OPSM 301 Operations Management Class 13: Service Design Waiting-Line Models Koç University Zeynep Aksin

Announcements  Lab activity will count as Quiz 2  Exam on 14:00 in SOS Z27 –Study hands-on by solving problems –Study class notes –Read from book to strengthen your background  On 17/11 exam solutions in class  I won’t have office hours on Monday, Canan Uckun will hold additional office hours Monday 13:00-15:00  Today –Service Design (Ch 7 p ) –Waiting-Line Models (Quantitative Module D) –Quiz 3

Services.. .. lead to some desired transformation or improvement in the condition of the consuming unit  …are provided to customers and cannot be produced independently of them  …are produced, distributed and consumed simultaneously

Service Product – Service Process  In most cases the product is your process (eg. concert, amusement park)  Product design involves process design like we have seen before  However customer contact and participation is distinguishing feature –Customer controlled arrivals –Service unique to customer: different service times –Customer experiences process flows

Where is the customer? Service Design Production Quality Assurance Marketing Co-production Measurement

Customer Interaction and Process Strategy Mass ServiceProfessional Service Service FactoryService Shop Commercial Banking General purpose law firms Fine dining restaurants Hospitals Airlines Full-service stockbroker Retailing Personal banking Boutiques Law clinics Fast food restaurants Warehouse and catalog stores No frills airlines Limited service stockbroker For-profit hospitals Degree of Interaction and Customization Degree of Labor Intensity Low High High Low

Service Design Tools: Service Blueprinting  A blueprint is a flowchart of the service process. Answers questions: ‘who does what, to whom?’, ‘how often?’, ‘under what conditions?’  Shows actions of employee and customer, front office and back office tasks, line of visibility and line of interaction  Instrumental in understanding the process and to improve the design. Are there redundancies, or unnecesssarily long paths? Fail points? Possible poka-yokes that might prevent failures?

Service Blueprint for Service at Ten Minute Lube, Inc.

Techniques for Improving Service Productivity  Separation  Self-service  Postponement  Focus  Structure service so customers must go where service is offered  Self-service so customers examine, compare and evaluate at their own pace  Customizing at delivery  Restricting the offerings Strategy Technique

Techniques for Improving Service Productivity - Continued  Modules  Automation  Scheduling  Training  Modular selection of service. Modular production  Separating services that lend themselves to automation  Precise personnel scheduling  Clarifying the service options  Explaining problems  Improving employee flexibility

If you can’t reduce it, fix it: Contact enhancement  consistent work hours  well trained service personnel  good queue discipline  reduce waiting This motivates our analysis of queueing systems

A Basic Queue Server

A Basic Queue Server Customer Arrivals

A Basic Queue Server

A Basic Queue Server Customer Departures

A Basic Queue Server Queue (waiting line) Customer Arrivals Customer Departures

A Basic Queue Server Queue (waiting line) Customer Arrivals Customer Departures Line too long? Customer balks (never enters queue) Line too long? Customer reneges (abandons queue)

Three Parts of a Queuing System at Dave’s Car-Wash

A common assumption: Poisson distribution  The probability that a customer arrives at any time does not depend on when other customers arrived  The probability that a customer arrives at any time does not depend on the time  Customers arrive one at a time  Interarrival times distributed as a negative exponential distribution

Picture of negative exponential distribution: interarrival times at an outpatient clinic

Independence from other customer’s arrival: interarrival times at an ATM

Time independent arrivals: cumulative arrivals at an ATM

Arrivals Served units Service facility Queue Service system Dock Waiting ship line Ships at sea Ship unloading system Empty ships Single-Channel, Single-Phase System

Cars & food Single-Channel, Multi-Phase System Arrivals Served units Service facility Queue Service system Pick-up Waiting cars Cars in area McDonald’s drive-through Pay Service facility

Arrivals Served units Service facility Queue Service system Service facility Example: Bank customers wait in single line for one of several tellers. Multi-Channel, Single Phase System

Service facility Arrivals Served units Service facility Queue Service system Service facility Example: At a laundromat, customers use one of several washers, then one of several dryers. Service facility Multi-Channel, Multi-Phase System

Queueing Analysis-Performance measures Arrival Rate (  Avg Number in Queue ( L q ) Service Rate (  Avg Wait in Queue ( W q )

Queueing Analysis Arrival Rate (  Service Rate (  Avg Time in System ( W s ) Avg Number in System ( L s ) Elements of Queuing System ArrivalsServiceWaiting line Exit Processing order System

Waiting Line Models ModelLayout Source PopulationService Pattern ASingle channelInfiniteExponential BMultichannelInfiniteExponential Single channelInfiniteConstant These three models share the following characteristics: Single phase, Poisson Arrivals, FCFS, and Unlimited Queue Length C

Notation

Operating Characteristics –Model A Utilization (fraction of time server is busy) Average waiting times Average numbers L= W Little’s Law

Example: Model A Drive-up window at a fast food restaurant: Customers arrive at the rate of 25 per hour. The employee can serve one customer every two minutes. Assume Poisson arrival and exponential service rates. A) What is the average utilization of the employee? B) What is the average number of customers in line? C) What is the average number of customers in the system? D) What is the average waiting time in line? E) What is the average waiting time in the system? F)What is the probability that exactly two cars will be in the system?

Example: Model A A) What is the average utilization of the employee?

Example: Model A B) What is the average number of customers in line? C) What is the average number of customers in the system?

Example: Model A D) What is the average waiting time in line? E) What is the average waiting time in the system?

Example: Model A F) What is the probability that exactly two cars will be in the system?

Example: Model B Recall Model A: If an identical window (and an identically trained server) were added, what would the effects be on the average number of cars in the system and the total time customers wait before being served?

Example: Model B Average number of cars in the system (by interpolation)

Example: Model B Total time customers wait before being served

Example: Model C An automated pizza vending machine heats and dispenses a slice of pizza in 4 minutes. Customers arrive at a rate of one every 6 minutes with the arrival rate exhibiting a Poisson distribution. Determine: A) The average number of customers in line. B) The average total waiting time in the system.

Example: Model C A) The average number of customers in line. B) The average total waiting time in the system.

Example: Secretarial Pool  4 Departments and 4 Departmental secretaries  Request rate for Operations, Accounting, and Finance is 2 requests/hour  Request rate for Marketing is 3 requests/hour  Secretaries can handle 4 requests per hour  Marketing department is complaining about the response time of the secretaries. They demand 30 min. response time  College is considering two options: –Hire a new secretary –Reorganize the secretarial support

Current Situation Accounting Finance Marketing Operations 2 requests/hour 3 requests/hour 2 requests/hour 4 requests/hour

Current Situation: waiting times W = service time + Wq W = 0.25 hrs hrs = 30 minutes Accounting, Operations, Finance: Marketing: W = service time + Wq W = 0.25 hrs hrs = 60 minutes

Proposal: Secretarial Pool Accounting Finance Marketing Operations 9 requests/hour

Proposal: Secretarial Pool Wq = hrs. W= hrs hrs.= 17 minutes In the proposed system, faculty members in all departments get their requests back in 17 minutes on the average. (Around 50% improvement for Acc, Fin, and Ops and 75% improvement for Marketing)

Deciding on the Optimum Level of Service Total expected cost Cost of waiting time Cost Low level of service Optimal service level High level of service Minimum total cost Cost of providing service