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Matching Supply with Demand: An Introduction to Operations Management Gérard Cachon ChristianTerwiesch All slides in this file are copyrighted by Gerard Cachon and Christian Terwiesch. Any instructor that adopts Matching Supply with Demand: An Introduction to Operations Management as a required text for their course is free to use and modify these slides as desired. All others must obtain explicit written permission from the authors to use these slides.

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**Variability – Waiting Times**

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**An Example of a Simple Queuing System**

Call center Answered Calls Incoming calls Sales reps processing calls Calls on Hold At peak, 80% of calls dialed received a busy signal. Customers getting through had to wait on average 10 minutes Extra telephone expense per day for waiting was $25,000. Blocked calls (busy signal) Abandoned calls (tired of waiting) Financial consequences Lost throughput Holding cost Lost goodwill Lost throughput (abandoned) Cost of capacity Cost per customer $$$ Revenue $$$

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**A Somewhat Odd Service Process**

Patient Arrival Time Service 1 2 3 4 5 6 7 8 9 10 11 12 15 20 25 30 35 40 45 50 55 7:00 7:10 7:20 7:30 7:40 7:50 8:00

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**A More Realistic Service Process**

Patient 1 Patient 3 Patient 5 Patient 7 Patient 9 Patient 11 Patient Arrival Time Service 1 2 3 4 5 6 7 8 9 10 11 12 18 22 25 30 36 45 51 55 Patient 2 Patient 4 Patient 6 Patient 8 Patient 10 Patient 12 Time 7:00 7:10 7:20 7:30 7:40 7:50 8:00 3 2 Number of cases 1 2 min. 3 min. 4 min. 5 min. 6 min. 7 min. Service times

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**Variability Leads to Waiting Time**

Patient Arrival Time Service 1 2 3 4 5 6 7 8 9 10 11 12 18 22 25 30 36 45 51 55 Service time Wait time 7:00 7:10 7:20 7:30 7:40 7:50 8:00 5 4 3 2 1 Inventory (Patients at lab) 7:00 7:10 7:20 7:30 7:40 7:50 8:00

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**Modeling Variability in Flow**

Flow Rate Minimum{Demand, Capacity} = Demand = 1/a Outflow No loss, waiting only This requires u<100% Outflow=Inflow Buffer Processing Inflow Demand process is “random” Look at the inter-arrival times a: average inter-arrival time CVa = Often Poisson distributed: CVa = 1 Constant hazard rate (no memory) Exponential inter-arrivals Difference between seasonality and variability Processing p: average processing time Same as “activity time” and “service time” CVp = Can have many distributions: CVp depends strongly on standardization Often Beta or LogNormal IA1 IA2 IA3 IA4 Time St-Dev(processing times) Average(processing times) St-Dev(inter-arrival times) Average(inter-arrival times)

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**The Waiting Time Formula**

Average flow time T Flow rate Inventory waiting Iq Increasing Variability Inflow Outflow Entry to system Begin Service Departure Time in queue Tq Service Time p Theoretical Flow Time Flow Time T=Tq+p Utilization 100% Waiting Time Formula Variability factor Utilization factor Service time factor

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**Waiting Time Formula for Multiple, Parallel Resources**

Inventory in service Ip Inventory waiting Iq Inflow Outflow Flow rate Entry to system Begin Service Departure Time in queue Tq Service Time p Flow Time T=Tq+p Waiting Time Formula for Multiple (m) Servers

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**Example: Online retailer**

Customers send s to a help desk of an online retailer every 2 minutes, on average, and the standard deviation of the inter-arrival time is also 2 minutes. The online retailer has three employees answering s. It takes on average 4 minutes to write a response . The standard deviation of the service times is 2 minutes. Estimate the average customer wait before being served.

