1588OperationsStrategyEMPClass4A

Operations Strategy/Ris k Mitigation & Operational Hedging

5/20/15

Operations Strategy (class 4) Capacity strategy and Operational Hedging ! ! !

Capacity strategy Analyze the value of flexibility as operational hedge Explain the concept of risk management "

Case: • Seagate Technology

Common Categories of Flexibility “Flexibility measures the ability to adapt or change.” (Upton)

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The ability to produce multiple products. This may refer to “simultaneous” production, e.g., different models produced at the same time on the same assembly line, or to the ability to easily switch production from one product to another. The ability to increase or decrease the production rate of a particular product (without a significant increase in cost or decrease in performance)

The ability to introduce new products into the operating system (sourcing, production and distribution)

Operations Strategy/Risk Management & Operational Hedging

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Operations Strategy/Risk Mitigation & Operational Hedging

5/20/15

Obstacles to achieving flexibility !

! !

Flexibility costs more but its benefits are difficult to measure, value and convey Competition and customer perception may preclude using flexibility It is often unclear which features of a process must be changed to enhance its flexibility: – Flexibility is driven more by people and management than by process structure (policies/procedures/culture)


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Assembly and Test Operations Contribution margins

!"##$%" '((#)*+,

Cheetah: $400

dedicated resource

2#($ -%..%/01% '((#)*+, dedicated resource

Barracuda: $300 shared resource

Proposed capacity plan Cheetah Assembly: 300K units @ $30K per 1K units Barracuda Assembly: 300K units @ $20K per 1K units Test: 600K Units @ $80K per 1k units CapEx = $40M (fixed)+9M (C Assy) + 6M (B Assy) + $48M (Test) = $103M


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Valuation of network capacity under uncertainty Demand D

Invest in capacity K

Market has demand D

Sales

Mismatch units and cost

Operating profit

Profit deviation from mean

Pessimistic 25%: (150, 350)

(150, 300)

(0, -50) = - $15M

$150M

-$41.25M

M os t l ik el y 5 0% : ( 30 0, 3 00 )

( 30 0, 3 00 )

( 0, 0 ) = $ 0

$ 21 0M

$ 18 .7 5M

Optimistic 25%: (450, 250)

(300, 250)

(-150,0) = -$60M

(300, 300, 600) CapEx = $103M

-$18.75M

!

$195M

$191.25M

$3.75M

$24.59M = sqrt(expected squared deviation)

E(NPV) = $191.25M - $103M = $88.25M – E(profit variability) = st.dev of operating profits = $24.59M


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Benchmark: Full Information ! ! !

“What if” capacity is built once demand is known? Capacity plan is perfect match to demand Operating profit = $300 x 300k + $400 x 300k = $210 M – NPV = $210M - $103M = $107M – ROI = 107/103 = 104%

!

What is the impact of uncertainty?


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5/20/15

Determining the Best Capacity 9*(#.:# '/$0%+ ;#)%71

-03+1 !%4%/3$,

5.610/# %71 8#++

Based on forecast

Outcome A: Demand = Capacity No Demand/Capacity Mismatch. Sales = Demand

Based on actual demand

Outcome B: Demand < Capacity Demand/Capacity Mismatch. Sales = Demand BUT You wish you had invested in less capacity!

Outcome C: Demand > Capacity Demand/Capacity Mismatch. Sales = Capacity BUT You wish you had invested in more capacity!

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<#)371( )# 6= 071#.%># /6($

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Simplification: Reduce variety or complexity: Single Product or Dedicated Test Imagine Seagate only sold the Cheetah drive Capacity cost = 30K per 1K units

Capacity cost = 80K per 1K units

!"##$%" '((#)*+,

2#($

Contribution margin $400

Would set Assembly Capacity = Test Capacity Two-resource problem equivalent to a one-resource problem Capacity cost = 30K +80K=110K per 1K units Contribution margin $400

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Determine optimal capacity, K* Set Assembly and Test Capacities equal to this K* Operations Strategy/Risk Management & Operational Hedging

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5/20/15

Determining the Best Capacity for a single resource: Marginal Analysis for 1D Cheetah Problem Demand Distribution

Capacity cost = $110 per unit

Demand Contribution margin $400

<#(60./#

!

Probability

Cumulative Probability

150

0.25

0.25

300

0.50

0.75

450

0.25

1.0

Should we invest one more unit above 150? – Marginal value = .5*$400 + .25*$400 = .75*$400 = Pr(shortage)*$400 – Marginal cost = $110

!

Add to investment MV > MC = as long as service level < = 1D newsvendor model

# #

Critical Fractile

290

c

u

= c

u

+ co

=

290 + 110

290 =

400

=

0.725

K* = 300 so set capacities of Cheetah Assembly and Test both at 300 With single product, optimal safety capacity = 0! •

Same for single Baracuda product.


