chapter 2. the process view

Chapter 2. The Process View

Slide 1

Some concepts fundamental to process analysis

§ It is not sufficient for a company to create great products and services;

§ The company also must design and improve its business processes that supply its products and services!

§ Gantt Chart (Figures 2.2 and 2.3 on page 12)

(cf.) The chart is named after Henry Gantt (1861–1919), who designed his chart around the years 1910–1915.

Slide 2

Examples of processes

§ Processes can involve both goods and services.

§ Processes can have multiple inputs and/or multiple outputs.

Factorywood

metalguitars

Universitystudents alumni

Distribution center

bulk items small parcels

Calculatecredit risk

mortgage applications

approved loans

rejected loans

Slide 3

Defining a process’ scope

§ A process is a set of activities that accepts inputs and produces outputs.

§ A process can be defined at an aggregate level:

§ A process can be defined at a micro level, with multiple sub-processes:

Calculatecredit risk


approved loans

rejected loans


approved loans

rejected loans

Collect datafrom client

Evaluateloan metrics

Underwritingdecision

Communicate decision to sales

Slide 4

Defining a process’ flow unit

§ The flow unit is what is tracked through the process and generally defines the process output of interest.

Universitystudents A personalumni

Processingplant

milk Lbs of milk powdermilk powder

Blooddonationcenter

people blood Pints of type AB blood

Processes Flow unit

Slide 5

Metrics of process analysis

§ I = Inventory = how many flow units are in the process

§ R = Flow Rate = rate at which flow units enter or leave the process

§ T = Flow Time = total time a flow unit is in the process

§ Little’s Law:

§ For example:

Call centerincoming calls

completed calls

Inventory = Flow Rate x Flow Time

or

I = R x T

R = On average 11 callers per minute

T = On average a caller spends 2.5 minutes with the call center

I = Average number of callers on the phone with the call center

= R x T = 11 x 2.5

= 27.5 callers

Slide 6

§ Although it looks intuitively reasonable, it's a quite remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else.”

§ Little published his proof of the law, showing that no such situation existed in 1961.

§ Little's proof was followed by a simpler version by Jewell and another by Eilon.

Slide 7

A Little’s Law application: In-transit inventory

§ O’Neill, based in California (CA), buys wetsuits from a supplier in Thailand:

- Each month they order on-average 15,000 wetsuits, R = 15,000

- Shipping between Thailand and CA takes on-average 2 months, T = 2

- I = R x T = 15,000 x 2 = 30,000 units are in-transit on average

T = 2 months

R = 15000/monthR = 15000/month

I = 30,000 wetsuits

Slide 8

Four different ways to count inventory

§ In terms of flow units (The “ I ” in I = R x T):

- Number of wetsuits, patients, tons of wheat, semiconductor chips, etc.

- Useful when the focus is on one particular flow unit.

§ In terms of $s (The “ I ” in I = R x T):

- The $ value of inventory

- This is an intuitive measure of a firm’s total inventory.

§ In terms of days-of-supply:

- The average number of days a unit spends in the system.

- Also, the number of days inventory would last at the average flow rate if no replenishments arrive.

§ In terms of turns:

- The number of times the average amount of inventory exits the system.

Slide 9

Days-of-supply calculations

§ Days-of-supply is the “T ” in I = R x T

§ Days-of-supply = I / R = Inventory / Average daily flow rate

§ Can also be measured in different time lengths (Keep units consistent):

- Weeks-of-supply = Inventory / Average weekly flow rate

- Months-of-supply = Inventory / Average monthly flow rate

- Years-of-supply = Inventory / Average yearly flow rate

§ Our O’Neill example:T = 2 months-of-supply

R = 15000/month

I = 30,000 wetsuits

Slide 10

Inventory turns calculations

§ Inventory Turns = 1 / T = R / I

§ Different measures of turns:

- Yearly turns = Average annual flow rate / Inventory

- Monthly turns = Average monthly flow rate / Inventory

- Weekly turns = Average weekly flow rate / Inventory

- Daily turns = Average daily flow rate / Inventory

- Keep units consistent!

§ O’Neill’s annual turns:

- R = 15000 x 12 = 180,000 per year

- I = 30,000

- T = 2 months = 1/6 year

- Annual Turns = R / I = 180,000 / 30,000 = 6

- Annual Turns = 1/T = 1 / (1/6) = 6

Slide 11

Turns and days-of-supply at Walmart in 2019*

§ COGS = Cost of Goods Sold = Flow Rate

- The Flow Rate is not Sales (which was $514,405) because inventory is measured in the cost to purchase goods, not in the sales revenue that may be earned from the goods.

