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What is capacity and why is it an important consideration in logistics management? What are the differences between design and effective capacity? If you were an operations manager, how would you bridge the gap between them or do you accept effective capacity as the standard? Be sure to defend your answers.

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7 SUPPLEMENT Capacity and Constraint Management PowerPoint presentation to accompany Heizer, Render, Munson Operations Management, Twelfth Edition Principles of Operations Management, Tenth Edition PowerPoint slides by Jeff Heyl Copyright © 2017 Pearson Education, Inc. S7 - 1 Outline ► ► ► ► Capacity Bottleneck Analysis and the Theory of Constraints Break-Even Analysis Reducing Risk with Incremental Changes Copyright © 2017 Pearson Education, Inc. S7 - 2 Outline - Continued ► ► Applying Expected Monetary Value (EMV) to Capacity Decisions Applying Investment Analysis to Strategy-Driven Investments Copyright © 2017 Pearson Education, Inc. S7 - 3 Learning Objectives When you complete this supplement you should be able to: S7.1 Define capacity S7.2 Determine design capacity, effective capacity, and utilization S7.3 Perform bottleneck analysis S7.4 Compute break-even Copyright © 2017 Pearson Education, Inc. S7 - 4 Learning Objectives When you complete this supplement you should be able to: S7.5 Determine the expected monetary value of a capacity decision S7.6 Compute net present value Copyright © 2017 Pearson Education, Inc. S7 - 5 Capacity ► The throughput, or the number of units a facility can hold, receive, store, or produce in a period of time ► Determines fixed costs ► Determines if demand will be satisfied ► Three time horizons Copyright © 2017 Pearson Education, Inc. S7 - 6 Planning Over a Time Horizon Figure S7.1 Options for Adjusting Capacity Time Horizon Long-range planning Intermediaterange planning (aggregate planning) Design new production processes Add (or sell existing) long-lead-time equipment Acquire or sell facilities Acquire competitors Subcontract Add or sell equipment Add or reduce shifts Short-range planning (scheduling) * Build or use inventory More or improved training Add or reduce personnel * Schedule jobs Schedule personnel Allocate machinery Modify capacity Use capacity * Difficult to adjust capacity as limited options exist Copyright © 2017 Pearson Education, Inc. S7 - 7 Design and Effective Capacity ► Design capacity is the maximum theoretical output of a system ► ► Normally expressed as a rate Effective capacity is the capacity a firm expects to achieve given current operating constraints ► Often lower than design capacity Copyright © 2017 Pearson Education, Inc. S7 - 8 Design and Effective Capacity TABLE S7.1 Capacity Measurements MEASURE DEFINITION EXAMPLE Ideal conditions exist during the time that the system is available Machines at Frito-Lay are designed to produce 1,000 bags of chips/hr., and the plant operates 16 hrs./day. Design Capacity = 1,000 bags/hr. × 16 hrs. = 16,000 bags/day Design capacity Copyright © 2017 Pearson Education, Inc. S7 - 9 Design and Effective Capacity TABLE S7.1 Capacity Measurements MEASURE DEFINITION Effective capacity Design capacity minus lost output because of planned resource unavailability (e.g., preventive maintenance, machine setups/changeovers, changes in product mix, scheduled breaks) Copyright © 2017 Pearson Education, Inc. EXAMPLE Frito-Lay loses 3 hours of output per day (= 0.5 hrs./day on preventive maintenance, 1 hr./day on employee