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**Service Levels in Waiting Systems**

Fraction of customers who have to wait x seconds or less 1 90% of calls had to wait 25 seconds or less 0.8 Waiting times for those customers who do not get served immediately 0.6 0.4 Fraction of customers who get served without waiting at all 0.2 50 100 150 200 Waiting time [seconds] Target Wait Time (TWT) Service Level = Probability{Waiting TimeTWT} Example: Deutsche Bundesbahn Call Center - now (2003): 30% of calls answered within 20 seconds - target: 80% of calls answered within 20 seconds Formulas: see Service Operations course / simulation

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**Managerial Responses to Variability: Pooling**

Independent Resources 2x(m=1) Waiting Time Tq 70.00 m=1 60.00 50.00 40.00 m=2 30.00 20.00 m=5 Pooled Resources (m=2) 10.00 m=10 0.00 60% 65% 70% 75% 80% 85% 90% 95% Utilization u Implications: + balanced utilization + Shorter waiting time (pooled safety capacity) - Change-overs / set-ups

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**Managerial Responses to Variability: Priority Rules in Waiting Time Systems**

Service times: A: 9 minutes B: 10 minutes C: 4 minutes D: 8 minutes A C B 4 min. D 9 min. 19 min. C 12 min. A 23 min. D 21 min. B Total wait time: =51min Total wait time: =38 min Flow units are sequenced in the waiting area (triage step) Shortest Processing Time Rule - Minimizes average waiting time - Problem of having “true” processing times First-Come-First-Serve - easy to implement - perceived fairness Sequence based on importance - emergency cases - identifying profitable flow units

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**Data in Practical Call Center Setting**

Number of customers Number of customers Distribution Function Distribution Function Per 15 minutes Per 15 minutes 160 160 1 1 140 140 0.8 0.8 Exponential distribution Exponential distribution 120 120 100 100 0.6 0.6 80 80 0.4 0.4 60 60 Empirical distribution Empirical distribution (individual points) (individual points) 40 40 0.2 0.2 20 20 0:00:00 0:00:00 0:00:09 0:00:09 0:00:17 0:00:17 0:00:26 0:00:26 0:00:35 0:00:35 0:00:43 0:00:43 0:00:52 0:00:52 0:01:00 0:01:00 0:01:09 0:01:09 0:15 0:15 2:00 2:00 3:45 3:45 5:30 5:30 7:15 7:15 9:00 9:00 10:45 10:45 12:30 12:30 14:15 14:15 16:00 16:00 17:45 17:45 19:30 19:30 21:15 21:15 23:00 23:00 Inter - arrival time Time Seasonality vs. variability Need to slice-up the data Within a “slice”, exponential distribution (CVa=1) See chapter 6 for various data analysis tools

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**What to Do With Seasonal Data**

Measure the true demand data Apply waiting model in each slice Slice the data by the hour (30min, 15min)

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**Managing Waiting Systems: Points to Remember**

Variability is the norm, not the exception Variability leads to waiting times although utilization<100% Use the Waiting Time Formula to - get a qualitative feeling of the system - analyze specific recommendations / scenarios Managerial response to variability: - understand where it comes from and eliminate what you can - accommodate the rest by holding excess capacity Difference between variability and seasonality - seasonality is addressed by staffing to (expected) demand

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**Call Center: Lean Diagnostic Example**

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Call Center Overview Large UK Retail bank- operates each week day from 8.30am to 6pm Handles three types of calls (agents are dedicated to call type) All subsequent information is about type 1 calls (similar numbers for type 2 and 3 calls) Type 1 calls are direct payments for mortgages / loans that customers do using checks or credit cards Agents make 30 pounds per hour loaded salary Typical time commitments from agents: Taking calls / “wrap time” Six weeks of paid vacation Average absenteeism rate of 6% Other time commitments for the agents, such as breaks, training, special duties: 15% Picture from Switzer & CO, illustrative only

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**Demand, Call Handling, and Service Level**

Demand information (Type 1 calls) Call volume changes over the course of the day Typical day is shown on the right Calls come in randomly, given this seasonal pattern (CVa=1) Call center operates for 252 days a year (closed week-ends, holidays) Call handling information Type 1 calls take on average 5 minutes (with a CVp=1) This includes actual call time as well as wrap time Actual value varies by agent / customer Number of staffed seats for type 1 calls In each time slot, 9 agents are currently on duty from 8.30am to 6.00pm Service level / targeted wait time The average wait time is targeted to be 2 minutes or less Present wait time is significantly longer for time slot At other times, agents have plenty of capacity (e.g., at 8.30am) Cost: about 0.04 pounds per minute for wait or talk time