- Note: Some companies use the term “Cost of sales” to mean COGS

§ Annual turns = $385,301 / $44,269 = 8.70

§ Days-of-supply = $44,269 / ($385,301 / 365) = 41.9

I = Inventory = $44,269

R = COGS = $385,301

* All figures in $Million from 2019 balance sheet and income statement

Slide 12

Walmart’s turns change from year to year

An

nu

al

turn

s

(blu

e lin

e)

Da

ys-o

f-su

pp

ly

(red lin

e)

Slide 13

What it is: Inventory (I) = Flow Rate (R) * Flow Time (T)

How to remember it: - units

Implications:• Out of the three fundamental performance measures (I,R,T), two can be chosen by

management, the other is GIVEN by nature• Hold throughput constant: Reducing inventory = reducing flow time

Given two of the three measures, you can solve for the third:• Indirect measurement of flow rate:

• Indirect measurement of inventory:

• Indirect measurement of flow time

Flow Rate: 5000kg/weekInventory: 2500kg

Little’s law: It’s more powerful than you think...

Inventory: 500 vehiclesFlow Time: 30 days on the lot

Flow Time: 6 hoursFlow Rate: 200 patients per day

Slide 14

Cost of Goods sold: 25,263 mill $/yearInventory: 2,003 mill $

Cost of Goods sold: 20,000 mil $/yearInventory: 391 mil $

Inventory Turns

Inventory TurnsComputed as:

Based on Little’s lawCareful to use COGS, not revenues

Inventory

COGSInventory turns=

0

10

20

30

40

50

60

70

80

90

100

Inventory Turns at Dell

Slide 15

Compaq was acquired by HP in 2002! (51.1 vs. 12.6)

리틀의법칙을활용하여공정재고 I 를계산하여라.

평균 60 일소요

I = ?

700개(웨이퍼)/일

* 웨이퍼한개당약 1,000개의칩

* 수율 = 80%

리틀의법칙적용사례: SK hynix

* 웨이퍼한개당약 1,000개의칩

* 수율 = 80%

평균 60 일소요

I = ?


• 매일평균 700 개의웨이퍼투입, R=700

• 8개공정을진행하는데평균 60일소요, T=60

• I = R x T = 700 개/일 x 60일= 42,000개

• 평균적으로 42,000개의웨이퍼공정재고(I ) 존재

• 출하예정인칩재공품량 : I x 0.8 x 1,000 = 3,360만개



* 웨이퍼한개당약 500개의칩 1 생산,

수율 = 90%, 공정소요시간 = 50일

* 웨이퍼한개당약 800개의칩 2 생산,

수율 = 80%, 공정소요시간 = 60일

I = ?

1,000개(웨이퍼)/일


칩 1

칩 2




I = ?

1,000개(웨이퍼)/일


칩 1

칩 2



I = ?


평균 60 일소요

= ?

= ?

= ?


A사

J사

M사

= ?

= ?

= ?


Five Reasons to Hold Inventory

1. Pipeline Inventory

No factory can operate without work in process (WIP)!How can we reduce PI?

2. Seasonal Inventory

Occurs when capacity is rigid and demand is variable.

Figure 2.10 Seasonal Inventory of Sugar

3. Cycle Inventory

Economy of Scale

4. Decoupling Inventory/Buffers

Line balancing, starvingBuffers can absorb variations in flow rates.

cf. Bucket brigade

5. Safety Inventory

Figure 2.12 Safety Inventory at a Blood Bank

Perishable Inventory

Source of pictures: www.bbc.co.ukwww2.isye.gatech.eduwww.travelpod.com

Job Shop

Batch Process

Worker-paced line

Machine-paced line

Continuous process

Low Volume(unique)

Medium Volume(high variety)

High Volume(lower variety)

Very high volume(standardized)

Utilization of fixed capitalgenerally too low

Unit variable costsgenerally too high

Examples from History:· In the matrix above, history has forced all industries to go down the diagonal· Examples: Eye Surgery, vehicle production, financial services

The Product Process Matrix and the Industrialization of Work(Figure 2.13)

Commercial Printer

Apparel Production

Low-VolumeAuto Assembly

High-VolumeAuto Assembly

Oil Refinery

Rank CompanyPeer Opinion(185 voters)

(25%)

Gartner Opinion(38 voters)

(25%)

Three-Year Weighted

ROA(20%)

Inventory Turns(10%)

Three-Year Weighted Revenue Growth

(10%)

CSRComponent

Score(10%)