breaks, and 1.5 hrs./day setting up machines for different products). Effective Capacity = 16,000 bags/day – (1,000 bags/hr.) (3 hrs./day) = 16,000 bags/day – 3,000 bags/day = 13,000 bags/day S7 - 10 Design and Effective Capacity TABLE S7.1 Capacity Measurements MEASURE DEFINITION Actual output Effective capacity minus lost output during unplanned resource idleness (e.g., absenteeism, machine breakdowns, unavailable parts, quality problems) Copyright © 2017 Pearson Education, Inc. EXAMPLE On average, machines at Frito-Lay are not running 1 hr./day due to late parts and machine breakdowns. Actual Output = 13,000 bags/day – (1,000 bags/hr.) (1 hr./day) = 13,000 bags/day – 1,000 bags/day = 12,000 bags/day S7 - 11 Utilization and Efficiency Utilization is the percent of design capacity actually achieved Utilization = Actual output/Design capacity Efficiency is the percent of effective capacity actually achieved Efficiency = Actual output/Effective capacity Copyright © 2017 Pearson Education, Inc. S7 - 12 Bakery Example Design Capacity Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, 3 - 8 hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls Copyright © 2017 Pearson Education, Inc. S7 - 13 Bakery Example Utilization Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, 3 - 8 hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls Utilization = 148,000/201,600 = 73.4% Copyright © 2017 Pearson Education, Inc. S7 - 14 Bakery Example Efficiency Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, 3 - 8 hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls Utilization = 148,000/201,600 = 73.4% Efficiency = 148,000/175,000 = 84.6% Copyright © 2017 Pearson Education, Inc. S7 - 15 Bakery Example Design Capacity Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 201,600 rolls per line Efficiency = 84.6% Expected output of new line = 130,000 rolls Design capacity = 201,600 x 2 = 403,200 rolls Copyright © 2017 Pearson Education, Inc. S7 - 16 Bakery Example Effective Capacity Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 201,600 rolls per line Efficiency = 84.6% Expected output of new line = 130,000 rolls Design capacity = 201,600 x 2 = 403,200 rolls Effective capacity = 175,000 x 2 = 350,000 rolls Copyright © 2017 Pearson Education, Inc. S7 - 17 Bakery Example Actual Output Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 201,600 rolls per line Efficiency = 84.6% Expected output of new line = 130,000 rolls Design capacity = 201,600 x 2 = 403,200 rolls Effective capacity = 175,000 x 2 = 350,000 rolls Actual output = 148,000 + 130,000 = 278,000 rolls Copyright © 2017 Pearson Education, Inc. S7 - 18 Bakery Example Utilization Efficiency Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 201,600 rolls per line Efficiency = 84.6% Expected output of new line = 130,000 rolls Design capacity = 201,600 x 2 = 403,200 rolls Effective capacity = 175,000 x 2 = 350,000 rolls Actual output = 148,000 + 130,000 = 278,000 rolls Utilization = 278,000/403,200 = 68.95% Efficiency = 278,000/350,000 = 79.43% Copyright © 2017 Pearson Education, Inc. S7 - 19 Capacity and Strategy ► Capacity decisions impact all 10 decisions of operations management as well as other functional areas of the organization ► Capacity decisions must be integrated into the organization’s mission and strategy Copyright © 2017 Pearson Education, Inc. S7 - 20 Capacity Considerations 1. Forecast demand accurately 2. Match technology increments and sales volume 3. Find the optimum operating size (volume) 4. Build for change Copyright © 2017 Pearson