Studied by 1 person

0.0(0)

Get a hint

Hint

Looks like no one added any tags here yet for you.

1

Critical Path

longest path from start to finish

New cards

2

ES

Earliest start time = max[EF times of all activities immediately preceding activity]

New cards

3

LS

Latest start time = LF – t

New cards

4

EF

Earliest finish time = ES + t

New cards

5

LF

Latest finish time = min[LS times of all activities immediately following activity]

New cards

6

Slack

subtraction of either LatestStart - EarliestStart or LatestFinish - EarliestFinish

New cards

7

Process

turning inputs into outputs

New cards

8

Backward integration

owning and controlling entities upstream (earlier on in the chain → strawberry farms, dairy farms)

New cards

9

Forward Integration

owning and controlling entities downstream (closer to the consumer → food trucks, deli restaurants)

New cards

10

4 Costs of Quality

External failure, Internal failure, Appraisal, Prevention

New cards

11

Causes of Variation: Common Causes

(completely unavoidable)

purely random; unidentifiable factors

e.g. diameter varies by 0.0001 in.

New cards

12

Causes of Variation: Assignable Causes

(the real reason why; to investigate)

Variation-causing factors that can be identified

e.g. poorly trained employee

New cards

13

Types of Variable Charts (continuous numerical data)

R-charts, xbar-charts

New cards

14

Types of Attribute Charts (discrete numerical data)

p-charts, c-charts

New cards

15

Variables (to measure performance)

weight, length, volume, or time

New cards

16

Variable Data Examples

How long did the customer wait? What was diameter of the pizza? Temp of food? Weight of chicken?

New cards

17

Attributes (to measure performance)

yes-no counts (yes, defect or no defect)

New cards

18

Attribute Data Examples

Food sent back to kitchen? Bill correct or not? Did food leave the kitchen < 147 degrees? Was the pizza larger than 12 in?

New cards

19

SPC

Statistical Process Control

New cards

20

Step 1: Calculating X-bar & R-Chart UCL & LCL

within each sample (will either be by row or by column), calculate the range and the average

New cards

21

Step 2: Calculating X-bar & R-Chart UCL & LCL

then calculate the average of all sample ranges and averages to compute x-bar and R-bar

New cards

22

Step 3: Calculating X-bar & R-Chart UCL & LCL

based on n (aka sample size), identify A2, D3, D4 values on the chart to be used in the formulas

New cards

23

What makes a process out of control

run = is when you have 5 consecutive sample points either to the upperside or lowerside of your nominal value

reached past UCL or LCL

New cards

24

Using Control Charts for Process Improvement

Sample the process

Find the assignable cause

Eliminate the problem

Repeat the cycle

New cards

25

Variable charts involve ____ measurements

precise

New cards

26

Attribute charts involve measuring by ____

counting the # of defects

New cards

27

R-bar & X-bar charts _____

have to be done together

New cards

28

p-charts & c-charts _____

are less expensive $$$ than other charts

New cards

29

p-bar =

total defectives / total observations

New cards

30

c-chart UCL & LCL steps

calculate average number of defects per item (total defects observed / total # of observed items) = c-bar

take c-bar + z (whatever sigma, ex 3) * sqrt c-bar = UCL

take c-bar - z (whatever sigma, ex 3) * sqrt c-bar = LCL

New cards

31

What makes c-Charts different?

c-Charts: defects on __ one unit__ (e.g. how many scratches are on one piece of plexiglass (c = 4)

New cards

32

Six Sigma Quality: DMAIC Cycle

D - Define

M - Measure

A - Analyze

I - Improve

C - Control

New cards

33

WBS stands for ____

Work Breakdown Structure

New cards

34

5 Steps to Planning Projects

Define work breakdown structure

Diagram the network

Develop the schedule

Analyze cost-time trade-offs

Assess risks

New cards

35

WBS First Level (after starting a business)

New cards

36

WBS Second Level

New cards

37

WBS Third Level

New cards

38

AON stands for

Activity-on-Node

New cards

39

EOQ (an Inventory Model) stands for ____

Economic Order Quantity

New cards

40

The Q System (an Inventory Model) is the _____

Continuous Review System

New cards

41

Aspects of the Continuous Review (Q) System

constant lead times introduced now

two models

certain demand and uncertain demand

Inventory position (IP) = OH (on hand inventory) + SR (scheduled receipts) - BO (backorders)

New cards

42

Continuous Review → continuously checking IP after every withdrawal….