Composite Score

1 Unilever 2074 649 10.20% 6.8 1.9% 10 6.39

2 McDonald's 1264 442 13.90% 174.5 -4.20% 3 5.27

3 Inditex 1192 337 16.30% 3.7 12.00% 10 4.98

4 Cisco systems 1018 524 8.30% 13.5 0.8% 10 4.82

5 H&M 901 208 22.00% 3.0 12.50% 10 4.63

6 Intel 952 486 10.50% 4.0 4.60% 7 4.42

7 Nestlé 1159 345 7.90% 5.1 -0.60% 10 4.10

8 Nike 1290 207 16.20% 3.8 7.90% 6 4.07

9 Colgate-Palmolive 843 313 18.00% 5.0 -4.90% 6 4.03

10 Starbucks 926 143 20.30% 11.1 12.70% 4 3.80

11 PepsiCo 974 356 8.50% 9.0 -1.80% 6 3.67

12 3M 553 210 15.30% 4.2 -1.10% 10 3.54

13 Johnson & Johnson 878 269 11.80% 2.6 0.40% 7 3.50

14 The Coca-Cola Co. 1579 232 7.80% 5.7 -4.20% 4 3.46

15 Nokia 315 133 5.80% 5.6 46.30% 10 3.32

16 BASF 579 298 6.1% 4.0 -10.60% 10 3.21

17 Schneider Electric 546 325 4.20% 5.1 -0.30% 10 3.15

18 Walmart 1312 225 7.50% 8.0 0.60% 3 3.11

19 HP 399 275 6.60% 9.8 -5.40% 10 3.06

20 L'Oréal 657 174 10.40% 2.8 5.10% 5 2.72

21 Kimberly-Clark 607 163 11.80% 6.5 -2.60% 5 2.68

22 BMW 681 129 3.70% 4.1 6.60% 10 2.62

23 Diageo 481 190 8.90% 0.9 -1.70% 7 2.57

24 Lenovo 498 223 1.50% 14.0 7.20% 7 2.50

25 Samsung Electronics 958 100 7.30% 15.1 -3.60% 4 2.46

The Gartner Supply Chain Top 25 for 2017


(25%)


(25%)

Three-Year Weighted

ROA(20%)



(10%)

CSRComponent

Score(10%)

Composite Score

1 Unilever 2413 667 10.30% 7.5 2.60% 10 6.36

2 Inditex 1254 345 16.50% 3.9 10.90% 10 4.85

3 Cisco Systems 785 541 7.90% 13.1 -0.40% 10 4.41

4 Colgate-Palmolive 898 324 17.60% 5.1 -2.20% 10 4.40

5 Intel 831 499 8.90% 3.6 4.80% 10 4.36

6 Nike 1349 270 17.40% 3.8 6.80% 6 4.25

7 Nestlé 1326 426 6.40% 4.8 -0.20% 10 4.21

8 PepsiCo 1094 391 7.30% 8.8 -0.60% 10 3.99

9 H&M 760 193 18.10% 2.8 7.80% 10 3.96

10 Starbucks 1040 186 20.40% 11.8 9.20% 4 3.85

11 3M 783 198 14.00% 4.1 1.40% 10 3.56

12 Schneider Electric 737 410 4.80% 5.2 -0.50% 10 3.55

13 Novo Nordisk 121 49 37.90% 1.2 5.30% 10 3.37

14 HP 390 354 7.30% 8.4 0.20% 10 3.30

15 L'Oréal 999 210 9.60% 2.9 4.60% 8 3.26

16 Diageo 651 227 9.20% 1.0 7.60% 10 3.25

17 Samsung Electronics 907 117 10.70% 14.6 9.80% 9 3.22

18 Johnson & Johnson 880 322 6.20% 2.7 2.80% 6 3.08

19 BASF 470 281 6.90% 4.4 -0.50% 10 3.02

20 Walmart 1416 256 6.20% 8.3 1.60% 3 2.98

21 Kimberly-Clark 619 133 13.60% 6.7 -1.60% 8 2.96

22 The Coca-Cola Co. 1558 221 4.60% 4.8 -10.10% 4 2.87

23 Home Depot 431 78 18.60% 5.1 6.70% 5 2.81

24 Adidas 821 115 6.80% 2.9 13.50% 7 2.58

25 BMW 679 118 4.10% 4.2 6.00% 10 2.45


Rank Company Rank in Global Top 100

1 Samsung Electronics 17

2 Lenovo 26

3 Huawei 35

4 Toyota 39

5 Haier 41

6 Bridgestone 56

7 Honda Motor 65

8 Wesfarmers 74

9 Woolworths 77

10 Sony 95

The Gartner Supply Chain Top 10 for 2018: Asia/Pacific


(25%)


(25%)

Three-Year Weighted

ROA(20%)



(10%)