Education, Inc. S7 - 21 Economies and Diseconomies of Scale Average unit cost (sales per square foot) Figure S7.2 1,300 sq ft store Economies of scale 1,300 2,600 sq ft store Diseconomies of scale 2,600 Number of square feet in store Copyright © 2017 Pearson Education, Inc. 8,000 sq ft store 8,000 S7 - 22 Managing Demand ► ► ► Demand exceeds capacity ► Curtail demand by raising prices, scheduling longer lead times ► Long-term solution is to increase capacity Capacity exceeds demand ► Stimulate market ► Product changes Adjusting to seasonal demands ► Produce products with complementary demand patterns Copyright © 2017 Pearson Education, Inc. S7 - 23 Complementary Demand Patterns Figure S7.3 Sales in units 4,000 – Combining the two demand patterns reduces the variation 3,000 – Snowmobile motor sales 2,000 – 1,000 – Jet ski engine sales JFMAMJJASONDJFMAMJJASONDJ Time (months) Copyright © 2017 Pearson Education, Inc. S7 - 24 Tactics for Matching Capacity to Demand 1. Making staffing changes 2. Adjusting equipment ► Purchasing additional machinery ► Selling or leasing out existing equipment 3. Improving processes to increase throughput 4. Redesigning products to facilitate more throughput 5. Adding process flexibility to meet changing product preferences 6. Closing facilities Copyright © 2017 Pearson Education, Inc. S7 - 25 Service-Sector Demand and Capacity Management ► Demand management ► ► Appointment, reservations, FCFS rule Capacity management ► Full time, temporary, part-time staff Copyright © 2017 Pearson Education, Inc. S7 - 26 Bottleneck Analysis and the Theory of Constraints ► Each work area can have its own unique capacity ► Capacity analysis determines the throughput capacity of workstations in a system ► A bottleneck is a limiting factor or constraint ► ► A bottleneck has the lowest effective capacity in a system The time to produce a unit or a specified batch size is the process time Copyright © 2017 Pearson Education, Inc. S7 - 27 Bottleneck Analysis and the Theory of Constraints ► The bottleneck time is the time of the slowest workstation (the one that takes the longest) in a production system ► The throughput time is the time it takes a unit to go through production from start to end, with no waiting Figure S7.4 A B C 2 min/unit 4 min/unit 3 min/unit Copyright © 2017 Pearson Education, Inc. S7 - 28 Capacity Analysis ► Two identical sandwich lines ► Lines have two workers and three operations ► All completed sandwiches are wrapped First assembly line Bread Fill Toaster 15 sec/sandwich 20 sec/sandwich 40 sec/sandwich Wrap/ Deliver Bread Fill Toaster 37.5 sec/sandwich 15 sec/sandwich 20 sec/sandwich 40 sec/sandwich Order 30 sec/sandwich Second assembly line Copyright © 2017 Pearson Education, Inc. S7 - 29 Capacity Analysis Order 30 sec Bread Fill Toaster 15 sec 20 sec 40 sec Wrap Bread Fill Toaster 37.5 sec 15 sec 20 sec 40 sec ► The two lines are identical, so parallel processing can occur ► At 40 seconds, the toaster has the longest processing time and is the bottleneck for each line ► At 40 seconds for two sandwiches, the bottleneck time of the combined lines = 20 seconds ► At 37.5 seconds, wrapping and delivery is the bottleneck for the entire operation Copyright © 2017 Pearson Education, Inc. S7 - 30 Capacity Analysis Order 30 sec Bread Fill Toaster 15 sec 20 sec 40 sec Wrap Bread Fill Toaster 37.5 sec 15 sec 20 sec 40 sec ► Capacity per hour is 3,600 seconds/37.5 seconds/sandwich = 96 sandwiches per hour ► Throughput time is 30 + 15 + 20 + 40 + 37.5 = 142.5 seconds Copyright © 2017 Pearson Education, Inc. S7 - 31 Capacity Analysis ► Standard process for cleaning teeth ► Cleaning and examining X-rays can happen simultaneously Hygienist cleaning Check in Takes X-ray Develops X-ray 24 min/unit 2 min/unit 2 min/unit 4 min/unit X-ray exam Dentist Check out 8 min/unit 6 min/unit 5 min/unit Copyright © 2017 Pearson Education, Inc. S7 - 32 Capacity Analysis ► ► ► ► ► ► Hygienist cleaning Check in Takes X-ray Develops X-ray 24 min/unit 2 min/unit 2 min/unit 4 min/unit X-ray exam Dentist Check out 8 min/unit 6 min/unit 5 min/unit All possible paths must be compared Bottleneck is the hygienist at 24 minutes Hourly capacity is 60/24 = 2.5 patients X-ray exam path is 2 + 2 + 4 + 5 + 8 + 6 = 27 minutes Cleaning path is 2 + 2 + 4 + 24 + 8 + 6 = 46 minutes Longest path involves the hygienist cleaning the teeth, patient should complete in 46 minutes Copyright © 2017 Pearson Education, Inc. S7 - 33 Theory of Constraints ► Five-step process for recognizing and managing limitations Step 1: Identify the constraints Step 2: Develop a plan for overcoming the constraints Step 3: Focus resources on accomplishing Step 2 Step 4: Reduce the effects of constraints by offloading work or expanding capability Step 5: Once overcome, go back to Step 1 and find new constraints Copyright © 2017 Pearson Education, Inc. S7 - 34 Bottleneck Management 1. Release work orders to the system at the pace of set by the bottleneck’s capacity ► Drum, Buffer, Rope 2. Lost time at the bottleneck represents lost capacity for the whole system 3. Increasing the capacity of a nonbottleneck station is a mirage 4. Increasing the capacity of a bottleneck increases the capacity of the whole system Copyright © 2017 Pearson Education, Inc. S7 - 35 Break-Even Analysis ► ► ► Technique for evaluating process and equipment alternatives Objective is to find the point in dollars and units at which cost equals revenue Requires estimation of fixed costs, variable costs, and revenue Copyright © 2017 Pearson Education, Inc. S7 - 36 Break-Even Analysis ► Fixed costs are costs that continue even if no units are produced ► ► Depreciation, taxes, debt, mortgage payments Variable costs are costs that vary with the volume of units produced ► Labor, materials, portion of utilities ► Contribution is the difference between selling price and variable cost Copyright © 2017 Pearson Education, Inc. S7 - 37 Break-Even Analysis ► Revenue function begins at the origin and proceeds upward to the right, increasing by the selling price of each unit ► Where the revenue function crosses the total cost line is the break-even point Copyright © 2017 Pearson Education, Inc. S7 - 38 Break-Even Analysis – Total revenue line 900 – 800 – 700 – Cost in dollars Total cost line Break-even point Total cost = Total revenue 600 – 500 – Variable cost 400 – 300 – 200 – 100 – | Figure S7.5 0 Fixed cost | | | | | | | | | | | 100 200 300 400 500 600 700 800 900 1000 1100 Volume (units per period) Copyright © 2017 Pearson Education, Inc. S7 - 39 Break-Even Analysis Assumptions ► Costs and revenue are linear functions ► ► We actually know these costs ► ► Generally not the case in the real world Very difficult to verify Time value of money is often ignored Copyright © 2017 Pearson Education, Inc. S7 - 40 Break-Even Analysis BEPx = break-even point in units BEP$ = break-even point in dollars P = price per unit (after all discounts) x = number of units produced TR = total revenue = Px F = fixed costs V = variable cost per unit TC = total costs = F + Vx Break-even point occurs when TR = TC or Px = F + Vx Copyright © 2017 Pearson