Operations Management Final

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Critical Path

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111 Terms

1

Critical Path

longest path from start to finish

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2

ES

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

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3

LS

Latest start time = LF – t

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4

EF

Earliest finish time = ES + t

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LF

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

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Slack

subtraction of either LatestStart - EarliestStart or LatestFinish - EarliestFinish

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Process

turning inputs into outputs

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Backward integration

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

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Forward Integration

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

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4 Costs of Quality

External failure, Internal failure, Appraisal, Prevention

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Causes of Variation: Common Causes

(completely unavoidable)

  • purely random; unidentifiable factors

  • e.g. diameter varies by 0.0001 in.

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Causes of Variation: Assignable Causes

(the real reason why; to investigate)

  • Variation-causing factors that can be identified

  • e.g. poorly trained employee

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13

Types of Variable Charts (continuous numerical data)

R-charts, xbar-charts

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14

Types of Attribute Charts (discrete numerical data)

p-charts, c-charts

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Variables (to measure performance)

weight, length, volume, or time

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Variable Data Examples

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

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Attributes (to measure performance)

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

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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?

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SPC

Statistical Process Control

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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

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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

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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

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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

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Using Control Charts for Process Improvement

Sample the process

Find the assignable cause

Eliminate the problem

Repeat the cycle

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Variable charts involve ____ measurements

precise

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Attribute charts involve measuring by ____

counting the # of defects

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R-bar & X-bar charts _____

have to be done together

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p-charts & c-charts _____

are less expensive $$$ than other charts

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29

p-bar =

total defectives / total observations

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c-chart UCL & LCL steps

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

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

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

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What makes c-Charts different?

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

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32

Six Sigma Quality: DMAIC Cycle

D - Define

M - Measure

A - Analyze

I - Improve

C - Control

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WBS stands for ____

Work Breakdown Structure

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5 Steps to Planning Projects

  1. Define work breakdown structure

  2. Diagram the network

  3. Develop the schedule

  4. Analyze cost-time trade-offs

  5. Assess risks

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35

WBS First Level (after starting a business)

<p></p>
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WBS Second Level

knowt flashcard image
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WBS Third Level

knowt flashcard image
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38

AON stands for

Activity-on-Node

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39

EOQ (an Inventory Model) stands for ____

Economic Order Quantity

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40

The Q System (an Inventory Model) is the _____

Continuous Review System

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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)

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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)

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The P System (an Inventory Model) is the _____

Periodic Review System

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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

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

Always place an order of size Qt = T - IPt

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Comparatively, P Systems (single-bin system)

  • convenient to administer

  • orders may be combined

  • IP only required at review

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47

Comparatively, Q Systems (two-bin system)

  • individual review frequencies

  • possible quantity discounts

  • lower, less-expensive safety stocks

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48

What is the average cost of inventory?

30-35% of product’s value (about 1/3)

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Examples of Pressures for High Inventory

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

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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

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51

Is inventory bad?

Not necessarily

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Physical Inventory

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

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Conceptual Inventory

  1. Cycle Inventory

  2. Safety Stock Inventory

  3. Anticipation Inventory

  4. Pipeline Inventory

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54

EOQ Assumption: Demand rate is ____

constant

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EOQ Assumption: No constraints on ____

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

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EOQ Assumption: Only costs are _____

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

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EOQ Assumption: Decisions for items are _____

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

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EOQ Assumption: No uncertainty in _____

lead time or supply

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Lead Time =

Time between placing an order and receiving it

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Lot sizing

Two Decisions:

  1. When to order?

  2. How much to order?

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Cycle Inventory

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

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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

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Anticipation Inventory

  • Absorbs uneven rates of demand

    • Predictable, seasonal demand patterns

    • Anticipating supplier strike

  • Stockpile during low demand

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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)

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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%

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66

Holding cost _____ as Lot Size (Q) increases

Holding cost increases

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Ordering cost _____ as Lot Size (Q) increases

ordering cost decreases

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Total cost ____ as Lot Size (Q) increases

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

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EOQ Variables are

D = Annual Demand

Q = Lot Size

S = Cost of Setup Ordering Cost (?)

H = Holding cost

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70

Aspects of a Push System

  • Production trigger is based on forecasts or desired inventory levels

  • No bounds on inventory

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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)

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Aspects of a Pull System

  • Production trigger is actual consumption of inventory

  • Imposes a bound on inventory

  • Eg. Kanban

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When to use a Pull system?

  • Small setups & Few product lines

  • Example: custom cakes

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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)

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75

Idle time

sitting waiting for a unit

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76

Cycle time

time in between finished goods getting off the assembly line

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77

Supply Chain

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

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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 <-

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79

Stockout

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

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80

Backorder

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

(cumulation of these would be a backlog)

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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

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82

Load-distance score

Select site that minimizes distances “loads” must travel

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Horizontal Pattern of Demand

Data cluster about a horizontal line as time progresses

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Trend Pattern of Demand

Data consistently increase or decrease

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Seasonal Pattern of Demand

Data consistently show peaks and valleys

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Cyclical Pattern of Demand

Data reveal gradual increases and decreases over extended periods

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Time-Series Methods

  1. Simple moving averages

  2. Weighted moving averages

  3. Exponential smoothing

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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

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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

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Exponential Smoothing

  • forecasting software

  • really strong results

  • Ft+1 = forecasted demand for period t + 1

    • Dt = actual demand in period t

    • sigma = smoothing parameter

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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

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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

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MSE

= Mean Squared Error

  • measure of variability

  • Square Error = error ^2

A measurement of the dispersion of forecast errors.

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94

Square Error

= error ^2

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95

MAD

= Mean Absolute Deviation

  • another indication of variability

  • Absolute Error = ABS(Actual - Predicted)

A measurement of the dispersion of forecast errors.

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96

Absolute Error

= ABS(Actual - Predicted)

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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

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Absolute % Error

= (Absolute Error / ACTUAL DEMAND) * 100%

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Error

Difference between actual and predicted

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100

Error relative to size of demand is related to _____

MAPE (Mean Absolute Percent Error)

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