase in demand of 4% annually. Due to the trend of the demand found in the historical data, the team used Holt’s Method for forecasting. Holt’s Method, also known as Double Exponential Smoothing, is a forecasting model that allows the forecasting of data with a trend. The model utilizes parameters that smooth two different equation: level (S) and slope (G).

α = Smoothing parameter for levelβ = Smoothing parameter for trendS0 = y-interceptG0 = slope

Level Equation: St = αDt + (1 – α) (St-1 + Gt-1)Trend Equation: Gt = β(St – St-1) + (1 – β)Gt-1

Forecast Equation: Ft,t+i = St + (i – t)Gt

Simulation MethodologyThe team utilized this data to perform simulation in ProModel and forecasting as forms of analysis on the historical data provided. Simulation not only acted as a validation for the data, but also allowed the team to simulate multiple scenarios in which certain variables are changed. The variables the team chose to alter are the demand, the size of the laundry container, and the truck capacity for those containers.

Internal Linen Distribution Operations

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The team observed the current process that employees follow at the hospital to distribute linen. The team first conducted two walk-throughs of the operation with the laundry director to gain a general understanding of the process.

Shadowing and InterviewsThe team then observed two shifts, one from 4 a.m. – 8:30 a.m., and another from 9 a.m. – 1 p.m. on October 19, 2017, to learn about the current state of laundry distribution in the hospital. While the original plan was to conduct time studies and inventory observations, the team learned that the process was not standardized, so collecting time studies was not easily possible. Instead, the team shadowed employees on their routes to learn about the waste currently associated with the process and also interviewed the employees. Overall, the team shadowed four employees for a total of about nine hours and took notes throughout this process. Following the completion of these observations, the team was able to make preliminary assessments of which aspects of the current process could be improved.

Determining Areas for ImprovementWith these preliminary assessments, the team created a current state process flow and diagnosed areas for improvement with a fishbone diagram. With a general idea of the methodology utilized to distribute the linen, the team focused on a redesign of the process to incorporate a standard procedure utilizing the new handheld scanner technology that is currently being implemented.

Time StudiesIn order to complete the data collection for analysis, the team learned more about the handheld scanner technology as well as collected various time measurements. With these time measurements, the team created a model to quantify the time savings associated with switching to a proposed future state design. While not every measurement could be collected and compared as a result of the non-standardized process, the team observed two more day-long shifts at the hospital on November 20, 2017 and November 28, 2017. The first day of observation yielded information about the time it takes employees to walk from the holding room to the clean rooms. The second day of observation yielded a more comprehensive measurement of the process; each worker was given a form (Appendix A) to self-collect process times of various different stages of the laundry distribution process.

The measurements employees were asked to collect included time to pack cart, time to walk from holding room to first clean room, time to count inventory needs at each clean room, time to walk back from last clean room to holding room, time to redistribute unused linen, and the total time of the process. By having employees self-collect this data, the team gained a holistic view of the laundry process with about 18 full routes.Benchmarking Analysis

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The team constructed a benchmarking analysis which compares similar functions within the same industry to determine possible improvement measures. The team began the functional benchmarking analysis by conducting a literature search to find advantages of using a COOP, general standards, common methods and technologies used, and how hospitals made the transition. Once the literature search outlined the important areas to focus on in benchmarking analysis, the team reached out to COOPs and other similar medical academic hospitals while continuously doing online research to support recommendations.

Finalizing Point of ContactsThe team acquired the names of the three main COOPs (MI Premier Laundry, West MI Shared Laundry, and North Grand River Cooperative) and contact information for two hospitals (St. Joseph Mercy Health System in Ann Arbor and St. John Providence) in Michigan from the Laundry Director. Later, the team acquired data from a benchmarking analysis conducted in 2016 which aimed to benchmark the number of distinct items, specialty items, need for space blankets, type of gowns, and scrub usage with other hospitals. Although the data itself was out of scope, the team was able to acquire the contact information of 14 hospitals from the benchmarking analysis [5]. The hospitals included in this initial study included: Stanford Health Care, New York Presbyterian Hospital-Weill Cornell Center, University of Pennsylvania Health System, University of Wisconsin Hospital, The Cleveland Clinic Foundation, Oakwood Hospital, University of Toledo Health, Vanderbilt University Medical Center, Henry Ford Hospital System, University of Washington, Yale New Haven Hospital, University of North Carolina, Ohio University, and University of Colorado.

Preparation and Distribution of SurveysAfter finalizing the point of contacts for the analysis, the team constructed two separate surveys for the 3 COOPs and 16 hospitals. The COOP survey focused on the number of hospitals the COOPs serve, hospital proximity to their facility, square footage of storage, capability of satisfying urgent unexpected laundry requests, number of unique items processed, responsibilities in terms of the delivery schedule, software and technological aids they are utilizing and so on. The survey questions sent to COOPs can be found in Appendix B. The survey for hospitals started by asking whether they have their own internal laundry operations or utilize a COOP. The hospital survey asked about hospitals’ safety stock presence, number of unique items, COOP proximity to their hospital, square footage of storage, methods used to count linen (manual vs. barcode system), software or other technological aids in use, and advantages of using a COOP, etc. The hospital benchmarking survey questions can be seen in Appendix C.

Once the contact information was finalized and surveys were created, the team started reaching out to point of contacts. The team first called the three COOPs and two hospitals in Michigan and asked for email addresses of responsible contacts to send out the survey. For the 14 hospitals from previous benchmarking analysis, the team sent introductory emails introducing the project

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and asking for whether or not they would be willing to contribute. Depending on the responses received and once the contacts agreed, the team sent them the survey. In addition to the surveys, the team conducted an on-site visit to St. Joseph Mercy Health System in Ann Arbor to observe their operations and learn about its existing COOP (Metropolitan Detroit Area Hospital Services (MDAHS)), the same COOP that Michigan Medicine is transitioning to.

With the data collected from benchmarking other organizations, the team analyzed distinct advantages and potential service impacts associated with the switch to a COOP to develop recommendations for this switch and construct an optimal timeline.

Literature Search

In addition to the literature search conducted to advance benchmarking analysis, the team also searched online to gather any additional information available about benefits and challenges of COOPs, different hospital laundry operational models, and how hospital linen and laundry services are provided at the start of the project. These findings from six resources influenced the team’s approach and recommendations for external laundry delivery process and internal linen distribution process. The team also leveraged previous IOE 481 projects to gather additional relevant insight about the Michigan Medicine laundry system.

Findings and Conclusions

With the data collected throughout the project, that team discovered many significant findings that led to conclusions about opportunities for improvement for the laundry operations at Michigan Medicine.


The data collected during the observation of the delivery process is listed in Table 1 below. The driver was also asked if he feels that the current delivery schedule is appropriate, as this schedule is desired for when the transition to COOP is complete. The driver expressed the schedule is necessary because the lack of laundry storage space at the hospitals results in delivering trucks that are not full when they could be. This line of thinking was shared by many others, including the Laundry Director.

Table 1: Observations of the Laundry Delivery Processn = 3 for the number of trips observed; October 17, 2017; Time Study

Trip 1 Trip 2 Trip 3Scheduled Time 6:30 AM 9:30 AM 10:30 AM

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Hospital UH CW UHTime for Delivery (min:sec) 44:32 60:07 45:01Route Time (min:sec) 25:12 30:07 30:37Number of Full Robos Delivered 18 14 18Empty Robo Space 0 4 0

The team used this, along with the given historical data, to assist with forecasting the future demand and simulating the delivery process.

ForecastingWith the forecasted future demand, the team determined the daily demand of laundry for the hospitals. The team determined that safety stock is a necessity, as the COOP delivers six days out of the week. The team is also aware that an abundant amount of laundry will not turn over as frequent, and therefore more likely to become contaminated.

