Capacity Management in Operations: Matching Demand and Service Levels

1. B7801: Operations Management27 March 1998 - Agenda Mass Customization National Cranberry Cooperative Capacity Management Queue and customer management

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

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

4. Key steps in capacity planning What is demand for our product/service like? What are its main characteristics? How accurately can we predict it? 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 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?

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

6. 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)

7. Forecasting Aggregate where possible, but keep enough detail to make your planning decisions. • 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 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.

8. 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 training @ $15/hr. • 80 hrs. severance pay @ $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.

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

10. Chase strategy financials

11. Strategy 2: Level production x10,000 units/month

12. Level strategy financials

13. Strategy 3: Mixed x10,000 units/month

14. Mixed strategy financials

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

16. 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)

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

18. 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?

19. Service-System Design Matrix Degree of customer/server contact High none some much Low Face-to-face total customization Face-to-face loose specs Sales Opportunity Production Efficiency Face-to-face tight specs Phone Contact On-site technology Mail contact Low High

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

21. 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

22. 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

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

24. 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

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

26. Characteristics of a Well-Designed Service System 5. It provides effective linksbetween 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

27. Servers Components of the Queuing Phenomenon Waiting Line Customer Arrivals Exit

28. 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

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

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

31. 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

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

33. Degree of Patience No Way! No Way! BALK RENEG

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

35. 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

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

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

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

39. 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?

40. 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

41. 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

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

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

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

46. 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

50. s servers, one line, priority (high or low) Poisson arrivals, high priority arrival rate = l1, low priority arrival rate = l2 Exponential service time, service rate at each server = m M/M/s Queue with Priority 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