B7801: Operations Management 27 March Agenda Mass Customization National Cranberry Cooperative Capacity Management Queue and customer management

Why is capacity management important? ROA PROFIT MARGIN ASSET TURNOVER = x 1) Driver of Financial Performance 2) Driver of Operating Performance direct labor overhead costs productivity facility utilization equipment utilization inventory turnover delivery performance fill rate lead time service levels wait times availability Capacity Utilization increasing decreasing

Matching demand and capacity time # units/hr. demand poor service / lost revenue excess assets and costs capacity How do firms match capacity to demand?

Key steps in capacity planning STEP 1: Forecast demand –forecast quantities –forecast methods –understanding errors and uncertainties STEP 2: Assess the options for meeting demand –capacity increases/decreases –capacity allocation –inventory –demand management STEP 3: Construct and evaluate the plans –planning methodology –evaluation/robustness scenario analysis simulation What is demand for our product/service like? What are its main characteristics? How accurately can we predict it? What options do we have available to meet demand? What constraints do we face? What is the relationship between capacity and service levels? What is our cost structure? How do we go about developing a plan? What is the effect of forecast uncertainty on plan performance?

A hierarchy of time scales Long Term (1-10 yrs.) Medium Term (3 mon. - 1 yr.) Short Term (hourly, daily,wkly) facility expansion hiring/firing technology investments make/buy capacity allocation hiring/firing overtime inventory build-up detailed prod. scheduling staff scheduling detailed allocation

An example: National Cranberry Cooperative Forecasting demand –peak season same as previous year –no increase in total volume –increase to 70% wet Assessing options to meet demand –do nothing –overtime –capacity expansion (bins, dryers) Constructing and evaluating a plan –methodology (trial and error, incremental analysis) –process flow analysis to determine cost/performance overtime cost truck backup –evaluation/robustness average cost/benefit estimates worst-case performance (peak day) (also remember McDonald’s,BK!!) simulation Time scales (med: add dryer, short: overtime on demand)

Forecasting What to forecast –level of aggregation one location vs. region individual product vs. product family daily, weekly or monthly –trade-off: detail vs. forecast accuracy Forecast methodology –subjective methods (Delphi method) –time series (exponential smoothing) –causal methods (regression) Forecast errors –point estimate = “best guess” –magnitude of error MAD (mean absolute deviation) MSD (mean square deviation) –distribution of errors Aggregate where possible, but keep enough detail to make your planning decisions. If data is available and product or service is mature, use data intensive methods; otherwise, resort to subjective methods. Try to quantify forecast errors as well as point estimates. Factor forecast uncertainty into your plans.

Ex: Aggregate planning in an ice tea bottling plant demand forecast next 9 months: 27, 20, 36, 45, 78, 97, 118, 121, 82 (x10,000 units (12-oz.)) 20 workers required capacity is 3,000 units/hour wages: –$15/hr regular time –$16/hr second shift (8 hr shifts) –$20/hr overtime hiring/firing –16 hrs. of $15/hr. –80 hrs. severance $16/hr. 500,000 unit warehouse. Extra storage is $1/month per 100 units. unit revenue = $0.40, unit cost (material) = $0.20 $2M working capital line of credit (18% per year). Current balance is $1M.

Strategy 1: Chase demand (production = demand) x10,000 units/month

Chase strategy financials

Strategy 2: Level production x10,000 units/month

Level strategy financials

Strategy 3: Mixed x10,000 units/month

Mixed strategy financials

Components of the Queuing Phenomenon Servers Waiting Line Servicing System Customer Arrivals Exit

Some Service Generalizations 1. Everyone is an expert on services. 2. Services are idiosyncratic. 3. Quality of work is not quality of service. 4. High-contact services are experienced, whereas goods are consumed. 5. We cannot inventory services (capacity becomes dominant issue)

Capacity Management in Services You cannot store service output If you cannot store output, you store the demand

Strategic Service Vision Who is our customer? How do we differentiate our service in the market? What is our service package and the focus? What are the actual processes, systems, people, technology and leadership?

Service-System Design Matrix Mail contact Face-to-face loose specs Face-to-face tight specs Phone Contact Face-to-face total customization nonesomemuch High Low High Low Degree of customer/server contact On-site technology Sales Opportunity Production Efficiency

Three Contrasting Service Designs The production line approach The self-service approach The personal attention approach

Some Performance Measures Average time spent waiting in queue Average time in system Average length of queue Average number of customers in system Probability that a customer waits before service begins Server utilization

Strategies for effective capacity management Maximize process flexibility –mix flexibility –volume flexibility Standardize the product/service reduce variety –risk pooling –reduced forecast error Centralize operations –risk pooling –reduced forecast error Reduce lead time –reduced forecast error –minimize overshooting/undershooting demand

Some Service Generalizations 1. Everyone is an expert on services. 2. Services are idiosyncratic. 3. Quality of work is not quality of service.