The team used samples of the historical data to determine the daily laundry demands. This analysis focused on the demand of laundry as a whole as well as the most demanded individual items. For the purpose of this report, demand figures and forecasting calculations are only shown for UH, CW and Thermal Blankets. The following forecasting calculations are based on weekday data, as the weekend demand is significantly smaller and would skew the data.

The following table shows the demands of the laundry items that were found using a sample size of 55 days from the historical data provided.

Table 2: Average Weekday Demands for Robosn = 55 for the number of days used

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Avg.UH 48.0 44.8 43.8 45.6 47.6 43.4 45.3 45.8 48.2 45.6 45.8CW 21.4 18.2 21.4 1.4 20.5 20 19.8 20.6 19.8 21.2 20.4Thermals

5.6 5.4 5.6 6.2 6.1 6.0 5.2 5.8 6.0 6.1 5.8

Using these values from Table 2, the team initialized Holt’s Forecasting Method to forecast the next 24 months. For simplicity of the model, both α and β were set at 0.1. Table 3 shows the forecasted future daily demand for future months.

Table 3: Forecasted demands calculated using Holt’s Methodn = N/A; Calculated using Excel Model

Future Time

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t (month) 6 12 18 24 26Month-Year Apr-18 Oct-18 Apr-19 Oct-19 Dec-19UH (robos) 46.7 47.1 47.6 48.0 48.2CW (robos) 21.3 21.7 22.1 22.5 22.6Thermals (robo) 6.1 6.4 6.6 6.8 6.8Thermals (unit) 818 845 873 900 909

As seen in Table 3, the total demand for both hospitals was forecasted to be 70.8 robos of laundry per day, an increase of 7% from the 66.2 robo average this year. Furthermore, the team also considered a long term 25% increase in demand, equating to 82.8 robos per day, which could occur after the construction of the new tower.

Simulation DesignThe simulation software that the team utilized is called ProModel. This program allows the user to emulate entities moving through a system over a large amount of time very quickly, and change certain aspects of the process to create multiple scenarios that can be viewed and compared. The following modules were utilized in this simulation.

Entities (People or products which move through and are processed by the system) Each of the five most demanded laundry items (Bath Blankets, Flat Sheets, Thermal

Blankets, Towels, Contour Sheets). Each laundry item has two entities: one for UH and one for CW

Full_Robo has two entities: one for UH and one for CW Truck

Locations (Physical locations for machines, queues, conveyers or resources)The main locations are the Laundry Facility, UH and CW; however, other locations were required for the setup of the simulation.

Arrival – A queue that accepts arriving laundry items and enters them into the system CW/UH Queue – Queues that combine laundry items into Full Robos CW/UH Dock – Docks that group Full Robos into a Truck to be shipped to the hospitals.

These docks have a capacity based on the Truck Capacity Factor macro

Path Network (A map for resource motion)The routing used in this simulation ensures that alternate deliveries are made to UH and CW throughout the day.

Resources (Employees or objects that are required to perform operations)The only resource in this simulation is the truck driver.

Arrivals (The way entities enter a system)

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Due to the assumption that laundry demand will be met, the arrivals are the demand for each item. The equation used for each entity’s arrival is as follows:

Arrival Quantity = Demand_Factor * Quantity per Robo * DistributionTurning robos into individual laundry items is necessary to utilize the Robo_Capacity_Factor.

Variables (Allow to tcart certain values)Each laundry item entity has its own variable, and these variables are set to the distribution of their respective entity. This value is set because the distributions are in number of robos, and these variables allow us to view those values at the end of the simulation and validate the data.

Macros (Allow for certain ‘what if’ scenarios)Macros are used to change certain aspects of a simulation and enable a user to simulate multiple scenarios, each with their different inputs and outputs. Multiple macros can be altered for a given scenario.

Demand_Factor – This allows the team to view a scenario in which the demand is increased, and how that increase effects the deliveries

Robo_Capacity_Factor – UM is changing from robos to the containers that the COOP is using; however, the container size has not been decided on yet. This macro allows the team to simulate different sizes of containers relative to the current robos and how they affect the deliveries

Truck_Capacity – This macro is needed because if the containers change, the amount of containers that can fit into the truck will likely change as well. The truck has a capacity of 18 of the current robos, and 22 of the current wire carts used by the COOP. As stated above, these wire carts are subject to change.

Processing (What happens to an entity at a location)The laundry arriving at the Arrival queue based on its demand distribution is converted into individual items using the Demand_Factor and quantity per robo before entering Laundry Services. The items then move to the hospital queue corresponding with their name (Bath_Blankets_UH go to UH_Queue while Bath_Blankets_CW go to CW_Queue). In the queue, the items are converted back to Full_Robos using the Robo_Capacity_Factor and the quantity per robo and then are moved to the dock. These docks have capacities based on the Truck_Capacity. From here, the contents of the dock are then grouped into a Truck and transported to their respective hospital with the truck driver. This ensures that even if the last delivery of the day does not meet the dock capacity, it will still be shipped. Once at the hospital, the Truck is ungrouped into the Full_Robos and enters into the hospital. Once the delivery is made, the Truck returns to the other dock to group and deliver those contents. This is repeated until all laundry for the day is delivered to the hospitals.

Assumptions (Every simulation has assumptions that must be clearly stated)

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While the process of receiving laundry from the COOP is still undetermined, a main assumption this simulation makes is that the laundry demand will always be met

The hospitals have the capacity to hold the delivered items. The number of deliveries will directly correspond to the number of robos of laundry demanded that day. For example, if CW only needs 18 robos of laundry for the day, ProModel will make one delivery

Model considers the five most demanded items and groups the other items as a single ‘Other’ entity

Model focuses on daily demand rather than per delivery demand Wire carts used by the COOP have a capacity of 80% of the robo capacity Truck capacity for COOP wire carts is 22 An increase in demand is the same over all items as a percentage An increase in robo capacity is the same over all items as a percentage The deliveries to UH and CW are independent of each other. The model assumes no Milk

Run is used when delivering laundry

The simulation was designed to emulate the delivery process. The main purpose of this simulation is to view how changing variables, such as the demand and the robo size, affect the current delivery process. The demand distributions found using Stat Fit software are for weekday demands, so this simulation is intended to simulate weekdays. For simplicity, the model simulates one day and has 20 replications.

SimulationThe team determined that different sized laundry containers may be needed for both the forecasted demand increase and the long-term increase through simulation. The following scenarios were simulated. Table 4 shows the different scenarios and the macro values associated with them.

1. Baseline – Current State2. Increase demand by the forecasted 7% increase3. Increase demand by 7% and change robos to COOP wire carts4. Increase demand by 25%5. Increase demand by 25% and change robos to COOP wire carts

Table 4: Scenario Macro Valuesn = N/A

Scenario 1 2 3 4 5Demand_Factor 1 1.07 1.07 1.25 1.25

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Robo_Capacity_Factor 1 1 0.8 1 0.8Truck_Capacity 18 18 22 1 22

The results of the simulation using the macro values in Table 4 can be seen below in Table 5. Each scenario was simulated for one day with 20 replications.

Table 5: Results of the Simulated Scenariosn = 20 replications for each scenario; ProModel Simulation

Scenario 1 2 3 4 5UH Deliveries 3.00 3.05 3.15 3.40 3.45CW Deliveries 1.65 1.65 1.75 1.85 1.85UH Containers 44.70 45.70 57.55 53.80 66.90CW Containers 20.20 20.20 25.50 23.90 29.80

From Table 5, it can be seen that an increase of 7% has an almost negligible effect on the current delivery schedule utilizing the current robos, with one out of 20 days having a demand that requires four deliveries. It can also be seen that the other scenarios cause a much more substantial increase in the number of deliveries required the meet the demand.

To supplement these findings, the team created three more scenarios, one for robo-sized containers and two for wire carts, and found the required Robo_Capacity_Factor for the scheduled deliveries as can be seen in Table 6.