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The real problem is more complicated

Necessity and Value of Network Analysis Contribution margins Cheetah: $400

!"##$%" '((#)*+, dedicated resource

2#($

-%..%/01% '((#)*+, dedicated resource

Barracuda: $300 shared resource

We need an “holistic” approach because Test is a shared resource #

Network analysis is needed to capture: – (shifting) bottlenecks in a multi-resource network – correlated demands – operational flexibility (substitution, rev. max) and hedging


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Capacity for a Multi-Resource Network !

!

If we have multiple resources, we have multiple constraints and capacity is multidimensional! A given capacity portfolio (vector), constrains later production decisions: Capacity = K C !"##$%" '((#)*+,

Capacity = K T

Cheetah Production xC < K C

dedicated resource

Barracuda Production xB < K B

2#($

Capacity = K B

Cheetah + Barracuda Production xB + xC < K T

shared resource

-%..%/01% '((#)*+, dedicated resource

Don’t forget: we also don’t want to produce more than demand in any given scenario If Test is the bottleneck, how should we allocate its capacity between Cheetah and Barracuda? Operations Strategy/Risk Management & Operational Hedging

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Proposed Capacity Plan -%..%/01% 37 DCCC 073$( KCC

JCC

2#($ !%4%/3$, ICC

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HCC

GCC

5#((3)3(U/ PCLEHT

VW4#/$#1 PCLHT

-%..%/01% '((,L FCC !%4%/3$,

94U)3(U/ PCLEHT

ECC

DCC

X#%(3*+# 5.610/U67

C DCC

ECC

FCC

GCC

!"##$%" '((,L !%4%/3$,


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HCC

ICC

JCC

KCC

!"##$%" 37 DCCC 073$(

2#($ !%4%/3$,

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5/20/15

Newsvendor Networks: Marginal Analysis for Barracuda Assembly -%..%/01% 37 DCCC 073$(

Y7/.#%(# !%4%/3$, =.6) FCC $6 FCD 8#++ D )6.# -%..%/01% 37 5#((3)3(U/ 8/#7%.36 8#++ C )6.# 37 6$"#. (/#7%.36( AZ [ CLEH\FCC [ JH A! [ EC A%.>37%+ 4.6]$ [ JHÊC[HH 46(3U:#

KCC

JCC

2#($ !%4%/3$, ICC

Y7/.#%(# !%4%/3$, =.6) FG_ $6 FHC 8#++ D )6.# -%..%/01% 37 5#((3)3(U/ 8/#7%.36 8#++ C )6.# 37 6$"#. (/#7%.36( AZ [ CLEH\FCC [ JH A! [ EC A%.>37%+ 4.6]$ [ JHÊC[HH 46(3U:#

HCC

GCC

5#((3)3(U/ PCLEHT

VW4#/$#1 PCLHT

-%..%/01% '((,L FCC !%4%/3$,

94U)3(U/ PCLEHT

Y7/.#%(# !%4%/3$, =.6) FHC $6 FHD 8#++ C )6.# -%..%/01% 37 %++ (/#7%.36( AZ [ C A! [ EC A%.>37%+ 4.6]$ [ ÊC 7#>%U:#

ECC

DCC

X#%(3*+# 5.610/U67

C DCC

ECC

FCC

GCC

HCC

!"##$%" '((,L !%4%/3$,


ICC

JCC

KCC

!"##$%" 37 DCCC 073$(

2#($ !%4%/3$,

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Newsvendor network analysis: Marginal analysis of value of capacity in a network !

!

Adding one unit of Barracuda final assembly capacity increases expected operating profit by 25%*$300 = $75 > CapEx of $20, as long as K b < 350. Marginal profit is negative beyond 350. Similarly, adding one unit of Cheetah final assembly capacity has expected marginal value of 25%*$400 - $30 = $70, as long as K c < 350. But if we increase K c beyond 350: –

–

test is constraining us and the marginal value of an additional cheetah capacity unit is that we can make one more Cheetah drive but one less Barra drive (due to test constraint). The expected marginal value is 25%*($400-$300) = $25 < CapEx of $30, which is suboptimal . Increasing both cheetah and test capacity is also suboptimal: marginal value = 25%*$400 = $100 < $30+$80

#

Sizing of a capacity portfolio starting guideline:

#

Maximize value using marginal analysis of overage versus underage!


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P(resource i is bottleneck) ! Marginal cost of capacity i Marginal return of capacity i

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5/20/15

Operational Capacity Hedge :

K = (350, 350, 600) !

Barracuda [1000 units] 700

What is utilization of the three capacities? – –

600

500

Pessimistic 25%

Expected 50%

400

Optimistic 25%

Original capacity proposal

100

0 0

100

200

300

400

In what sense is this capacity plan an “operational hedge?” 1. Upstream excess capacity illustrates one of the operational hedging strategies: capacity reserves mitigate demand risk 2. Less excess capacity is needed in testing, which illustrates another OH strategy: pooling mitigates demand risk 3. To exercise the switching real option embedded in test, we need upstream capacity imbalance !