Rule:

if IP > R do not place an order

if IP <= R, place an order of size Q

Eg walmart checkout

(R means Reorder point)

New cards

43

The P System (an Inventory Model) is the _____

Periodic Review System

New cards

44

Aspects of Periodic Review (P) Systems

Fixed interval reorder system (order once a week or month)

Q may vary with each order

IP reviewed periodically instead of continuously, new order after every review

TBO (Time Between Orders) fixed at P

New cards

45

Periodic Review → Only check your inventory position after every P time periods…

Always place an order of size Qt = T - IPt

New cards

46

Comparatively, P Systems (single-bin system)

convenient to administer

orders may be combined

IP only required at review

New cards

47

Comparatively, Q Systems (two-bin system)

individual review frequencies

possible quantity discounts

lower, less-expensive safety stocks

New cards

48

What is the average cost of inventory?

30-35% of product’s value (about 1/3)

New cards

49

Examples of Pressures for High Inventory

Customer Service/On-Time delivery, Quantity discounts, Transportation costs, Setup & Ordering costs, Supplier’s prices about to increase

New cards

50

Examples of Pressures for Low Inventory

Pilferage (stealing), Storage and handling costs, Obsolescence (becomes obsolete soon, short product lifetime), Interest or opportunity cost, Insurance on assets, End of year taxes

New cards

51

Is inventory bad?

Not necessarily

New cards

52

Physical Inventory

Raw materials, component parts, work in process (WIP), finished goods

New cards

53

Conceptual Inventory

Cycle Inventory

Safety Stock Inventory

Anticipation Inventory

Pipeline Inventory

New cards

54

EOQ Assumption: Demand rate is ____

constant

New cards

55

EOQ Assumption: No constraints on ____

lot size (Q) --- any size Q is possible!

New cards

56

EOQ Assumption: Only costs are _____

holding (storing an item) and ordering (administrative costs)

New cards

57

EOQ Assumption: Decisions for items are _____

independent (no correlation between different products; we’re just ordering final products)

New cards

58

EOQ Assumption: No uncertainty in _____

lead time or supply

New cards

59

Lead Time =

Time between placing an order and receiving it

New cards

60

Lot sizing

Two Decisions:

When to order?

How much to order?

New cards

61

Cycle Inventory

saw-tooth diagram (max Q units, min 0 units)

New cards

62

Safety Stock Inventory

Protects against uncertainties in demand, lead time, and/or supply

Operations not disrupted

Avoid customer service problems

**Place order earlier than needed**

New cards

63

Anticipation Inventory

Absorbs uneven rates of demand

Predictable, seasonal demand patterns

Anticipating supplier strike

Stockpile during low demand

New cards

64

Pipeline Inventory

Inventory moving from point to point in material flow system

eg parts traveling on trucks (inbound)

eg materials moving between operations (within plant)

eg finished goods shipped to distribution center (outbound)

New cards

65

ABC Analysis

Classifying inventory to best manage it

Class A: 20% of the items make up 80% of the total dollar value (TVs in the back; protect them)

Class B: 30% of the items make up like 25%

Class C: 50% of the items make up like 5%

New cards

66

Holding cost _____ as Lot Size (Q) increases

Holding cost increases

New cards

67

Ordering cost _____ as Lot Size (Q) increases

ordering cost decreases

New cards

68

Total cost ____ as Lot Size (Q) increases

Total cost decreases initially, then increases (like a Nike swoosh)

New cards

69

EOQ Variables are

D = Annual Demand

Q = Lot Size

S = Cost of Setup Ordering Cost (?)