Table 6: Last Three Scenarios Simulation Resultsn = 20 replications for each scenario; ProModel Simulation

Scenario 6 7 8Demand_Factor 1.25 1.25 1.07Robo_Capacity_Factor 1.15 1.00 0.85Truck_Capacity 18.00 22.00 22.00UH Deliveries 3.00 2.95 2.95CW Deliveries 1.65 1.60 1.60UH Containers 44.70 53.80 53.80CW Containers 20.20 23.90 23.90

As seen in Table 6, with an increase in 25% in demand, the current robos will need to hold 15% more laundry, or the COOP wire carts will need to hold as much as the robos do in order to maintain the current delivery schedule. In addition, with a COOP wire cart capacity of 85% of the robo, an increase of 7% in demand will allow for the current delivery schedule.


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After observing and interviewing employees, the team learned that while there was a designed process in place, many employees did what they believed to be the most efficient when delivering linen rather than following this designed process.

Current State Process FlowFigure 1 contains the process map of what linen distribution employees are currently supposed to do.

Figure 1: Designed Current State Process Flow

By designing the process flow, the team recognized that there is significant additional walking in having the stock keeper walk to manually measure inventory, return to collect the inventory, and then walk back to deliver the inventory. However, many employees have recognized this as well, and as a result, they oftentimes conduct counts during other delivery trips to reduce the total number of trips taken. While this does decrease walking for employees, it is a non-standardized process and takes significant knowledge of the process to be successful. During observation, new hires had to go through significant shadowing to learn the nuances associated to be efficient rather than having a standard work plan.

Cause-and-Effect Analysis

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The team also constructed a cause-and-effect diagram (Figure 2) to further assess the core causes of the current inefficiencies.

Figure 2: Fishbone Diagram for Internal Laundry Distribution

As seen in Figure 2, there are areas for improvement with the current process in each area analyzed (materials, man, methods, and machines). Creating the fishbone diagram helped the team diagnose that a major concern with the current process is the lack of a standardized process. With the data and associated findings, the team was confident that designing a new future state process could significantly reduce the walking distance as well as the overall process time of linen distribution. Specific future state designs and the associated time savings will be elaborated upon in the design recommendations section, but first, the current state time breakdown of the process was assessed.

Current State Process Time BreakdownWith an understanding of the process and the data collected regarding how long it takes employees to conduct various aspects of the distribution process, the team calculated the average time of each major step in the process as seen in Figure 3.

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Figure 3: Current State Process Time Breakdown (Actual)n = 15 for each step of process; November 28, 2017; Time Study

As can been seen in Figure 3 based on the snapshot of data collected, the current process for UH takes about 73 minutes from packing the cart to returning back to the holding room and restocking unused linen. Further, the total walking in this process accounts for about 21% of the process time while counting inventory accounts for an additional 13% of the total process time. The IOE 481 team determined that these two components of the current process are aspects that can experience significant reductions with improvements to the current state design.

Improvement OpportunitiesFor the walking component of the process, while the time it takes to walk to each clean room cannot be changed (unless the time of linen distribution changes), the amount of total trips can be decreased assuming that employees are currently making some additional trips to count inventory. Further, the team hypothesized that by having a single (or select few) people count inventory at the beginning of shifts, this would eliminate the need to do the count during each linen distribution round-trip. Not only would this eliminate the need to count inventory during each trip, but also it would likely be more efficient to have a few employees measure all areas. Furthermore, this would eliminate the amount of usage for each employee with the handheld scanner, which can be seen as an advantage due to the current delayed acceptance with the new handheld technology.

Benchmarking Analysis

The team determined advantages and potential service impacts of COOPs through benchmarking to supplement internal and external data collections. The team analyzed findings from literature

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searches, surveys sent to other COOPs and hospitals, and its on-site visit to a hospital that utilizes a COOP for laundry.

Literature SearchAfter conducting a literature search about the general concepts of hospital laundry services to guide the team in an initially unknown area, the team focused on learning more about COOPs rather than the general laundry process to tailor benchmarking survey questions. The main areas that guided team’s questions in benchmarking were potential benefits of having a COOP, challenges facing operating a COOP, and whether it has a sustainable future or not [6]. An important event that supports the advantages of COOPs was the Evergreen Cooperative Laundry which is a $6 million facility in Cleveland, Ohio. This COOP not only focused on how to serve its clients, but also was known for having a “green” environmental approach [7]. Although the case of Evergreen Cooperative Laundry is extreme, it still provided the team a different perspective while analyzing COOPs and hospitals.

COOP FindingsThe team reached out to three main COOPs (Michigan Premier Laundry, West Michigan Shared Laundry, and North Grand River Cooperative). Although the General Manger in West MI Shared Laundry could not fill out the survey due to conflict of interest, the Administrative Manager in Michigan Premier Laundry (MPL) and General Manager in North Grand River Cooperative (NGRC) provided responses.

Michigan Premier Laundry (MPL)The team gathered information about Michigan Premier Laundry (MPL) and overall operations from its Administrative Manager. MPL is a COOP located in Saginaw, MI, which is currently serving 24 hospitals and processes 23,000,000 pounds of laundry annually. All the hospitals that MPL serves are within a 100-mile radius from their facility except for one customer which is over the 100-mile radius. MPL is responsible for purchasing, processing, delivering, and picking up soiled laundry. The COOP runs only one shift with two start times, and delivers laundry once a day to main hospitals (Monday – Friday). MPL does not store processed linen and keeps its dock area empty to hold carts. Since its drivers are constantly traveling and delivering orders, they do not have the need to dedicate a space for storage in their facility.

MPL has a standard linen list used by all hospitals they service; however, all unique linen items owned by the hospitals can be processed in their facility at a higher cost rate. Keeping the number of items processed limited helps stay within their pound limitations due to the facility’s capacity. The COOP is responsible for controlling the inventory of hospitals; therefore, its customers do not own safety stocks and do not have the need to utilize barcode system to track their laundry item limits. The scheduling interruptions due to extreme cases are negligible. MPL

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utilizes LinenMaster for their operations and sees cost savings in linen purchases, utility and labor as the main benefits for hospitals using a COOP.

North Grand River Cooperative, Inc. (NGRC)The team acquired insight on the operations of NGRC after contacting the General Manager. NGRC, which is a COOP with two owners, is located in East Lansing. NGRC has two facilities; one facility where all processing of linen takes place and another where all distribution of linen to customers takes place. They deliver laundry with exchange carts to some customers and as bulk to others. NGRC processes 9 million pounds annually. They currently serve nine hospitals and have 151 delivery points in total. The proximity to delivery points is a large consideration during customer acquisition. All current deliveries are within a 45-mile radius of East Lansing. Delivery schedules vary by delivery point and customers range from multiple deliveries daily to Monday-Wednesday-Friday deliveries to once-a-week deliveries to call in orders. The COOP has trucks that make one stop deliveries, with up to 15 stops daily. Although NGRC has set start times for each delivery route, each day is routed as if every possible delivery that could be made will be made and is adjusted based on actual needs. All of the large hospitals that NGRC works with utilize exchange carts which are built and shipped to them.

All NGRC facilities are part of a pooled linen system. As such, they regularly have a 72-96 hour supply of circulating inventory on-hand whereas the rest of the laundry is kept at the laundry facility. A pooled COOP system operates in an all in first out endeavor, meaning NGRC does not necessarily value where the linen came from. Rather, once it is processed, whoever is the next order in the schedule to be filled is where the linen goes. The laundry processing operation has a standardized method and is not customized for different hospitals. NGRC has very few unique items due to the nature of pooled system, which aims to simplify item quantities to allow for better productivity flow, better acquisition costs, better plant productivity and the resulting lower cost of operations. NGRC currently has fewer than 10 items in their system that would be considered non-pooled or unique items. The COOP aims to ensure that they satisfy the 98% of utilization and not buy a product for every perceived customer need. The number of items processed is decided by NGRC’s Operations Committee that routinely meets to discuss new prospective items and how current items are utilized. This committee conducts due diligence and forwards recommendations to the Board of Directors for final decisions; having less items in COOP operations is essential. Fewer items allow for better acquisition costs, easier to place orders (time), better plant flow, better productivity, and lower costs.