Education, Inc. BEPx = F P–V S7 - 41 Break-Even Analysis BEPx = break-even point in units BEP$ = break-even point in dollars P = price per unit (after all discounts) F BEP$ = BEPx P = P P–V F = (P – V)/P = x = number of units produced TR = total revenue = Px F = fixed costs V = variable cost per unit TC = total costs = F + Vx Profit = TR - TC = Px – (F + Vx) = Px – F – Vx = (P - V)x – F F 1 – V/P Copyright © 2017 Pearson Education, Inc. S7 - 42 Break-Even Example Fixed costs = $10,000 Direct labor = $1.50/unit BEP$ = Material = $.75/unit Selling price = $4.00 per unit $10,000 F = 1 – [(1.50 + .75)/(4.00)] 1 – (V/P) $10,000 = = $22,857.14 .4375 Copyright © 2017 Pearson Education, Inc. S7 - 43 Break-Even Example Fixed costs = $10,000 Direct labor = $1.50/unit BEP$ = Material = $.75/unit Selling price = $4.00 per unit $10,000 F = 1 – [(1.50 + .75)/(4.00)] 1 – (V/P) $10,000 = = $22,857.14 .4375 $10,000 F BEPx = = = 5,714 4.00 – (1.50 + .75) P–V Copyright © 2017 Pearson Education, Inc. S7 - 44 Break-Even Example 50,000 – Revenue Dollars 40,000 – Break-even point 30,000 – Total costs 20,000 – Fixed costs 10,000 – | | | | | | 0 2,000 4,000 6,000 8,000 10,000 Units Copyright © 2017 Pearson Education, Inc. S7 - 45 Break-Even Example Multiproduct Case Break-even F point in dollars = éæ V ö ù (BEP$) åêêç1- Pi ÷ ´ Wi úú ëè û i ø ( ) where = variable cost per unit = price per unit = fixed costs = percent each product is of total dollar sales expressed as a decimal i = each product V P F W Copyright © 2017 Pearson Education, Inc. S7 - 46 Multiproduct Example Fixed costs = $3,000 per month ANNUAL FORECASTED SALES UNITS PRICE COST Sandwich 9,000 $5.00 $3.00 Drink 9,000 1.50 .50 Baked potato 7,000 2.00 1.00 ITEM 1 2 3 4 ITEM (i) ANNUAL FORECASTED SALES UNITS SELLING PRICE (Pi) VARIABLE COST (Vi) 5 6 7 8 9 (Vi/Pi) 1 - (Vi/Pi) ANNUAL FORECASTED SALES $ % OF SALES (Wi) WEIGHTED CONTRIBUTION (COL 6 X COL 8) Sandwich 9,000 $5.00 $3.00 .60 .40 $45,000 .621 .248 Drinks 9,000 1.50 0.50 .33 .67 13,500 .186 .125 2.00 1.00 .50 .50 14,000 .193 .097 $72,500 1.000 .470 Baked potato 7,000 Copyright © 2017 Pearson Education, Inc. S7 - 47 F MultiproductBEP Example = éæ ö $ Fixed costs = $3,000 per month ANNUAL FORECASTED SALES UNITS ITEM Sandwich 9,000 Drink 9,000 Baked potato 7,000 1 2 3 4 ITEM (i) ANNUAL FORECASTED SALES UNITS SELLING PRICE (P) VARIABLE COST (V) ù Vi åêêç1- P ÷ ´ Wi úú ëè û i ø ( ) PRICE x 12 COST $3,000 = $5.00 = $76,596 $3.00 .47 1.50 .50 $76,596 Daily 2.00 1.00 sales = 312 days = $245.50 5 6 7 (V/P) 1 - (V/P) ANNUAL FORECASTED SALES $ 8 9 % OF SALES WEIGHTED CONTRIBUTION (COL 5 X COL 7) Sandwich 9,000 $5.00 $3.00 .60 .40 $45,000 .621 .248 Drinks 9,000 1.50 0.50 .33 .67 13,500 .186 .125 2.00 1.00 .50 .50 14,000 .193 .097 $72,500 1.000 .470 Baked potato 7,000 Copyright © 2017 Pearson Education, Inc. S7 - 48 Figure S7.6 Reducing Risk with Incremental Changes (b) Leading demand with a one-step expansion Expected demand Demand (c) Lagging demand with incremental expansion New capacity Copyright © 2017 Pearson Education, Inc. Expected demand Demand New capacity New capacity Expected demand (d) Attempts to have an average capacity with incremental expansion Demand Demand (a) Leading demand with incremental expansion New capacity Expected demand S7 - 49 Reducing Risk with Incremental Changes (a) Leading demand with incremental expansion Figure S7.6 Demand New capacity Expected demand 1 Copyright © 2017 Pearson Education, Inc. 2 3 Time (years) S7 - 50 Reducing Risk with Incremental Changes (b) Leading demand with a one-step expansion Figure