S7 - 1© 2011 Pearson Education, Inc. publishing as Prentice Hall Process Strategies ( process, repetitive, product) The objective of the process strategy is to build a production process that has capacity to meet customer requirements (quality & quantity)customer requirements (quality & quantity) product specifications (quality & cost)product specifications (quality & cost) within finance (fixed costs = capital invested)within finance (fixed costs = capital invested) other managerial constraints (flexibility)other managerial constraints (flexibility)

S7 - 2 Process Strategy & Capacity Process strategy chosen has to Meet consumer demand (quality & quantity expectations) –low/medium/high ? –constant or changing ? –predictable or unpredictable ? Meet Business requirements (average cost per unit) –efficient use of existing capacity –ability to change output (up or down) if needed © 2011 Pearson Education, Inc. publishing as Prentice Hall

S7 - 3© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity The number of units a facility can produce, hold, receive, or store, in a period of time Has a big effect on fixed costs (& therefore break even ) Determines if demand will be satisfied Three time horizons Long (>1yr) Medium (3 -18 months) Short (<3 months)

S7 - 4© 2011 Pearson Education, Inc. publishing as Prentice Hall Planning Over a Time Horizon (relates to forecasting accuracy) Figure S7.1 To increase capacityTo use excess capacity Medium (3-18 month) SubcontractAdd personnel Add equipmentBuild or use inventory Add shifts Short (< 3 month) Schedule jobs Schedule personnel Allocate machinery * Long (> 12 month) Add facilities Add long lead time equipment * * Difficult to adjust capacity as limited options exist Options for Adjusting Capacity

S7 - 5© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity Definitions Design capacity (measured as utilisation) is the maximum theoretical output of a system normally expressed as a rate (output/time) Effective capacity (measured as efficiency) is the actual output expects to achieve given current operating constraints (e.g. downtime for maintenance) Often lower than design capacity

S7 - 6© 2011 Pearson Education, Inc. publishing as Prentice Hall Utilization and Efficiency Utilization is the percent of design capacity achieved Efficiency is the percent of effective capacity achieved Utilization = Actual output/Design capacity (as %) Efficiency = Actual output/Effective capacity (as %)

S7 - 7© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (design capacity & utilisation) Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls

S7 - 8© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (design capacity & utilisation) Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls

S7 - 9© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (design capacity & utilisation) Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls Utilization = 148,000/201,600 = 73.4%

S7 - 10© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (design capacity & utilisation) Actual production last week = 148,000 rolls Effective capacity = 175,000 rolls Design capacity = 1,200 rolls per hour Bakery operates 7 days/week, hour shifts Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls Utilization = 148,000/201,600 = 73.4%

S7 - 11© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (effective capacity & 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, 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%

S7 - 12© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (effective capacity & 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, 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%

S7 - 13© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (effective capacity & 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, hour shifts Efficiency = 84.6% Efficiency of new line = 75% Expected Output = (Effective Capacity)(Efficiency) = (175,000)(.75) = 131,250 rolls

S7 - 14© 2011 Pearson Education, Inc. publishing as Prentice Hall Bakery Example Bakery Example (effective capacity & 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, hour shifts Efficiency = 84.6% Efficiency of new line = 75% Expected Output = (Effective Capacity)(Efficiency) = (175,000)(.75) = 131,250 rolls

S7 - 15© 2011 Pearson Education, Inc. publishing as Prentice Hall

S7 - 16© 2011 Pearson Education, Inc. publishing as Prentice Hall 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 organizations mission and strategy

S7 - 17© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity Considerations 1.Forecast demand accurately (marketing department do this) 2.Understand the technology and capacity increments 3.Find the optimum operating level (volume) 4.Build for change (flexibility is desirable)

S7 - 18© 2011 Pearson Education, Inc. publishing as Prentice Hall Economies and Diseconomies of Scale Economies of scale Diseconomies of scale 25 - room roadside motel 50 - room roadside motel 75 - room roadside motel Number of Rooms Average unit cost (dollars per room per night) Figure S7.2

S7 - 19© 2011 Pearson Education, Inc. publishing as Prentice Hall Managing Demand Demand exceeds capacity reduce demand by raising prices, scheduling longer lead time Long term solution is to increase capacity Capacity exceeds demand Stimulate market (advertising, price cuts, etc) Product changes (diversify, make to stock) Adjusting to seasonal demands Produce products with opposite demand patterns (surf wear / snow wear)