The laundry operations, like any other plant process involving delivery to customers, can be affected due to extreme uncontrollable cases. Due to weather, NGRC sometimes has delays in 30-minute window for all deliveries. To alleviate these problems, the COOP schedules their largest delivery facilities for their first runs daily. In cases of emergency customer laundry needs, NGRC has enough linen on-site to get through any normal emergency. Also, in more extreme emergency cases, they have an emergency action plan. To ensure smooth operations, NGRC

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utilizes LinenMaster for all linen programs and invoicing. NGRC views lower costs as the biggest benefit for hospitals to use a COOP instead of managing their own laundry since hospitals end up getting discounts for economy-of-scale purchasing, shared replacement cost for equipment and facility needs.

Hospital FindingsThe team reached out to 16 hospitals in total. The team contacted the two hospitals in Michigan (St. Joseph Mercy Health System, Ann Arbor and St. John Providence) via phone and sent introductory emails to the rest of the 14 hospitals. From the two hospitals in Michigan, the team acquired information from St. Joseph Mercy Health System. From the 14 hospitals, University of Wisconsin Health and University of Washington volunteered to provide assistance. The information gathered from these three hospitals is given below:

University of Wisconsin HealthThe Director of Materials Management provided data on University of Wisconsin Health (UW Health). The hospital has processed its laundry by using a COOP (Madison United Healthcare Linens- MUHL) since the 1960s, which is only a few miles away from the hospital. UW Health finds MUHL highly responsive and available, and has had no delivery issues including delivery schedule interruptions or delays. Therefore, they do not feel the need to own a safety stock at the hospital. The hospital, which has 5,000 square footage of in-hospital storage, processes 14 million pounds of linen annually.

UW Health has more than 130 SKUs (stock keeping unit) as item offerings including specialty items determined by a linen service committee comprised of COOP members. The COOP, which does not own a depot and has its own trucks, delivers more than five times each business day directly to the hospital starting at 6:00 am and finishing around 3:30 pm. While MUHL uses LinenMaster software, the hospital itself utilizes PeopleSoft to handle laundry related operations.

University of Washington Medical CenterThe Program Operations Manager provided information about University of Washington’s medical center laundry operations. University of Washington handles their laundry operations by using a COOP (UW Consolidated Laundry) for the UW Medical Center’s (UWMC’s) Hospital and non-profit government and regional district hospitals since 1959. UW Consolidated Laundry was built to support the UW Medicine hospitals and non-profit government agencies in the area. The hospitals that the COOP serves are within a 4 mile to 65 miles range from their facility. UWMC processed 14.3 million pounds of laundry last year. The member hospitals have more than 200 unique linen items including specialty items like mammography wraps and multiple children / baby items.

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The COOP does not own a depot. However, each member hospital keeps its own safety stock which is very minimal. Each hospital is responsible for their own supply. The UWMC has built cabinets in each unit that holds a supply of 2-3 days, if necessary. Another hospital that also uses the same COOP keeps a couple of extra carts of the main items for emergency supply. The main issue that every member hospital is facing is space; therefore, the hospitals rely on the COOP to keep a large emergency backup supply as part of their regional contingency planning. The COOP has adapted the laundry to be able to transition completely to a large generator in the event of power failure. UWMC has not experienced any delivery schedule interruptions due to weather. The COOP reroutes their trucks to accommodate the customers for any delays and delivers urgent requests same day. In case of a truck unavailability, the COOP has also completed urgent requests using COOP employee personal cars to help meet the demand of member hospitals until the next morning.

UW Consolidated Laundry has five leased trucks that start delivering around 4:30 am. One truck completes deliveries by 5:00 pm while the other four trucks complete deliveries by 1:00 pm. The COOP delivers to all of the member hospitals and their affiliated clinics. For the larger member hospitals, the COOP completes daily deliveries at four times. In some cases, sometimes students are contracted to make the internal deliveries. Currently, neither the COOP nor the member hospitals are utilizing a barcode system. The COOP utilizes LinenMaster, LinenHelper, and Spindle for production and Bigfoot for maintenance as technological aids.

Price reduction in buying power of inventory is seen as the biggest advantage of using a COOP. Moreover, belonging to a COOP, the hospitals benefit from being in the same situation as the other hospitals utilizing the same COOP and adds an additional point of contact to reach out to when needed for any concern.

St. Joseph Mercy Health System, Ann ArborThe Interim Manager of Hospital Transportation and Linen Services provided the following data. St. Joseph Mercy Health System uses the COOP (MDAHS) that Michigan Medicine is switching to. The COOP is within 40-50 miles from the hospital. The hospital experienced minimal delivery schedule interruptions and unexpected delays due to breakdowns, accidents and weather. So far, the COOP had always accommodated the hospital with an extra delivery to account for these types of unexpected cases. Also, for urgent laundry delivery requests in an emergency, the COOP again usually meets the demand on the same day, which is as responsive as having internal laundry operations, but internal operations would need more staff. Therefore, the hospital does not have a space dedicated at the hospital as a safety stock or a separate depot operated off-site. The COOP is responsible for the deliveries to the hospital directly. The COOP owns their trucks, and the hospital also owns two trucks on-site that are used to move linen from the main delivery dock to several other delivery points in the main hospital and some outlying areas. Those trucks owned by the hospital are only used for overnight storage (last truck loaded) and are emptied every morning. The delivery is completed via two trucks daily, each

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truck having 22 wired carts. The hospital is currently using the COOP’s website as a technological aid and is not utilizing an electronic barcode system to count linen items.

The COOP processes approximately 4 million pounds annually for the hospital. Although St. Joseph Mercy, Ann Arbor is not limited to a specific number of items, they still try to keep it to a minimum. The hospital also has thermals, which are high in demand as patient satisfiers. They use approximately 550 thermals each day. The hospital owns specialty items mostly for women and babies, which are billed separately. Once the requests are sent on a need basis, the COOP cleans the items, separates them, and bills accordingly. In case of torn unusable linen, the COOP has been very accommodating; however, if those unusable items are categorized as dirty (dead linen), the hospital still gets charged. Although operating laundry internally helps to maintain tight chain of custody and makes requests be available on the same day, using a COOP has been more beneficial in terms of maintaining a steady supply of clean linen every day without the human resources and managerial issues that come along with trying to run an operation of this size and magnitude.

Observations from the on-site visit to St. Joseph Mercy Health System, Ann ArborThe team visited the St. Joseph Mercy Health System Hospital in Ann Arbor on November 27th, 2017. The Interim Manager of Hospital Transportation and Linen Services conducted the on-site tour. The hospital has two towers, with each floor having six carts for clean linen storage. Two trucks from the COOP delivers clean laundry twice per day to the hospital. Each truck can fit 22 wired carts supplied from the COOP which delivers six days a week. An additional amount is delivered during the week to account for Sunday linen needs and urgent extra needs. The wired carts that are currently being used can be seen in Figure 4 below (all pictures were taken during the team’s on-site visit). Therefore, the hospital does not own a safety stock. Once the clean laundry is delivered in wired carts, packers at the main delivery area fill the buckets and carts that will be distributed among the floors of the hospital. The packers are responsible for filling out the buckets and carts with the same number of linen items identically and they do not leave the main delivery area.