Some Service Generalizations 4. High-contact services are experienced, whereas goods are consumed. 5. Effective management of services requires an understanding of marketing and personnel, as well as operations. 6. Services often take the form of cycles of encounters involving face-to-face, phone, electromechanical, and/or mail interactions

Characteristics of a Well- Designed Service System 1. Each element of the service system is consistent with the operating focus of the firm. 2. It is user-friendly. 3. It is robust. 4. It is structured so that consistent performance by its people and systems is easily maintained

Characteristics of a Well- Designed Service System 5. It provides effective links between the back office and the front office so that nothing falls between the cracks. 6. It manages the evidence of service quality in such a way that customers see the value of the service provided. 7. It is cost-effective

Components of the Queuing Phenomenon Servers Waiting Line Customer Arrivals Exit

Customers arrivals to a bank Average customers per minute = 10 Average service time = 30 seconds –HOW MANY TELLERS ARE NEEDED? Case I:No variability Case II:Variability in arrival process Case III:Variability in arrival & service processes

How many tellers?: Variability in both arrival and service processes

Methods for reducing impact of variability Demand –better forecasting –pricing –appointment systems Process –standardization –training –automation –self-service –variable staffing –use of inventory

Tools for capacity planning in service systems Queueing models –fast –little data needed Simulation –can handle complexity Linear programming –to allocate capacity over multiple facilities or multiple locations –scheduling and other constraints can be readily incorporated

Line Structures Single Channel Multichannel Single Phase Multiphase One-person barber shop Car wash Hospital admissions Bank tellers’ windows

Degree of Patience No Way! BALKRENEG

Key facts needed for a model Average number of customer arrivals per unit of time Average service time per customer The number of servers

Assumptions in our models FCFS Events occur one at a time We are interested in long run avg performance Unlimited storage Utilization < 100% No predictable variation Unpredictable variation –arrivals - Poisson processes –service - exponential distributed processing times

Operating Focus Customer treatment Speed and convenience of service delivery Variety of services Quality of tangibles Unique skills

Service-System Design Matrix Mail contact Face-to-face loose specs Face-to-face tight specs Phone Contact Face-to-face total customization Buffered core (none) Permeable system (some) Reactive system (much) High Low High Low Degree of customer/server contact On-site technology Sales Opportunity Production Efficiency

Three Contrasting Service Designs The production line approach The self-service approach The personal attention approach

Example: Model 1 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?

Example: CVS Manager is considering two ways of using cashiers: ( Assume customers arrive randomly at a rate of 15 per hour ) 1 fast clerk -- serves at an average of 2 minutes per customer or 2 moderate clerks -- each serves at an average of 4 minutes per customer

Some Performance Measures Average time spent waiting in queue Average time in system Average length of queue Average number of customers in system Probability that a customer waits before service begins Server utilization

Example: Model 1 A) What is the average utilization of the employee?

13 Example: Model 1 B) What is the average number of customers in line? C) What is the average number of customers in the system?

14 Example: Model 1 D) What is the average waiting time in line? E) What is the average waiting time in the system?

Example: CVS Manager is considering two ways of using cashiers: ( Assume customers arrive randomly at a rate of 15 per hour ) 1 fast clerk -- serves at an average of 2 minutes per customer or 2 moderate clerks -- each serves at an average of 4 minutes per customer

M/M/s Queue with Priority Poisson arrivals, high priority arrival rate =  low priority arrival rate = 2 Exponential service time, service rate at each server =  s servers, one line, priority (high or low) Performance measures (high and low): utilization, probability of delay average number of customers in system average throughput time average queue length average waiting time ===> On-line queueing spreadsheets

M/M/s-Priority Queueing Spreadsheet

Suggestions for Managing Queues Do not overlook the effects of perceptions management. Determine the acceptable waiting time for your customers. Install distractions that entertain and physically involve the customer. Get customers out of line. Only make people conscious of time if they grossly overestimate waiting times

Perceptions of waiting times Unoccupied delays feel longer than occupied delays Pre-process delays feel longer than in- process delays Anxious delays feel longer than relaxed delays Unacknowledged delays feel longer than acknowledged delays Waiting alone vs. waiting with others

Suggestions for Managing Queues Modify customer arrival behavior. Keep resources not serving customers out of sight. Segment customers by personality types. Adopt a long-term perspective. Never underestimate the power of a friendly server

What did we learn?