300

200

Safety capacity in each scenario More upstream capacity than downstream > patently unbalanced capacity portfolio

500

600

700

Cheetah [1000 units]


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Seagate Technology :

Excel formulation and Mean-Variances Production Margin per thousand (in unit of $1000) Cheetah $ 400 Barracuda $ 300 CapEx per thousand (in unit of $1000) Cheetah $ Barracuda $ Testing $ Fixed Cost $

30 20 80 40,000

Demand Probability (In unit of thousand) Cheetah Barracuda

Scenario A 0.25

Scenario B 0.5

150 350

300 300

450 250

Contingent Production Cheetah Barracuda Sum(Cheetah+Barra) Op. Profit

(in unit of thousand) 150 350 500 $ 165,000

300 300 600 210,000

350 250 600 215,000

Probability Expected Op. Profit Std. Dev. of op. profit Investment (CapEx) Net Incom ROI

0.25 $ $ $ $


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$

0.5

0.25

Expected Value $ 88,250 $ 93,500 $ 94,500

ROI 86% 80% 90%

Capacity (in thousands) 350 350 600

200,000 20,310 105,500 94,500 90%

Summary comparison 3 capacity portfolios

Coordinated: Full Insurance: E(NPV)-Optimal

$

Scenario C 0.25

(300, 300, 600) (450, 350, 700) (350, 350, 600)

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$ $ $

Std dev Mismatch cost 24,590 $ 18,750 31,820 $ 13,500 20,310 $ 12,500 © Allon

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5/20/15

Mean-Variance Analysis of Operational Hedging:

Example via Capacity Portfolio Imbalance in Seagate 10

8

NPVOptimal K *

6

Initial Sales-plan driven K

Full insurance K

!

For whom is K * optimal?

!

Reduce risk: –

What is the optimal riskreducing capacity portfolio?

–

When is “operational hedging” most effective?

4

4

0

Expected Value ($)

-4

-6

Standard deviation of value ($) Operations Strategy/Risk Management & Operational Hedging

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Optimal Capacity Portfolio Investment : Implementation Challenges !

The optimally installed capacity will never be all used simultaneously

!

The current sales-plan driven capacity planning practice will never lead to the optimal capacity vector

!

Which organizational changes are needed so the optimal capacity vector is chosen? Incentives: mktg-sales v. finance-ops Demand & sales planning & forecasting – Integration and coordination (ERP) – –


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5/20/15

Demand, Production, and Capacity Planning: Pitfalls of Sales-Plan driven investments Still happens: - Tech - Medical devices - Automotive

Resource Planning & Capital Budgeting

Prod &Inv Planning

Informal Feedback

Sales Plan

Demand forecast

Master Production Scheduling

MPS

CRP CRP = How much capacity is required to produce MPS?

MRP

Mat INV

CRP &MRP Records

Shop-floor & Vendor Systems


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Learning Points from Seagate Case: Operational hedging through capacity portfolios !

Capacity portfolio investment is simultaneous investment in multiple resource types. The basic question is to determine the capacity for each resource type.

!

Don’t do sales-plan driven capacity planning – i.e., don’t build capacity for one single demand scenario

!

Explicitly capture forecast uncertainty – Agree on a demand forecast, i.e., set of scenarios, capturing correlation – Build capacity based on the forecast. – Use newsvendor logic to balance underage and overage consequences.

!

Use a portfolio approach – Best capacity for resource X depends on capacity choice for resource Y. – Network model captures moving bottlenecks and operational flexibility


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Learning Points from Seagate Case: Operational hedging through capacity portfolios !

Tailor operational hedges to specific risks: 1. Adding different amounts of reserves (“insurance” or safety capacity) to different resources. 2. Flexible resources pool demand risk and need less safety capacity. This also enables them to exercise their real switching option to maximize profits.

!

The above mitigate operational risk: they reduce the expected mismatch cost and thus add value.

!

They also mitigate financial risk: profit variability is reduced.


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GM Chevy !

2 assembly plants: – Arlington, TX: 75,000 units/yr – Fairfax, KS: 200,000 units/yr

!

2 stamping plants: – Pontiac, MI – Fairfax, KS [contiguous, same location as assembly]

!

A die-set (for stamping hood, roof, etc) costs about $30 to $50M

!

Should we have one die-set in each plant, or 1 set in Fairfax (and ship to Arlington)?


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Approach ! !

Value proposed capacity plan Consider other simple capacity investments – Give bounds on value – Build intuition

! !

Towards an value-maximizing investment Strengths and weaknesses


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1588OperationsStrategyEMPClass4A

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