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Figure 4: Wired carts in Use

St. Joseph Mercy Health System Hospital in Ann Arbor has two main components in its internal linen distribution operations. The hospital currently maintains seven robots daily and has two soiled linen chutes in total. The hospital acquired robots originally from a vendor called FFC, but right now, the robots are supported by a company called JBT. Automated guided system (AGV) is used to navigate the robots, and the robots in use can be seen in Figure 5 below. The robots in use, software to run them, and parts needed to maintain them are a part of an old system and are maintained by in-house engineer’s and supported by JBT. The hospital has multiple charging stations that serve one robot at a time and completes charging within 10 minutes.

Figure 5: Empty Robot En-Route

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Since the separate buildings are connected underground, the hospital has long halls connecting each tower of the hospital which makes it easier for robots to travel from one location to the other. The routes that robots take have specific labels and each door that the robots pass have shiny triangles helping robots to navigate their path. The AGV system is tracked via monitors which are both used to program where each robot should be travelling (Pioneer screens) and show where each robot is in real time, as seen in Figure 6.

Figure 6: Robot Technological Aids

The robots get the buckets and carts filled by the packers from the main delivery area and drop them at the closest location to the elevators. Then, stock keepers manually drop the buckets and carts full of clean items to the clean rooms at each floor which are located right next to the elevator entrance in each floor. Therefore, stock keepers do not need to walk in floors since elevators doors directly open to the clean laundry storage rooms in each floor which minimizes the staff patient interaction. One door in the elevator opens up to the clean linen storage area, the other opens up to the soiled linen storage room. In the soiled items storage room, an air chute exists which directly sends the soiled linen items down to the soiled linen storage area. These soiled linen chutes are centrally located with one in each tower. Thanks to this design, every person on every floor of the patient care towers is never more than 150-200 feet away from the soiled linen chute.

St. Joseph Mercy Health System Hospital in Ann Arbor used to utilize the Yellow Dog inventory system and quantified their own used laundry items. However, currently the hospital uses the COOP’s numbers for billing. They are currently working on coming up with a verification process where they can bill by department and cross check the COOP’s given linen item quantities.

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Literature SearchIn addition to the literature search conducted to advance benchmarking analysis, the team also searched online to gather any additional information available about hospital laundry operations in general. The team also leveraged previous IOE 481 projects to gather any additional relevant insight about the Michigan Medicine laundry system. The team utilized a report’s findings on optimizing the refill and distribution process in the linen use at the VA Boston Healthcare System-West Roxbury Campus. This source helped the team to see how engineering methodologies can be utilized to create a future state design helping the team in the brainstorming phase for recommendations section [8].

Inventory shortages, which were mentioned in the past IOE 481 project report, have been seen as a reoccurring problem [9]. Therefore, the team realized that the lack of inventory available at the distribution center in the hospital originated from overstocking the clean rooms throughout the hospital. As a result, the team decided to focus on redesigning the laundry distribution process and creating a standardized work plan to eliminate inventory shortages caused by overstocking.

Design Methods, Constraints, and Standards

The design of the internal distribution process is documented throughout this paper. The Pugh Selection Matrices can be found in Appendix D.

Design Constraints1. Time- The stock-keepers work in 8 hour shifts within the interval of 4 a.m. and 5 p.m., so

the distribution process is constrained by those time boundaries. Internal hospital traffic fluctuates throughout the day, and one can easily conclude that there is less traffic at 5:00 a.m. compared to 4:00 p.m. The stock-keepers manually distribute the laundry by moving large baskets (robos) from the laundry storage room to the clean rooms of the different hospital floors. With more traffic within the hospitals, the stock-keepers must be that much more cautious, increasing their walking and overall process time.

2. Hospital Storage Capacity- The hospital storage spaces for laundry are currently very limited. This ties directly to the laundry delivery schedule and the timing in which the distribution process can occur. Due to the limited capacity, the laundry distribution process cannot take place at any time throughout the day, but is triggered by the delivery that replenishes inventory.

3. Size of Robos- The robo size has an effect similar to the hospital storage capacity. The hospitals can store up to a certain amount of robos, and the robos can store up to a certain amount of laundry (differs by item). Due to the capacity of both, each laundry delivery must act as a trigger for the distribution process to ensure that the following delivery will not exceed the hospital capacities while also meeting the hospital needs.

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Design StandardsOSHA (Occupational Safety & Health Administration) standards

Part 1908: Consultation Agreements (1908.6 Conduct of a visit)To comply with this standard, the team became familiar with as many factors concerning the hospital’s operation as possible before the onsite visit. The team completed online MLearning modules before the onsite visit to acquire all necessary information about the codes and standards of the hospital.

Part 1910: Occupational Safety and Health Standards (1910.30 Training Requirements) To comply with the training requirements, once the standardized work procedure is implemented, the team will recommend all stock keepers to go through a training session to familiarize themselves with the new workflow. This way, the employees will not only be familiar with the new process but also will understand the necessary actions to take in emergency situations.

UM policy: After conducting interviews with employees during on-site visits, the team did not reveal employee names in documentations, and during client and coordinator meetings.

UM Hospital policy: During the onsite hospital visit, the team observed that stock-keepers were not allowed to do deliveries for certain locations in the hospital before certain times of the day. For example, for the CVC area in UH South, all linen deliveries should be completed after 3:00 PM and the stock-keepers are required to wear scrubs while doing the linen delivery to these locations. The team complied with this policy, waited until the specific time for delivery and wore scrubs during the visit. The process flow chart was designed to meet the standards of practice of the University of Michigan Process Improvement Department and IOE 481 course standards.

Summary of Conclusions

Table 7: Summary of ConclusionsExternal Internal Benchmarking

Forecasted a 7% demand increase instead of 25%

Container size/type will affect feasibility of current schedule along with demand increase

Current distribution process is not standardized

Opportunities for improvement in terms of process efficiency

New technology has ability to improve process

Minimal in-hospital storage space

Importance of less items Technological aids used “Dead linen” issue Best advantage of

COOPs is cost

Recommendations

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With the findings and conclusions, the team was able to make recommendations across all three areas examined as the laundry operation transitions to a COOP, and also constructed a timeline to map out these changes.


The team has determined recommendations for the size and capacity of the depot that will be needed to store laundry, and the laundry container size to maintain the current delivery schedule with the increased demand.

Depot Size and CapacityThe construction of a depot will need to take place within the next 24 months, as the Laundry Services will no longer be available to hold laundry. The depot will need to hold both clean laundry delivered by the COOP and dirty laundry to be picked up by the COOP. From the forecasted findings, the team determined the daily demand of clean laundry by December of 2019 to be 70.8 robos, a 7% increase from the daily average of 66.2 robos this year. However, due to the lack of storage space at the hospital and the need for safety stock in order to protect service level, the team recommends that one day of clean laundry be stored at the depot as safety stock. Due to the COOP delivery schedule over the weekend, the team also recommends that the depot has a minimum capacity of three days’ worth of clean laundry. The team has two recommendations for the depot; one for the forecasted demand increase, and the other for the long-term demand increase. For the forecasted demand increase, a safety stock of 71 robos and 633 ft2 is needed, while the minimum capacity must be 1988 ft2. For the long term demand increase, a safety stock of 83 robos and 774 ft2 is needed, while the minimum capacity must be 2324 ft2.

Please note that the above square footage only considers the space required to store clean laundry. More space will be needed to store the dirty laundry and empty containers that the COOP will retrieve from the depot. The future demand will also have implications with the COOP, as the more laundry demanded may result in an increased number of deliveries from the COOP to ensure that the demand is met.

Laundry Delivery ScheduleIn terms of the delivery schedule, Laundry Services would like to keep the current schedule in place. This will likely be achievable with the forecasted 7% increase in demand without any changes. However, it is almost certain that the robos will change to a different container, and the team has findings based on the current wire carts used by the COOP and assumptions of those carts listed in the Simulation Design section. The truck capacity is 18 robos or 22 wire carts. In

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order to maintain the current delivery schedule with the 7% increase in demand, the wire carts will need a capacity of 85% of the robos. In expecting the larger 25% increase in demand, robos with 15% more capacity or wire carts with the same capacity as the robos are needed in order to maintain the current delivery schedule. Note that in keeping the same truck capacity of a certain container while increasing the capacity of said container requires the container to be taller. A taller container will result in ergonomic issues that the smaller containers did not have.