S7 - 20© 2011 Pearson Education, Inc. publishing as Prentice Hall Complementary Demand Patterns 4,000 – 3,000 – 2,000 – 1,000 – J F M A M J J A S O N D J F M A M J J A S O N D J Sales in units Time (months) Combining both demand patterns reduces the variation Snowmobile motor sales Jet ski engine sales Figure S7.3

S7 - 21© 2011 Pearson Education, Inc. publishing as Prentice Hall Tactics for Matching Capacity to Demand 1.Making staffing changes (extra shifts(+), reduce hours (-)) 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(-)

S7 - 22© 2011 Pearson Education, Inc. publishing as Prentice Hall Demand & Capacity Management in Services Demand management Appointment, reservations, FCFS rule Capacity management Staffing levels & scheduling full-time part-time Temporary/casual

S7 - 23© 2011 Pearson Education, Inc. publishing as Prentice Hall

S7 - 24© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity Analysis & Theory of Constraints Each workstation (within a system) can have its own unique capacity Some work areas will be faster ( higher output) and some will be slower (lower output) than others. Capacity analysis measures the output capacity of workstations in a system A bottleneck is the workstation with the lowest effective capacity in the system and it will be the limiting factor or constraint for that system. (Chains as strong as weakest link / a column marches as fast as the slowest marcher)

S7 - 25© 2011 Pearson Education, Inc. publishing as Prentice Hall Process Times for Stations, Systems, and Cycles Process time of a station Process time of a station time to produce one unit at that single workstation inverse of capacity for that workstation rocess time of a system Process time of a system the longest process time in the system … the bottleneck Process cycle time Process cycle time time a product takes to go through the production process with no waiting These two might be quite different!

S7 - 26© 2011 Pearson Education, Inc. publishing as Prentice Hall Process Times for Stations, Systems, and Cycles System process time Is the process time of the bottleneck after dividing by the number of parallel operations System capacity is the inverse of the system process time Process cycle time is the total time through the longest path in the system

S7 - 27© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity Analysis Two identical sandwich lines Lines have two workers and three operations All completed sandwiches are wrapped Wrap 37.5 sec/sandwich Order 30 sec/sandwich BreadFillToast 15 sec/sandwich 20 sec/sandwich 40 sec/sandwich BreadFillToast 15 sec/sandwich20 sec/sandwich40 sec/sandwich

S7 - 28© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity Analysis Wrap 37.5 sec Order 30 sec BreadFillToast 15 sec 20 sec 40 sec BreadFillToast 15 sec20 sec40 sec Toast work station has the longest processing time – 40 seconds The two lines each deliver a sandwich every 40 seconds so the process time of the combined lines is 40/2 = 20 seconds At 37.5 seconds, wrapping and delivery has the longest processing time and is the bottleneck Capacity per hour is 3,600 seconds/37.5 seconds/sandwich = 96 sandwiches per hour Process cycle time is = seconds

S7 - 29© 2011 Pearson Education, Inc. publishing as Prentice Hall Capacity Analysis All possible paths must be compared Cleaning path is = 46 minutes X-ray exam path is = 27 minutes Longest path involves the hygienist cleaning the teeth Bottleneck is the hygienist at 24 minutes Hourly capacity is 60/24 = 2.5 patients Patient should be complete in 46 minutes Check out 6 min/unit Check in 2 min/unit Develops X-ray 4 min/unit 8 min/unit Dentist Takes X-ray 2 min/unit 5 min/unit X-ray exam Cleaning 24 min/unit

S7 - 30© 2011 Pearson Education, Inc. publishing as Prentice Hall Theory of Constraints Five-step process for recognizing and managing constraints/ bottlenecks Step 1: Step 1:Identify the constraint Step 2: Step 2:Develop a plan for overcoming the constraints Step 3: Step 3:Focus resources on accomplishing Step 2 Step 4: Step 4:Reduce the effects of constraints by offloading work or expanding capability Step 5: Step 5:Once overcome, go back to Step 1 and find new constraints

S7 - 31© 2011 Pearson Education, Inc. publishing as Prentice Hall Bottleneck Management 1.Release work orders to the system at the pace of set by the bottleneck 2.Inspect products before the bottleneck 3.Lost time at the bottleneck represents lost time for the whole system 4.Increasing the capacity of a non-bottleneck station is a mirage 5.Increasing the capacity of a bottleneck increases the capacity of the whole system

