flow line forecasting

7/30/2019 Flow Line Forecasting

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ME 101: High Mix/Low Volume Manufacturing

University of California, Berkeley

Lecture #9

Mechanical Engineering 101




Today’s lecture

Flow lines

Forecasting




1 2 3 4 NIN OUT

Production System Model

N unit processes

• operation or suite of operations

• self-contained, distinct

transfer mechanism (arrows)• continuous

• intermittent (synchronous or asynchronous)




1 2 3 4 5IN OUT

Toc1=1 Toc2=3 Toc3=2 Toc4=2 Toc5=1

Mass production, synchronous

flow line

• Slowest operation is bottleneck

• line cycle time is longest operation cycletime plus transfer/repositioning time

Tc = Tr + Max (Toci) – “ideal cycle time” (theoretical)

Rc = 60 / Tc

– ideal production rate




1 2 3 4 NIN OUT

What actually happens at each station?

2

From 1 To 3

Process

a b c d e (was a for 1)

Transfer = a b 2

Transfer = 2 d e“simultaneous”




What happens if Toci < Tocmax ?

3

From 2 To 4

Process

a b c d e

Tocmax

Toc3

idle

Process




1 2 3 4 5IN OUT

WIP = 0

0Tc

2Tc

3Tc

4Tc

5Tc

6Tc

7Tc




0Tc

2Tc

3Tc

4Tc

5Tc

6Tc

7Tc

1 2 3 4 5IN OUT

WIP = 1




0Tc

2Tc

3Tc

4Tc

5Tc

6Tc

7Tc

1 2 3 4 5IN OUT

WIP = 2




1 2 3 4 5IN OUT

WIP = 3

0Tc

2Tc

3Tc

4Tc

5Tc

6Tc

7Tc




0Tc

2Tc

3Tc

4Tc

5Tc

6Tc

7Tc

1 2 3 4 5IN OUT

WIP = 4




0Tc

2Tc

3Tc

4Tc

5Tc

6Tc

7Tc

1 2 3 4 5IN OUT

WIP = 5




9Tc

10Tc

11Tc

12Tc

13Tc

6Tc

7Tc

8Tc

1 2 3 4 5IN OUT

WIP = 5




1 2 3 4 5IN OUT

WIP = 4

9Tc

10Tc

11Tc

12Tc

13Tc

6Tc

7Tc

8Tc




1 2 3 4 5IN OUT

WIP = 3

9Tc

10Tc

11Tc

12Tc

13Tc

6Tc

7Tc

8Tc




1 2 3 4 5IN OUT

WIP = 2

9Tc

10Tc

11Tc

12Tc

13Tc

6Tc

7Tc

8Tc




1 2 3 4 5IN OUT

WIP = 1

9Tc

10Tc

11Tc

12Tc

13Tc

6Tc

7Tc

8Tc




1 2 3 4 5IN OUT

WIP = 0

9Tc

10Tc

11Tc

12Tc

13Tc

6Tc

7Tc

8Tc




WIP

Cycle increments0 1 2 3 4 5 6 7 8 9 10 11 12

Variation in work in process over batch

System at capacity




Announcements

TI calculator




Stats Background Survey

What is the highest level of formal statisticsyou’ve studied?

1. some mention in high school math2. some coverage in college classes

3. AP stats in high school

4. significant coverage in college classes

5. took a college class focused on statistics6. took more than one college class focused on

statistics




For a normal distribution, percent of area undercurve between red lines is approximately:

1. 25%2. 33%3. 50%4. 68%

5. 75%6. 95%7. 98%8. none of the above

Stats Quiz

Frequency

+-

- 2 + 2

mean=00




Today’s lecture

Flow lines

Forecasting




Demand

Production planning

• flow line

need for setting cycle time

• batch

need for EOQ formula

when to develop new products

• product life cycle when to add/close/upgrade production

facilities




Ask experts?

“I think there is a world market for maybe fivecomputers.”• Thomas J. Watson, IBM (1943)

“There is no reason for any individuals to have acomputer in their home.”• Ken Olsen, Digital (1977)

“The wireless music box has no imaginable value.Who would pay for a message sent to nobody inparticular?” • David Sarnoff, RCA




Forecasting

• looking at existing products… • examine past data

• find historical trends• extrapolate forward

• assumptions• past trends will continue

• demand values a function of time




Naïve forecasting

this week’s demand = last week’s • D(t) = D(t-1)

So if we want 1 week safety stock• target inventory I*(t) = 2 x D(t-1)

• production level P(t) = I*(t) - I(t) + D(t-1)

• = 3 x D(t-1) - I(t)

Issues• amplifies variations in demand

• large fluctuations in production

• extra inventory w/ slight demand variations




Forecasts need to reflect variability in demand data.

Caution: data not always normally distributed (why?)

Demand

Frequency

+-

Averagedemand

- 3 + 3

confidence interval ( 3)

Variability in Demand




Demand Data

• typically look at:• shipments

• past customer orders• is this the same as demand?




Demand Data

• Issues• orders never delivered

• extra orders “booked” at end of quarter • over/under ordering• special advertising campaigns• lost sales due to missed delivery




Short term forecasting

constant demand model

demand in period t (D t ) =

• mean expected demand

• + “random error” term t (0, 2)



ME 101: High Mix/Low Volume Manufacturing Lecture 7

demand in period t estimated as constant mean + “random error” term: D t = + t

Time (t)

1

1 2 3 10

Constant mean demand

1




Simple exponential smoothing

use weighted average of all past data

weight so importance of older data

decays• is weighting factor

proportion of weight for most recent data

estimate S t

(for future periods givendata through period t):• S t = D t + (1 - )S t-1 (text eqn 3.3)





S t = D t + (1 - )S t-1 S t = D t + (1 - )[D t-1 + (1- ) S t-2]

S

t = D

t + (1 - )D

t-1+ (1- )2 [D

t-2 + (1 - )S

t-3]

S t = D t + (1 - )D t-1 + (1- )2D t-2 + (1 - )3[ D t-3+(1 - ) S t-4]





S t = D t + (1 - )S t-1 S t = D t + (1 - )[D t-1 + (1- ) S t-2]

S

t = D

t + (1 - )D

t-1+ (1- )2 [D

t-2 + (1 - )S

t-3]

S t = D t + (1 - )D t-1 + (1- )2D t-2 + (1 - )3[ D t-3+(1 - ) S t-4]

… S

t (1 )

k

k 0

Dt k

flow line forecasting

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