Given that the laundry process will not switch to a COOP for another two years, the team has developed a series of recommendations for internal linen distribution operations.

General Future State ProcessThe team has designed a general future state process. With this future state process, the team recommends two variations of the process flow; a first variation that can be implemented now, and a second variation that can be implemented when the laundry system transitions to a COOP where there is more flexibility in laundry deliveries. The general future state process is displayed below in Figure 7.

Figure 7: General Future State Process Flow

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As seen in Figure 7, the new process eliminates the need for each worker to go to each room and count inventory beforehand. Instead, just a few employees can go measure each inventory level and electronically send it to other employees so that everyone else can solely focus on picking the inventory and delivering it to the respective clean rooms. This future state process can improve efficiency greatly but eliminating the additional non value-added walking to count inventory that employees are currently supposed to do. Further, this process will foster a team-based system, which will lead to increased accountability.

Excel Model to Determine Time SavingsWith this new process flow, the team has also created a model (attached in separate Excel document) that will allow for the estimation of time savings. This model allows for the user to input data about future state time differences based on either measurements or estimates. The model is referenced below, and it will be explained in order to allow future users to understand how to use it. This model justifies how future state time savings are calculated by comparing:

Current State (Designed): How original process was intended to be (count inventory before making each trip).

Current State (Actual): How process was observed (counts made during other trips or estimated).

Future State: Eliminates counting during process. Rather, few employees count all inventory beforehand.

InputsThe model has five sets of inputs, as can be seen in Figure 8. Since the process is highly variable and time savings with the future state are based on assumptions, the model allows for flexibility for estimating time savings depending on what the user feels is the most appropriate measure.

Figure 8: Inputs for Distribution Model

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Times Referenced from Data: In the first section, these values can either be referenced from the data sheet on the previous tab of the Excel document, or they can be inputted manually based on what the user feels is the most appropriate time for each step of the process.

Future State Time Multipliers: These multipliers allow the user to input what they expect to be the increase or decrease in the various process steps of the linen distribution process. For example, if the user believes that the new process will eliminate the need to count the inventory during the distribution process, this multiplier can be entered as “0”, which will then be factored into the new output.

General: The general setting allows the user to determine the number of employees for a given day. Further, it allows the user to estimate how many trips a worker makes per day. This number is very variable based on shift, so an average is used to generalize the model. Further, it is important to note that one trip does not equate to stocking one clean room. Multiple cleans rooms can be stocked by one trip, or a clean room may need multiple trips.

Number of Trips Multiplier: This multiplier helps account for the variation in the number of trips that occur. Since each worker counts inventory in a different manner, the exact number of trips for the current process is unknown in comparison to the current standard process. For the shown model, given the current state (actual) has a multiplier of 1, if a multiplier of 1.5 is assigned to the current state (designed) and a multiplier of 0.75 is applied to the future state, this would indicate that the current state (actual) has 33% less trips than the designed process, and the proposed future state would have 50% less trips than the designed process (which correlates with the new design that eliminates half of the walking).

Future State Preliminary Counting: These inputs account for the additional time that must be factored in for the initial counting of inventory by taking into account the number of employees scanning, how many rooms / areas each worker will cover, and the average scan time per trip.

Using assumptions and hypotheses from observation in conjunction with the model, the team created preliminary estimates in savings for each of the two new future state processes. The exact steps of the processes in addition to estimated time savings are specified below. An important aspect to acknowledge is that these projected time savings are based on a snapshot of data the team collected for UH Main; the process is highly variable, and no two days are alike. The time savings calculated are intended to indicate potential savings given the scenario entered.

Future State Variation #1 (Same Time Frame)The first future state variation has been designed to be implemented within the next 18-24 months (before the transition to COOP occurs). With this process, the project team recommends that three employees arrive early to measure the inventory levels with the handheld scanner for

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all of the areas of the hospitals. By doing so, all the necessary inventory will be known for each employee, so the majority of the employees can focus on either picking or delivering the linen, depending on what may be needed at the time. Not only does this eliminate the counting aspect for most employees, but it will also promote a team-oriented environment which will help keep team members accountable.

Process AdvantagesFirst, each employee will only have to take one trip to each clean room instead of walking to measure inventory, walking back to the holding room to collect the inventory, and then having to walk all the way back to stock the inventory. Second, the work process will now be standardized for all employees. Not only will this create a more efficient and effective process, but it will also simplify training for new employees. Finally, given the initial hesitation with the acceptance of scanners, this method will vastly reduce the amount of usage with the scanners since the majority of employees will not have to use them to count inventory at the clean rooms.

Model InputsGiven this redesign, the model’s inputs were updated with the following settings to calculate time savings:

Future state multipliers for time to count inventory (included later) and time to restock linen updated to 0

Number of trips multiplier updated to 0.75 (50% less than designed process and 25% less than actual process)

3 employees scanning with an average scanning time per round trip of 9 minutes

Time ImprovementsWith these changes and the snapshot of data that the team collected, the average time of a round trip decreased from 80.73 minutes in the designed current state and 73.26 minutes in the actual current state to 59.54 minutes in the future state. Further, even after factoring in the time taken to initially scan the inventory needs, the total time of linen distribution per day was 15.84 hours fewer than the current designed state and 6.13 hours fewer than the current actual state, which demonstrates significant time (and distance) savings. Figure 9 below demonstrates the savings across various steps in the process for this future state implementation.

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Tim

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Figure 9: Comparison of Process Steps for 3 Current vs. Future State (Same Time)n = N/A; Calculated Data using Excel Model

As can been seen in Figure 9, the time savings in the new system are in the walking times, inventory counting times, and restocking times. While inventory is still counted, it is not factored into individual employees’ process times anymore since only a few employees will actually do this. Furthermore, it was assumed that if employees are solely responsible for counting inventory, they will be more efficient at this than having many do it while also dealing with robos. As can be seen, there are many potential improvements from the implementation of this future state model.

Future State Variation #2 (Varied Time Frame) The second future state variation has been designed to be implemented once Michigan Medicine fully transitions to the COOP. With this variation, the general future state process will still be implemented; however, the majority of this process will be shifted to the nighttime. By doing so, there will be much less congestion in the hospital in terms of patients, visitors, and other employees. Furthermore, there will also be much less competition for elevator usage, which appears to account for a significant portion of the transportation time. Once the transition to the COOP is established and Michigan Medicine has a storage depot, it will be easier for linen deliveries to the hospital to occur since the actual processing of laundry will not have to be shifted as well.

DisadvantagesThe major downside associated with shifting this process to the nighttime is that it will affect all of the hospital laundry employees’ hours. Instead of working normal hours, employees will have to work during the night, which could be an issue with current employees’ availabilities. Furthermore, some linen distribution must occur during the day, so it is not possible to switch every shift over to the nighttime.

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Model InputsGiven this redesign, the model’s inputs were updated with the following settings:

Future state multipliers for time to count inventory (included later) and time to restock linen updated to 0

Future state multipliers for walking to first clean and from last clean room updated to 0.75 (less traffic and congestion)

Future state multiplier for time to unload linen at clean room updated to 0.9 (less traffic and congestion)

Number of trips multiplier updated to 0.75 (50% less than designed process and 25% less than actual process)

3 employees scanning with an average scanning time per round trip of 9 minutes (less traffic and congestion)

Time ImprovementsWith these changes to the assumptions as well as the snapshot of data collected, the average time of a round trip decreased from 80.73 minutes in the designed current state and 73.26 minutes in the actual current state to 54.24 minutes in the future state. Further, even after factoring in the time taken to initially scan the inventory needs, the total time reduction per day across all employees was 22.3 hours fewer than the current designed state and 12.6 hours fewer than the current actual state, which demonstrates a significant time (and distance) saving over the course of a day compared to the current state as well as the alternate proposed future state. Figure 10 below demonstrates the savings across various processes for this future state implementation.