S7.6 Demand New capacity Expected demand 1 Copyright © 2017 Pearson Education, Inc. 2 3 Time (years) S7 - 51 Reducing Risk with Incremental Changes (c) Lagging demand with incremental expansion Figure S7.6 New capacity Demand Expected demand 1 Copyright © 2017 Pearson Education, Inc. 2 3 Time (years) S7 - 52 Reducing Risk with Incremental Changes (d) Attempts to have an average capacity with incremental expansion Figure S7.6 New capacity Demand Expected demand 1 Copyright © 2017 Pearson Education, Inc. 2 Time (years) 3 S7 - 53 Applying Expected Monetary Value (EMV) and Capacity Decisions ► ► Determine states of nature ► Future demand ► Market favorability Assign probability values to states of nature to determine expected value Copyright © 2017 Pearson Education, Inc. S7 - 54 EMV Applied to Capacity Decision ▶Southern Hospital Supplies capacity expansion EMV (large plant) = (.4)($100,000) + (.6)(–$90,000) = –$14,000 EMV (medium plant) = (.4)($60,000) + (.6)(–$10,000) = +$18,000 EMV (small plant) = (.4)($40,000) + (.6)(–$5,000) = +$13,000 EMV (do nothing) = $0 Copyright © 2017 Pearson Education, Inc. S7 - 55 Strategy-Driven Investments ► Operations managers may have to decide among various financial options ► Analyzing capacity alternatives should include capital investment, variable cost, cash flows, and net present value Copyright © 2017 Pearson Education, Inc. S7 - 56 Net Present Value (NPV) In general: F = P(1 + i)N where F P i N = future value = present value = interest rate = number of years Solving for P: F P= (1 + i)N Copyright © 2017 Pearson Education, Inc. S7 - 57 Net Present Value (NPV) In general: F = P(1 + i)N where F P i N = future value While = present value this works fine, is cumbersome for = interest rate = number oflarger years values of N it Solving for P: F P= (1 + i)N Copyright © 2017 Pearson Education, Inc. S7 - 58 NPV Using Factors F P= = FX N (1 + i) where X = a factor from Table S7.2 defined as = 1/(1 + i)N and F = future value TABLE S7.2 Present Value of $1 YEAR 6% 8% 10% 12% 14% 1 .943 .926 .909 .893 .877 2 .890 .857 .826 .797 .769 3 .840 .794 .751 .712 .675 4 .792 .735 .683 .636 .592 5 .747 .681 .621 .567 .519 Copyright © 2017 Pearson Education, Inc. Portion of Table S7.2 S7 - 59 Present Value of an Annuity An annuity is an investment that generates uniform equal payments S = RX where X = factor from Table S7.3 S = present value of a series of uniform annual receipts R = receipts that are received every year of the life of the investment Copyright © 2017 Pearson Education, Inc. S7 - 60 Present Value of an Annuity TABLE S7.3 Present Value of and Annuity of $1 YEAR 6% 8% 10% 12% 14% 1 .943 .926 .909 .893 .877 2 1.833 1.783 1.736 1.690 1.647 3 2.673 2.577 2.487 2.402 2.322 4 3.465 3.312 3.170 3.037 2.914 5 4.212 3.993 3.791 3.605 3.433 Portion of Table S7.3 Copyright © 2017 Pearson Education, Inc. S7 - 61 Present Value of an Annuity ▶River Road Medical Clinic equipment investment $7,000 in receipts per year for 5 years Interest rate = 6% From Table S7.3 X = 4.212 S = RX S = $7,000(4.212) = $29,484 Copyright © 2017 Pearson Education, Inc. S7 - 62 Limitations 1. Investments with the same NPV may have different projected lives and salvage values 2. Investments with the same NPV may have different cash flows 3. Assumes we know future interest rates 4. Payments are not always made at the end of a period Copyright © 2017 Pearson Education, Inc. S7 - 63 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 © 2017 Pearson Education, Inc. S7 - 64
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