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Sample of Calls Variation exists across agents handling calls – the following information is about type 1 calls A sample of calls across three agents has been collected (Matthew, Daniel, and Anna) For the purpose of this exercise, consider the following calls: 1b, 1c, 2a, 2b, 2d, 3a, 3b You can assume that this data (and its variation in customer and agent behavior) is representative Audio files have been made available to you Picture from Livehacker blog

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Assignment You are on site in the call center and have the information provided above The goal of the project is to conduct an initial diagnosis of the call center operation What productivity improvement will you aim for? What is your pitch to the client’s CEO? Detailed assignments: 1. Excel model of status quo (with Tq) 2. Compute current OPE / OEE 3. Detailed, micro-level analysis of calls; estimate improvement potential 4. Evaluate improvements (shorter calls, staffing plan, pooling, other)

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What generates queues? An arrival rate that predictably exceeds the service rate (i.e., capacity) of a process. e.g., toll booth congestion on the NJ Turnpike during the Thanksgiving Day rush. Variation in the arrival and service rates in a process where the average service rate is more than adequate to process the average arrival rate. e.g., calls to a brokerage are unusually high during a particular hour relative to the same hour in other weeks (i.e., calls are high by random chance).

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**Predicable and unpredictable variation at An-ser**

20 40 60 80 100 120 140 160 0:15 2:00 3:45 5:30 7:15 9:00 10:45 12:30 14:15 16:00 17:45 19:30 21:15 23:00 Number of customers Per 15 minutes Time

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**Defining inter-arrival times and a stationary process**

An inter-arrival time is the amount of time between two arrivals to a process. An arrival process is stationary over a period of time if the number of arrivals in any sub-interval depends only on the length of the interval and not on when the interval starts. For example, if the process is stationary over the course of a day, then the expected number of arrivals within any three hour interval is about the same no matter which three hour window is chosen (or six hour window, or one hour window, etc). Processes tend to be non-stationary (or seasonal) over long time periods (e.g., over a day or several hours) but stationary over short periods of time (say one hour, or 15 minutes).

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**Arrivals to An-ser over the day are non-stationary**

20 40 60 80 100 120 140 160 0:15 2:00 3:45 5:30 7:15 9:00 10:45 12:30 14:15 16:00 17:45 19:30 21:15 23:00 Number of customers Per 15 minutes Time The number of arrivals in a 3-hour interval clearly depends on which 3-hour interval your choose … … and the peaks and troughs are predictable (they occur roughly at the same time each day.

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**How to describe (or model) inter-arrival times**

We will use two parameters to describe inter-arrival times to a process: The average inter-arrival time. The standard deviation of the inter-arrival times. What is the standard deviation? Roughly speaking, the standard deviation is a measure of how variable the inter-arrival times are. Two arrival processes can have the same average inter-arrival time (say 1 minute) but one can have more variation about that average, i.e., a higher standard deviation.

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**Relative and absolute variability**

The standard deviation is an absolute measure of variability. Two processes can have the same standard deviation but one can seem much more variable than the other: Below are random samples from two processes that have the same standard deviation. The left one seems more variable.

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**Relative and absolute variability (cont)**

The previous slide plotted the processes on two different axes. Here, the two are plotted relative to their average and with the same axes. Relative to their average, the one on the left is clearly more variable (sometime more than 400% above the average or less than 25% of the average)

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**Coefficient of variation**

The coefficient of variation is a measure of the relative variability of a process – it is the standard deviation divided by the average. The coefficient of variation of the arrival process: CVa = 10 /10 = 1 CVa = 10 /100 = 0.1

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**Service time variability**

The coefficient of variation of the service time process: Frequency 800 Standard deviation of activity times = 150 seconds. Average call time (i.e., activity time) = 120 seconds. CVp = 150 / 120 = 1.25 600 400 200 100 200 300 400 500 600 700 800 900 1000 Call durations (seconds)

