Managing Capacity Chapter Chapter Objectives Be able to:  Explain what capacity is, how firms measure capacity, and the difference between theoretical and rated capacity  Describe the pros and cons associated with three different capacity strategies: lead, lag, and match  Apply a wide variety of analytical tools to capacity decisions, including expected value and break-even analysis, decision trees, learning curves, the Theory of Constraints, waiting line theory, and Little’s Law Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-2 Definitions  Capacity – The capability of a worker, a machine, a workcenter, a plant, or an organization to produce output in a time period  Capacity decisions – © 2010 APICS Dictionary  How is it measured?  Which factors affect capacity?  The impact of the supply chain on the organization’s effective capacity Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-3 Measures of Capacity  Theoretical capacity – The maximum output capability, allowing for no adjustments for preventive maintenance, unplanned downtime, or the like  Rated capacity – The long-term, expected output capability of a resource or system © 2010 APICS Dictionary Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-4 Examples of Capacity Table 6.1 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-5 Indifference Point Examples Capacity for a PC Assembly Plant (800 units per line per shift)×(# of lines)×(# of shifts) Controllable Factors Uncontrollable Factors or shifts? or lines? Employee training? Supplier problems? 98% or 100% good? Late or on time? Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-6 Three Common Capacity Strategies  Lead capacity strategy – A capacity strategy in which capacity is added in anticipation of demand  Lag capacity strategy – A capacity strategy in which capacity is added only after demand has materialized  Match capacity strategy – A capacity strategy that strikes a balance between the lead and lag capacity strategies by avoiding period of high under or overutilization Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-7 Comparing Strategies Figure 6.1 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-8 Evaluating Capacity Alternatives      Cost Comparison Expected Value Decision Trees Break-Even Analysis Learning Curves Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall 6-9 Cost Comparison  Fixed costs – The expenses an organization incurs regardless of the level of business activity  Variable costs – Expenses directly tied to the level of business activity Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 10 Theory of Constraints – Example 6.6 Current Process Figure 6.9 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 30 Theory of Constraints – Example 6.6 Adding a Second Stylist Figure 6.10 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 31 Theory of Constraints – Example 6.6 Adding One Shampooer and Two Stylists Figure 6.11 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 32 Waiting Line Theory  Waiting Line Theory – A theory that helps managers evaluate the relationship between capacity decisions and important performance issues such as waiting times and line lengths Figure 6.12 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 33 Waiting Line Theory  Waiting Line Concerns:  What percentage of the time will the server be busy?  On average, how long will a customer have to wait in line? How long will the customer be in the system?  On average, how may customers will be in line?  How will those averages be affected by the arrival rate of customers and the service rate of the workers? Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 34 Waiting Lines – Example 6.7 The probability of arrivals in a time period = Example: Customers arrive at a drive-up window at a rate of per minute If the number of arrivals follows a Poisson distribution, what is the probability that two or fewer customers would arrive in a minute? P(< 2) = P(0) + P(1) + P(2) = 050 + 149 + 224 = 423 or 42.3% Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 35 Waiting Lines – Example 6.7 The average utilization of the system: Example: Suppose that customers arrive at a rate of four per minute and that the worker at the window is able to handle on average customers per minute The average utilization of the system is: Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 36 Waiting Lines – Example 6.8 The average number of customers waiting in the system (CW) = The average number of customers in the system (CS) = Example: Given an arrival rate of four customers per minute and a service rate of five customers per minute: Average number of customers waiting: Average number in the system: Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 37 Waiting Lines – Example 6.9 The average time spent waiting (TW) = The average time spent in the system (TS) = Example: Given an arrival rate of four customers per minute and a service rate of five customers per minute: Average time spent waiting: Average time spent in the system: Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 38 Little’s Law Little’s Law is a law that holds for any system that has reached a steady state that enables us to understand the relationship between inventory, arrival time, and throughput time I = RT Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 39 Little’s Law - Example 6.11 Figure 6.14 Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 40 Little’s Law - Example 6.11 Average Throughput Time = T = I/R = (25 orders) / (100 orders per day) = 25 days in order processing Average time an order spends in workcenter A = T = I/R = (14 orders)/(70 orders per day) = days in workcenter A Amount of time the average A order spends in the plant = Order processing time + workcenter A time = 25 days + days = 45 days Amount of time the average B order spends in the plant = Order processing time + workcenter B time = 25 days + 05 days = 30 days Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 41 Little’s Law - Example 6.11 Average time an order spends in the plant = 70% x 45 days + 30% *.30 days = 405 days Estimate average throughout time for the entire system = T = I/R = (40.5 orders)/(100 orders per day) = 405 days for the average order Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 42 Managing Capacity Case Study Forster’s Market Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 43 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher Printed in the United States of America Copyright © 2013 Pearson Education, Inc publishing as Prentice Hall - 44 .. .Chapter Objectives Be able to:  Explain what capacity is, how firms measure capacity, and the difference between theoretical and rated capacity  Describe the pros and cons associated... Trees  Decision tree – A visual tool that decision makers use to evaluate capacity decisions to enable users to see the interrelationships between decisions and possible outcomes Copyright ©... organization to produce output in a time period  Capacity decisions – © 2010 APICS Dictionary  How is it measured?  Which factors affect capacity?  The impact of the supply chain on the organization’s