S7 - 32© 2011 Pearson Education, Inc. publishing as Prentice Hall

S7 - 33© 2011 Pearson Education, Inc. publishing as Prentice Hall Decision making about Capacity Changes (a)Leading demand with incremental expansion Demand Expected demand New capacity (c)Attempts to have an average capacity with incremental expansion Demand New capacity Expected demand (b)Capacity lags demand with incremental expansion Demand New capacity Expected demand Figure S7.6

S7 - 34© 2011 Pearson Education, Inc. publishing as Prentice Hall Expected Monetary Value (EMV) and Capacity Decisions Forecast probable Future demand Market favorability Analyse using decision trees and expected value Hospital supply company Four alternatives

S Decision Trees (used for comparing options) Square = decision being considered. Circle = probability of some state of nature (e.g. market response being favourable 0.7 being unfavourable 0.3) © 2011 Pearson Education, Inc. publishing as Prentice Hall

S7 - 36© 2011 Pearson Education, Inc. publishing as Prentice Hall Expected Monetary Value (EMV) and Capacity Decisions -$90,000 Market unfavorable (.6) Market favorable (.4) $100,000 Large plant Market favorable (.4) Market unfavorable (.6) $60,000 -$10,000 Medium plant Market favorable (.4) Market unfavorable (.6) $40,000 -$5,000 Small plant $0 Do nothing

S7 - 37© 2011 Pearson Education, Inc. publishing as Prentice Hall Expected Monetary Value (EMV) and Capacity Decisions -$90,000 Market unfavorable (.6) Market favorable (.4) $100,000 Large plant Market favorable (.4) Market unfavorable (.6) $60,000 -$10,000 Medium plant Market favorable (.4) Market unfavorable (.6) $40,000 -$5,000 Small plant $0 Do nothing EMV =(.4)($100,000) + (.6)(-$90,000) Large Plant EMV = -$14,000

S7 - 38© 2011 Pearson Education, Inc. publishing as Prentice Hall Expected Monetary Value (EMV) and Capacity Decisions -$90,000 Market unfavorable (.6) Market favorable (.4) $100,000 Large plant Market favorable (.4) Market unfavorable (.6) $60,000 -$10,000 Medium plant Market favorable (.4) Market unfavorable (.6) $40,000 -$5,000 Small plant $0 Do nothing -$14,000 $13,000$18,000

S7 - 39© 2011 Pearson Education, Inc. publishing as Prentice Hall Investment Appraisal for Capacity Investment Operations may be responsible for investment appraisal of capacity decisions. Methods include Payback period – comparing cash returns from investment to cash invested until balance is zero. (No discounting used – but useful if debt used to invest) Net Present Value – finding the cash value of the investment (in todays dollars) before investing by applying discount rate to future earnings.

S Payback period New machine costs $600,000. Income from machine = $255,000 each year © 2011 Pearson Education, Inc. publishing as Prentice Hall investmentreturnsNet position Year 0($600,000)(600,000) Year 1$255,000(345,000) Year 2$255,000(90,000) Year 3$255,000$165,000 ( So payback is sometime in year 3.) To find the month : =(cash needed / annual cash flow) *12 = (90,000/255,000)*12 = 0.352*12 = 4.2 (first week of April) Payback period = 2 yrs 4.2 months

S7 - 41© 2011 Pearson Education, Inc. publishing as Prentice Hall Net Present Value (NPV) NPV discounts the future value of cash due to uncertainty loss of earnings compared to having it now. Key decision is the discount rate to use to reduce the value of future cash flows. High rate – future is heavily discounted / lower value Low rate – future is lightly discounted / higher value Usual choice is the interest rate on bank deposits. If i= 10% then investing $ today is worth $ in one year – so - $ is worth more than $ received in one year from now.

S7 - 42© 2011 Pearson Education, Inc. publishing as Prentice Hall Net Present Value (NPV) whereF= future value P= present value i= interest rate N= number of years P = F (1 + i) N F = P(1 + i) N In general: Solving for P:

S7 - 43© 2011 Pearson Education, Inc. publishing as Prentice Hall NPV Using Factors P = = FX F (1 + i) N whereX=a factor from Table S7.1 defined as = 1/(1 + i) N and F = future value Portion of Table S7.1 Year6%8%10%12%14%

S7 - 44© 2011 Pearson Education, Inc. publishing as Prentice Hall 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