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**Customers waiting for service**

A multi-server queue Arriving customers Departing customers Customers waiting for service Multiple servers Assumptions: All servers are equally skilled, i.e., they all take p time to process each customer. Each customer is served by only one server. Customers wait until their service is completed. There is sufficient capacity to serve all demand. a = average interarrival time Flow rate = 1 / a p = average activity time Capacity of each server = 1 / p m = number of servers Capacity = m x 1 / p Utilization = Flow rate / Capacity = (1 / a) / (m x 1 / p) = p / (a x m)

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**A multi-server queue – some data and analysis**

Suppose: a = 35 seconds per customer Flow rate = 1 / 35 customers per second p = 120 seconds per customer Capacity of each server = 1 / 120 customers per second m = number of servers = 4 Then Capacity = m x 1 / p = 4 x 1 / 120 = 1 / 30 customers per second Utilization = Flow rate / Capacity = p / (a x m) = 120 / (35 x 4) = 85.7%

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**The implications of utilization**

A 85.7% utilization means: At any given moment, there is a 85.7% chance a server is busy serving a customer and a = 14.3% chance the server is idle. At any given moment, on average 85.7% of the servers are busy serving customers and 1 – = 14.3% are idle. If utilization < 100% then: The system is stable which means that the size of the queue does not keep growing over time. (Capacity is sufficient to serve all demand) If utilization >= 100% then: The system is unstable, which means that the size of the queue will continue to grow over time.

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**Stable and unstable queues**

p = 120 m = 4 Utilization = 86% a = 35 p = 120 m = 3 Utilization = 114%

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**Time spent in the two stages of the system**

Recall: p = Activity time m = number of servers Tq = Time in queue p = Time in service T = Tq + p = Flow time The above Time in queue equation works only for a stable system, i.e., a system with utilization less than 100%

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**What determines time in queue in a stable system?**

The capacity factor: Average processing time of the system = (p/m). Demand does not influence this factor. The variability factor: This is how variability influences time in queue – the more variability (holding average demand and capacity constant) the more time in queue. The utilization factor: Average demand influences this factor because utilization is the ratio of demand to capacity.

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**Evaluating Time in queue**

Suppose: a = 35 seconds, p = 120 seconds, m = 4 Utilization = p / (a x m) = 85.7% CVa = 1, CVp = 1 So Flow time = = 270 seconds. In other words, the average customer will spend 270 seconds in the system (waiting for service plus time in service)

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**How many customers are in the system?**

Use Little’s Law, I = R x T R = Flow Rate = (1/a) The flow rate through the system equals the demand rate because we are demand constrained (utilization is less than 100%) Iq = (1/a) x Tq = Tq / a Ip = (1/a) x p = p / a Note, time in service does not depend on the number of servers because when a customer is in service they are processed by only one server no matter how many servers are in the system. Iq = Inventory in queue Ip = Inventory in service I = Iq + Ip = Inventory in the system

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**Utilization and system performance**

Activity time = 1 m = 1 CVa = 1 CVp = 1 Time in queue increases dramatically as the utilization approaches 100%

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**Which system is more effective?**

Across these three types of systems: Variability is the same: CVa = 1, CVp = 1 Total demand is the same: 1/35 customers per second. Activity time is the same: p = 120 Utilization is the same: p / a x m = 85.7% The probability a server is busy is the same = 0.857 Pooled system: One queue, four servers a = 35 m = 4 Partially pooled system: Two queues, two servers with each queue. For each of the 2 queues: a = 35 x 2 = 70 m = 2 Separate queue system: Four queues, one server with each queue. For each of the 4 queues: a = 35 x 4 = 140 m = 1

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**Pooling can reduce Time in queue considerably**

a = 35, p = 120, m = 4, CVa = 1, CVp = 1 Tq = 150 Flow Time = 270 Pooled system For each of the two queues: a = 70, p = 120, m = 2, CVa = 1, CVp = 1 Tq = 336 Flow Time = 456 Partially pooled system For each of the four queues: a = 140, p = 120, m = 1, CVa = 1, CVp = 1 Tq = 720 Flow Time = 840 Separate queue system