Time t

o Pack Cart

Time t

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lean Roo

m

Time t

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Linen at

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tory

Time t

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ck unu

sed lin

en

0.00

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30.00 27.4

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Tim

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Figure 10: Comparison of Process Steps for 3 Different States (Future State Varied Time)n = N/A; Calculated Data using Excel Model

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As can be seen in Figure 10, this future process has time savings across every step in the process besides the time it takes to pack the cart due to the reduction in congestion in the hospital. While this design would save the most time, the effect of the time shift must be taken into account. A comparison of the two future state processes can be found in Appendix D.

Next StepsIn order to implement the future state process recommended above, there will first need to be verification that the technological requirements outlined are feasible. While the new technology should possess the capabilities outlined, it will be necessary to check, confirm, and implement it. Further, the employees must go through a standardized training so that everyone abides to the standard process to ensure that the new process is effective.

Benchmarking Analysis

The team recommends two possible improvement measures that supplement external and internal process recommendations. The first measure involves minor changes, while the second measure involves major changes. By considering the laundry operations both from a COOP and a hospital perspective, the team came up with several recommendations to construct two sets of improvement measures after COOP is implemented for increasing efficiency with this new process.

Minor changes in improving the overall laundry operations include decreasing dead linen quantities, and assigning minimal safety-stock storage in the hospital. The second set of improvement measures which involves major changes include implementing other ways to count linen items like RFIDs and weighted smart shelves which brings up re-assigning employee responsibilities, utilizing automation, and additional software aids for increasing efficiency.

Improvement Measures with Minor ChangesLinen costs range from 20 to 40 percent of the total cost of a hospital laundry and linen services. As estimated, 80 percent of the linen replaced is attributed to linen misuse and wear out [10]. As observed from the team’s on-site visit to St. Joseph Mercy Health Services, one of the main contributing factors for increase in cost was the number of “dead linen” items. Hospital staff usually ends up categorizing unusable torn linen items as “soil linen” which causes the COOP in use to over-charge the hospitals. Therefore, once the transition to the COOP is completed, Michigan Medicine should not only prepare an institutional guideline making the distinction, but also implement a series of training sessions to inform both stock keepers and nurses of the differences between unusable versus soiled linen items. In common storage room, clean rooms, and soil item rooms on each floor, an additional disposal bag could be placed just for separating torn unusable items so that those items won’t be categorized as soiled and will not be charged.

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To ease the transition period of this structural change, the Laundry Services could place a sheet of instructions and steps with images to distinguish one type from the other next to the newly added disposal bins.

In addition, the team observed that not having a separate safety stock storage in the hospital was a common strategy for hospitals using COOPs. Therefore, the team decided that dedicating a space in the hospital for safety stock granted Michigan Medicine has already limited linen storage space at the hospital is infeasible. It is more efficient to develop COOP delivery schedule by requiring deliveries to have excess linen items than the daily need so that the hospital can account for urgent daily laundry needs without the need of a separate storage space and keep extras in clean rooms. In case of situations where that excess laundry is not enough, Michigan Medicine could start utilizing the clean items from their depot.

Improvement Measures with Major Changes After the COOP is implemented, Michigan Medicine can choose to have major changes in its operations. As an alternative to manually identifying and counting linen items or utilizing hand-held barcode scanners, the hospital can use the RFID’s to make the linen item count for each department at the hospital easier [11]. RFID refers to a chip consisting of a silicone micro-processer, and a protection cover made of glass or polymer. The whole RFID-system consists of a chip, reader, and a computer system which all can be combined with a common software. The distance of activation is up to 1-meter and does not require to be in-line of sight as opposed to handheld scanners [11]. Although RFID is usually used for tracking items, it also has advantages in providing accurate item count records [12]. RFID tags sewn into each textile help to take inventory faster, and more efficiently. Once RFID readers are placed in each storage room, continuous inventory will be taken and linen leaving the facility to the COOP will be tracked and made sure they come back [12]. In addition to its advantages in counting linen items, RFIDs can also be used supplementary to the minor changes mentioned earlier about solving the “dead linen” problem. RFIDs can keep wash cycle analytics, which helps to predict the end-life date for each linen item [12]. Therefore, the unusable old linen items can be detected ahead of time preventing them to be categorized as soiled linen.

In addition to RFIDs, weighted smart shelves can also be utilized as an aid to ease the linen counting process. By replacing current carts with weighted smart shelves (or just adding weight sensors to current shelves in use), the stock keepers won’t need to count, resulting in time savings. Also, with a more standardized process mentioned in the internal linen laundry operations section and implementation of weighted smart shelves, the time to count items will significantly drop. Currently, smart shelf technology is being used in retailers to keep track of in-store inventory [13]. However, it could be applied to keep track of the inventory levels of the clean linen items in hospitals. With smart shelf technology, employees can keep track of the inventory automatically. Smart shelves are wireless inventory control systems that have weight sensors which can either be built-in or installed under normal shelves currently in use. These

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weight sensors consistently send notifications about the existing items on shelves and detect when items are running low or are misplaced. The overall system is supported by RFIDs placed inside laundry items on shelves [13]. This way, stock keepers will not need to check each clean room to count the items running low, they will automatically be notified. The implementation of both the RFIDs and weighted smart shelves will bring in re-assigning responsibilities of the current stock keepers and decrease the current workload.

From the information gathered from COOPs and hospitals, the team also suggests Laundry Services to look more into implementing additional software options like LinenMaster, LinenHelper, Spindle, and Bigfoot to ease the overall laundry distribution process. In addition, in case of addition of new hospitals in the future, if more space would be dedicated for Laundry Services and if the building will have connections underground, then the laundry distribution process can be highly automated. As the team observed, St. Joseph Mercy Health Services had more than enough space for robots to travel underground with minimal interaction with people. Only one employee was in the dock area packing carts which then travelled to the rooms close to the elevators going directly to the clean rooms via robots. Then, the employees in those rooms across from elevators, manually took the carts to respective floors. It is not feasible to implement that system currently due to lack of space in the hospitals; however, in the future, in the case of adding new hospitals on campus and creating the infrastructure for automation, the return for investing in robots could be seen in the long run. Overall, the recommendations with minor and major changes from benchmarking analysis complements the external laundry delivery process and internal linen distribution operations.

Timeline

In order to successfully implement the design recommendations across all three areas analyzed, the team has constructed a timeline to guide the recommended changes.

Immediate Determine if handheld scanners have the technological ability to scan for all rooms and

send down information to either a central location, or back to other handheld scanners

Within next 18-24 Months Implement Future State Variation #1 (Same Time Frame) Determine the size of the container that will replace the robo. The will have an effect on

the depot size Construct depot that will hold the determined safety stock of clean laundry and the dirty

laundry that will be picked up by the COOP Develop COOP delivery schedule considering minimal need for in-hospital safety stock

storage by requiring COOP deliveries to have excess linen items than the daily need

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After COOP is Implemented Implement Future State Variation #2 (Different Time Frame) Implement a training program for stock keepers and nurses explaining how to distinguish

between unusable versus soil linen items to decrease the number of “dead linen” items Integrate RFIDs and weighted smart shelves to ease counting process resulting in re-

defining employee tasks Utilize software (LinenMaster, Spindle, Bigfoot) and automation to aid with production

and maintenance

Expected Impact

By conducting the various forms of analysis discussed in the project approach, the team has been able to provide recommendations for a successful transition to the COOP laundry system while benchmarking impacts with the transition to COOP. The recommendations will result in:

A deep understanding of the changes that will occur between the current and future state An optimal system for storage and distribution with forecasted linen usage factored in Service impacts associated with the transition to COOP A feasible timeline for the transition to COOP

References[1] "ExpenseAnalysis17.xlsx", Rolando Croocks, Ann Arbor, 2017.