CSRComponent

Score(10%)

Composite Score

1 Colgate-Palmolive 961 347 19.9% 5.0 -0.2% 10.00 4.88

2 Inditex 1,091 341 16.2% 3.8 6.5% 10.00 4.80

3 Nestlé 1,262 374 6.9% 4.8 1.2% 10.00 4.27

4 PepsiCo 997 368 11.7% 9.0 1.2% 8.00 4.22

5 Cisco Systems 699 518 4.0% 10.2 0.7% 10.00 4.13

6 Intel 576 454 12.4% 3.7 9.6% 6.00 4.12

7 HP Inc. 293 353 11.7% 8.2 7.3% 10.00 3.81

8 Johnson & Johnson 737 348 7.6% 3.1 5.8% 10.00 3.80

9 Starbucks 900 167 19.3% 12.7 9.0% 4.00 3.74

10 Nike 1,194 186 13.3% 3.9 6.0% 4.00 3.73

11 Schneider Electric 677 256 5.4% 4.9 0.7% 10.00 3.71

12 Diageo 625 404 9.8% 0.9 4.3% 10.00 3.44

13 Alibaba 1,095 72 10.6% 23.4 52.6% 0.00 3.43

14 Walmart 1,415 268 4.6% 8.6 2.5% 5.00 3.40

15 L’Oréal 858 229 9.9% 2.7 3.6% 8.00 3.38

16 H&M 582 155 13.7% 2.7 5.1% 10.00 3.35

17 3M 597 192 14.3% 3.8 3.2% 8.00 3.34

18 Novo Nordisk 86 54 36.4% 1.1 0.8% 10.00 3.31

19 Home Depot 402 124 22.2% 5.0 7.0% 5.00 3.29

20 Coca Cola Company 1,329 196 5.8% 4.2 -10.7% 6.00 3.13

21 Samsung Electronics 748 83 13.2% 9.8 8.7% 7.00 3.05

22 BASF. 597 252 6.4% 3.9 -0.6% 8.00 2.89

23 Adidas 714 172 9.2% 3.2 7.9% 5.00 2.75

24 Akzo Nobel 137 0 20.9% 4.6 -8.6% 8.00 2.61

25 BMW 733 131 3.8% 3.8 1.3% 10.00 2.57



(25%)


(25%)

Three-Year Weighted

ROPA(20%)

Inventory Turns(5%)


(10%)

ESGComponent

Score(15%)

Composite Score

1 Cisco Systems 470 574 300.7% 12.5 2.9% 10.00 6.25

2 Colgate-Palmolive 1113 532 68.8% 4.7 1.0% 10.00 5.37

3 Johnson & Johnson 885 454 77.6% 3.0 3.6% 8.00 4.65

4 Schneider Electric 567 453 63.0% 5.4 4.2% 10.00 4.48

5 Nestlé 1084 350 40.0% 4.8 1.2% 10.00 4.44

6 PepsiCo 857 385 47.9% 8.2 2.7% 10.00 4.42

7 Alibaba 991 316 106.7% 23.9 54.0% 0.00 4.39

8 Intel 583 488 37.4% 3.5 5.8% 8.00 4.12

9 Inditex 737 351 34.7% 4.6 6.8% 10.00 4.11

10 L’Oréal 677 252 71.1% 2.8 7.4% 10.00 4.01

11 Walmart 1333 324 13.2% 8.5 2.4% 7.00 4.00

12 HP Inc. 296 389 51.1% 8.5 5.5% 10.00 3.87

13 Coca Cola Company 1195 207 75.4% 4.4 0.0% 6.00 3.74

14 Diageo 403 280 41.4% 0.9 6.2% 10.00 3.49

15 Lenovo 397 307 16.9% 11.2 7.0% 10.00 3.44

16 Nike 768 265 47.2% 4.0 6.7% 6.00 3.35

17 AbbVie 128 30 262.4% 4.1 7.6% 5.00 3.20

18 BMW 575 182 24.8% 3.9 4.2% 10.00 3.17

19 Starbucks 799 202 52.6% 13.0 7.7% 4.00 2.99

20 H&M 412 161 22.4% 2.8 7.7% 10.00 2.95

21 British American Tobacco 154 56 85.6% 0.7 18.1% 9.00 2.90

22 3M 624 207 54.1% 3.9 1.1% 6.00 2.90

23 Reckitt Benckiser 265 14 99.0% 3.8 8.2% 9.00 2.79

24 Biogen 79 27 152.2% 2.5 7.8% 7.00 2.78

25 Kimberly-Clark 534 80 34.6% 6.6 0.2% 10.00 2.76


chapter 2. the process view

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