H = Holding cost

New cards

70

Aspects of a Push System

Production trigger is based on forecasts or desired inventory levels

No bounds on inventory

New cards

71

When to use a Push system?

Example: Convenient store (inventory has built up; waiting for you to buy it, based off of forecasts)

Long setups (spread the costs over the course of the setup) & Variety of products (will have to produce an array of colors in batches to offer customers)

New cards

72

Aspects of a Pull System

Production trigger is actual consumption of inventory

Imposes a bound on inventory

Eg. Kanban

New cards

73

When to use a Pull system?

Small setups & Few product lines

Example: custom cakes

New cards

74

Kanban

Visual display to decrease throughput time e.g. must have 3 in your inbox, 0 in your outbox to start working (airplane example)

New cards

75

Idle time

sitting waiting for a unit

New cards

76

Cycle time

time in between finished goods getting off the assembly line

New cards

77

Supply Chain

Two or more parties linked by a flow of material, information, & money, often global in scope

New cards

78

Bullwhip

order variation increases as you go upstream towards the supplier

Supplier → M → R → Customer (looks like a bullwhip with the handle at the customer and the wavy end of variability is at the supplier)

downstream is going → to the customer, upstream is going to the supplier <-

New cards

79

Stockout

when you run out of a product, you will no longer be selling the product anymore

New cards

80

Backorder

Ran out of a product but eventually are going to fulfill the order

(cumulation of these would be a backlog)

New cards

81

Center of Gravity

Determine x and y coordinates in the middle (may not be feasible right at this point but start there and go out)

x* and y*

example: where you pinpoint the starting point to the realtor

New cards

82

Load-distance score

Select site that minimizes distances “loads” must travel

New cards

83

Horizontal Pattern of Demand

Data cluster about a horizontal line as time progresses

New cards

84

Trend Pattern of Demand

Data consistently increase or decrease

New cards

85

Seasonal Pattern of Demand

Data consistently show peaks and valleys

New cards

86

Cyclical Pattern of Demand

Data reveal gradual increases and decreases over extended periods

New cards

87

Time-Series Methods

Simple moving averages

Weighted moving averages

Exponential smoothing

New cards

88

Simple Moving Average

Simple;

Dt = actual demand in period t

n = total number of periods in the average

F t+1 = forecast for period t + 1

New cards

89

Weighted Moving Average

weights on historical demand (weight more recent demand more heavily, lowering

more control

Ft+1 = forecasted demand for period t + 1

Dt = actual demand in period t

Wi = assigned weight

New cards

90

Exponential Smoothing

forecasting software

really strong results

Ft+1 = forecasted demand for period t + 1

Dt = actual demand in period t

sigma = smoothing parameter

New cards

91

Trend-Adjusted Exponential Smoothing

At = exponentially smoothed average of the series in period t

Tt = exponentially smoothed average of the trend in period t

sigma, Beta = smoothing parameters

New cards

92

CFE

= Cumulative Forecast Error

*A measurement of the total forecast error that assesses the bias in a forecast.*

if CFE is negative, we are overestimating

asses **bias**

New cards

93

MSE

= Mean Squared Error

measure of variability

Square Error = error ^2

*A measurement of the dispersion of forecast errors.*

New cards

94

Square Error

= error ^2

New cards

95

MAD

= Mean Absolute Deviation

another indication of variability

Absolute Error = ABS(Actual - Predicted)

A measurement of the dispersion of forecast errors.

New cards

96

Absolute Error

= ABS(Actual - Predicted)

New cards

97

MAPE

= Mean Absolute Percent Error

average of the absolute percent errors; telling us amount of error relative to the size of demand

Absolute % Error = (Absolute Error / ACTUAL DEMAND) * 100%

big deal to be off by 10 diamonds at a small jewelry store versus 10 cotton balls at a large manufacturing plant

New cards

98

Absolute % Error

= (Absolute Error / ACTUAL DEMAND) * 100%

New cards

99

Error

Difference between actual and predicted

New cards

100

Error relative to size of demand is related to _____

MAPE (Mean Absolute Percent Error)

New cards