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[2] “Truck Log”, Helen Wilson, Ann Arbor, 2017.

[3] “Clean Linen DRIVERS LOG 2017”, Helen Wilson, Ann Arbor, 2017.

[4] “Production Report”, Helen Wilson, Ann Arbor, 2017.

[5] "FINAL PEER GROUP 1-25-16v2.xlsx", Rolando Croocks, Ann Arbor, 2017.

[6] Becker Hospital Review, The Benefits and Challenges of COOPs: Where Do They Fit in Healthcare Reform. Becker Hospital Review, 2012 September 6. [Online]. Available: https://www.beckershospitalreview.com/finance/the-benefits-and-challenges-of-co-ops-where-do-they-fit-in-healthcare-reform.html

[7] Alperovitz, G., Howard, T., & Dubb, S. (2009, Jun). “Cleveland’s Worker-Owned Boom.” Yes Magazine. [Online]. Available: http://staging.democracycollaborative.org/sites/clone.community-wealth.org/files/downloads/Cleveland's%20Worker-Owned%20Boom%20YES!.pdf

[8] Zhang, X., Serrano, J., & Zamora, S. (2013, April). VA Laundry and Linen Distribution Optimization. Unpublished manuscript, Industrial Engineering Department, Worcester Polytechnic Institute.

[9] Chen, C., Poon, R., Shirer, M., & Zhang, M. (2013). Analysis of linen utilization at the University Hospital and the Children and Women’s Hospital. Unpublished manuscript, Industrial and Operations Engineering Department, University of Michigan, Ann Arbor, MI.

[10] Lake, Jerome P. “A Study of the Linen and Laundry Control Procedures at the U.S. Air Force Medical Center, Wright-Patterson Air Force Base, Ohio.” DTIC Online. Available: www.dtic.mil/docs/citations/ADA195435.

[11] Sogaard, Steen. “Laundry Operations.” Textile Services Association, Feb. 2015, pp. 109–114. [Online]. Available: www.tvatteriforbundet.se/wp-content/uploads/2015/01/laundry-operations-av-steen-sagaard-laundry-logics.pdf.

[12] Smiley, S. (2015, April). “What can RFID Laundry Tcarting Do For You?” Tcarting the FRID Industry. [Online]. Available: https://blog.atlasrfidstore.com/what-can-rfid-laundry-tcarting-do

[13] Mahesh, D. “Smart Shelf Technology Shapes Retailing.” Digital Transformation Services at Happiest Minds. [Online]. Available: https://pointofsale.com/2016061612134/Point-of-Sale-News/Redefine-Your-In-Store-Experience-with-Smart-Shelves.html

Appendix A: Internal Linen Distribution Data Collection Sheet

IOE 481 MM Linen Distribution Process Data Collection Sheet

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To: All Stockkeepers

From: Rolando Croocks

Date: November 27, 2017

SUBJ: Data Collection

As part of our ongoing efforts to be more efficient in our linen par stocking efforts, Jake Biegger will be collecting data tomorrow beginning at 4:00 A.M. The purpose is to get a better understanding of the time it takes to perform various tasks and identify opportunity to improve our process. Jake will be approaching several of you to gather information. My expectation is that you will work with him so that we can collect the data, which will be shared with you once analyzed. Please fill out one sheet per round-trip.

Shift (circle one): 4:00 AM 5:00 AM 9:00 AM

Worker: ______________

Area of hospital delivering to (hospital name, floor): ________________________

1. Time to pack cart: ____________

2. Time taken to walk from holding room to first clean room: __________________

3. Total (estimated) time taken to unload linen at the clean rooms: ______________________

4. *If done while unloading* Time to count inventory needs at each clean room:

______________ ______________ ______________ ______________ ______________ *If not done while unloading* please indicate estimated time spent counting inventory for this round

trip: __________________

5. Time to walk back from last clean room to holding room: ___________________

6. Time to restock unused linen (if applicable): _______________________

7. Total time of process (from packing cart to returning to the holding room): ______________

Appendix B: Survey Questions Asked to COOPs COOP Benchmarking Survey Questions (adapted from Google Form):-Name of the COOP-Name and position of the contact

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-Number of hospitals being served-Do majority of hospitals have their own safety stock (in case of unexpected changes in delivery schedules due to extreme cases)? If so, is that managed separately or are you providing the service? - Approximately how many times do the delivery schedule gets interrupted annually due to extreme cases? - Is your institution capable of satisfying urgent laundry needs outside of scheduled times in emergency situations? If so, what steps are being taken? - Is proximity to hospitals the main deciding factor for client acquisition? What is the range of proximity to hospitals being served at the moment? - What is the number of unique linen items processed? Is this number the same for all hospitals? - Does your institution provide linen items for specialty services like women's and children's services? - What is the impact of having less items in COOP operations? What was the process as in terms of finalizing the current number of items? - What is the number of pounds processed? - What are the responsibilities of your COOP in operations? (any documents outlining the process flow will be appreciated)- Is there a standardized work method or is the process customized for different hospitals? - How does a typical daily delivery schedule look like? (number of deliveries each day per hospital, in total, etc.)- Are trucks assigned to deliveries for certain hospitals? Can trips to multiple hospitals be fulfilled by one truck load of clean laundry? - What is the square footage of the storage? Is mobile storage within trucks an option? -What type of software/ technological aids are being utilized? - Do hospitals use an electronic barcode system to count linen which makes pre-picking orders possible? If so, compared to manual system, does this make your institutions' job easier in terms of delivering orders? - What are the biggest advantages of using a COOP for hospitals instead of managing their own laundry?

Appendix C: Survey Questions Asked to HospitalsHospital Benchmarking Survey Questions (adapted from Google Form):- Name of the hospital- Name and position of the contact

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- How is the laundry process handled?- If COOP is not used, what are the reasons why the hospital has the current laundry process? Advantages and disadvantages over using a COOP (or advantages in general)?- When was the change to a COOP made? If it is recent, what was the main determinator for this change? (Name of the COOP being used?)- Do you have your own safety stock at the hospital? If so, how is it managed and how are the quota levels determined for the safety stock? - How often do COOP laundry delivery schedules get interrupted? How do you manage unexpected delays or other similar extreme cases? (Approximately how many times do those scheduling changes occur annually?) - In case of an urgent laundry delivery request in an emergency, how responsive is the COOP in terms of meeting the demand? Would operating own laundry operations be more preferable to manage similar crisis cases? - Was COOP proximity to the hospital the main deciding factor? What is the hospital proximity to the COOP being used? - What other factors were considered while choosing the COOP? - Do you own a depot? Is it managed by the COOP? - What is the number of unique linen items processed? Was this number modified with the switch to COOP? What was the process as in terms of finalizing the current number of items? - Do you provide linen items for specialty services like women's and children's services? - What is the number of pounds processed? - What are the responsibilities of the COOP being used in laundry operations? (any documents outlining the process flow will be appreciated) (e.g., are they only responsible from transporting clean linen to the depot (if it exists) or do they perform deliveries to the hospital?)- How does a typical daily delivery schedule look like? (number of deliveries each day, in total, etc.)- Is mobile storage within trucks an option? Or does the COOP own the trucks? - What is the square footage of the in-hospital storage? - What type of software/ technological aids are you utilizing? - Are you using an electronic barcode system to count linen items in clean rooms in the hospital which makes pre-picking orders possible? If so, compared to manual system, does this make your institutions' job easier in terms of distributing linen in hospital? - What are the biggest advantages of using a COOP instead of managing your own laundry? Is COOPs becoming more and more common for hospitals? If so, what do think the reasons are?

Appendix D: Pugh Chart for Comparison of Future State Processes

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