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**Pooling reduces the number of customers waiting but not the number in service**

Iq = 4.3 Ip = 3.4 Iq = 4.3 Ip = 3.4 Pooled system Inventories for each queue within the system Partially pooled system Iq = 4.8 Ip = 1.7 Iq = 9.6 Ip = 3.4 ( 2 times the inventory in each queue ) Inventories for the total system Separate queue system Iq = 5.1 Ip = 0.86 Iq = 20.4 Ip = 3.4 ( 4 times the inventory in each queue )

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**Why does pooling work? = A customer waiting = A customer in service**

Separate queue system Pooled system Both systems currently have 6 customers In the pooled system, all servers are busy and only 2 customers are waiting In the separate queue system, 2 servers are idle and 4 customers are waiting The separate queue system is inefficient because there can be customers waiting and idle servers at the same time.

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**Some quick service restaurants pool drive through ordering**

The person saying “Can I take your order?” may be hundreds (or even thousands) of miles away: Pooling the order taking process can improve time-in-queue while requiring less labor. It has been shown that queue length at the drive through influences demand – people don’t stop if the queue is long. However, this system incurs additional communication and software costs.

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**Limitations to pooling**

Pooling may require workers to have a broader set of skills, which may require more training and higher wages: Imagine a call center that took orders for McDonalds and Wendys … now the order takers need to be experts in two menus. Suppose cardiac surgeons need to be skilled at kidney transplants as well. Pooling may disrupt the customer – server relationship: Patients like to see the same physician. Pooling may increase the time-in-queue for one customer class at the expense of another: Removing priority security screening for first-class passengers may decrease the average time-in-queue for all passengers but will likely increase it for first-class passengers.

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**Why the long queues for the women’s restroom?**

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**Potty-parity Laws Women’s Restroom Equity Bill:**

Passed NY City Counsel May 2005. Requires women’s restrooms to have twice the flushing capacity of men’s restrooms (at least a 2:1 ratio of women’s toilets to men’s toilets & urinals) In the context of Time in queue equation, this law requires m to increase for women, which decreases the capacity factor (p/m), and decreases utilization. Other solutions: Unisex restrooms (pools capacity, at least the toilets) Flexible partitions that can change the size of each restroom.

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Summary Even when a process is demand constrained (utilization is less than 100%), waiting time for service can be substantial due to variability in the arrival and/or service process. Waiting times tend to increase dramatically as the utilization of a process approaches 100%. Pooling multiple queues can reduce the time-in-queue with the same amount of labor (or use less labor to achieve the same time-in-queue).

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**Recitation Questions (prepare the questions described on the following slides for the R5 recitation)**

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Rent-A-Phone Rent-A-Phone is a new service company that provides European mobile phones to American visitors to Europe. The company currently has 80 phones available at Charles de Gaulle Airport in Paris. There are, on average, 25 customers per day requesting a phone. These requests arrive uniformly throughout the 24 hours the store is open. (Note: This means customers arrive at a faster rate than 1 customer per hour) The corresponding coefficient of variation is 1.

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Rent-A-Phone (cont) Customers keep their phone for 72 hours on average. The standard deviation of this time is 100 hours. Given that Rent-A-Phone currently does not have a competitor in France providing equally good service, customers are willing to wait for the telephones. Yet, during the waiting period, customers are provided a free calling card. Based on prior experiences, Rent-A-Phone found that the company incurred a cost of $1 per hour per waiting customer, independent of day or night.

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**Rent-A-Phone (Questions)**

What is the average number of telephones the company has in its store? How long does a customer, on average, have to wait for the phone? What are the total monthly (30 days) expenses for telephone cards? Assume that it costs Rent-A-Phone $1000 per unit per month for a new phone. Is it worth it to buy one additional phone? Why?

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Queuing Models and Capacity Planning

Queuing Models and Capacity Planning

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