# Public Operations Management

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Public Operations Management

## 1. The Waiting Time Formula

### 1.1. Waiting Time Formula for Multiple, Parallel Resources

1.1.1. U=FR/CAP

1.1.1.1. (1/a)/(m/p)=p/(a*m)

### 1.4. Utilization = process time / ( interarrival time * number of servers)

1.4.1. U= p/(a*m)

## 2. Implementations

### 2.1. Six Sigma

2.1.1. ppm

2.1.1.1. Parts Per Million

2.1.1.1.1. Defects Per Million Parts

2.1.2. Variation

2.1.2.1. assignable cause

2.1.2.1.1. Statistical Process Control

2.1.2.2. common cause

2.1.3. Process Capability / Capability Score

2.1.3.1. Cp = (USL - LSL)/6*std

2.1.3.1.1. USL = Upper Specification Limit

2.1.3.1.2. LSL = Lower Specification Limit

2.1.3.2. NORMDIST in excel

2.1.4. Manage Quality

### 2.3. Toyota Production System

2.3.1. Inventory leads to longer "Inventory turnaround time" / feedback loops

2.3.2. Jidoka

2.3.2.1. Detect / Alert / Stop

2.3.2.2. Andon Cord

2.3.3. Manage Quality

2.3.4. Kaizen

2.3.4.1. Root Cause Analysis

2.3.4.1.1. Ishikawa Diagram

2.3.4.1.2. 5 Whys

2.3.4.1.3. Pareto Chart

2.3.4.2. interplay of reality and models, iterative problem solving

### 2.4. pull system / kanban

2.4.1. "buffers are as unlean as it can get"

## 3. Variability of Demand / pooling

### 3.1. statistics

3.1.1. mean

3.1.1.1. mü

3.1.2. standard deviation

3.1.2.1. sigma

3.1.2.1.1. s

3.1.3. Coefficient of Variation

3.1.3.1. CV=mü/s

3.1.3.1.1. doubling size leads to 2^(1/2) CV

3.1.4. positive correletion

3.1.4.1. when two things are positively correlated, it means two things: 1) a change in one of the two things directly leads to a change in the other thing. 2) both changes are similar in sign. take for example, temperature and ice melt. these two are positively correlated in that when temperature goes up, ice melt goes up. If temperature goes down, ice melt goes down.

3.1.5. random arrival times

3.1.5.1. a= average inter arrival time

3.1.5.2. CV = Coefficent of Variation

3.1.5.3. CVa = St-Dev ( inter arrival times)/(Avg(inter arrival times)

3.1.5.4. Poisson ditribution

3.1.5.4.1. CVa = 1

3.1.5.4.2. Constant hazard times (no memory)

3.1.5.4.3. Exponential inter arrivals

3.1.5.4.4. Exponential inter-arrivals

### 3.4. Delayed Differntiation

3.4.1. Modular Production

### 3.6. priority rules

3.6.1. FCFS

3.6.1.1. = FIFO

3.6.1.2. easy to implement

3.6.1.3. fairness

3.6.1.4. lowest variance of waiting time

### 3.7. sequence based on importance

3.7.1. shortest Processing Time Rule (SPT)

3.7.1.1. minimize average wiating time

3.7.1.2. Hard to hav true processing time

### 3.8. appointments

3.8.1. problem

3.8.1.1. no shows

3.8.1.2. solves wrong problem: shifts queue

### 3.9. waiting Problems, loss problems

3.9.1. Customers leaving while waiting.

3.9.2. due to limited buffers

3.9.3. impatient customers

3.9.4. Loss analyzation

3.9.4.1. Percentage of Lost Customers

3.9.4.1.1. r=p/a (p=processing time, a=interarrival time)

### 3.10. Implied Utilization with Loss

3.10.1. 1. Calculate Demand D

3.10.1.1. begin from the end of process

3.10.2. 2. IU = effective Demand at step / Capacity

## 4. Quality

### 4.1. yield

4.1.1. percentage according to specification

4.1.2. 1 - defect probabilty

### 4.2. Swiss Cheese Model

4.2.1. Redundency

4.2.2. ALL things have to go wrong

### 4.3. Defects

4.3.1. Scrap

4.3.2. Rework

4.3.3. Cost of Defects

4.3.3.1. CATCH defects BEFORE bottleneck

4.3.3.1.1. costs before bottleneck: costs of goods used

4.3.3.1.2. costs after bottleneck: opportunity costs, full price charged!

4.3.4. Buffer or Suffer

4.3.4.1. Starved vs. Blocked

4.3.4.1.1. Starve = nothing recieved from up stream

4.3.4.1.2. Blocked = no place to put downstream

4.3.4.2. Buffers can reduce variability in Quality (prevent starving or blocking)

4.3.4.3. Toyota: Inventory prevents quality problems from being reocognized

4.3.4.3.1. expose rocks!

## 5. Process Mapping

### 5.1. Yves Pigneur

5.1.1. Customer Actions

5.1.2. Onstage Actions

5.1.3. Backstage Actions

## 6. Flexibilty

### 6.1. No Flexibility / Full Flexibility / Partial Flexibility

6.1.1. Partial Flexibility get almost all benefits of Full felxibilty with lower costs

## 7. mixed Module Production

### 7.3. calculate batch size

7.3.1. B/(S+B*p) = Demand or Capacity (Units/t)

7.3.1.1. B= Batch Size

7.3.1.2. S= Total Setup Time

7.3.1.3. p = Processing Time

## 8. Variety

### 8.1. Forms

8.1.1. Fit Based Variety

8.1.1.1. Horizontal Variety

8.1.1.2. distribution

8.1.1.3. examples

8.1.1.3.1. T-shirts, Shoes

8.1.1.3.2. Opening Hours

8.1.1.3.3. Departure Times for planes, trains

8.1.2. Perfomance Based Variety

8.1.2.1. Vertical

8.1.2.2. Customers differntiate on quality

8.1.2.3. distribution

8.1.2.4. examples

8.1.2.4.1. more features

8.1.2.4.2. better quality

8.1.3. Taste Based Variety

8.1.3.1. customers differ in their preferences or taste

8.1.3.2. distribution

8.1.3.3. examples

8.1.3.3.1. taste of food

8.1.3.3.2. art

8.1.3.3.3. colors

### 8.2. economic motives

8.2.1. performance based

8.2.1.1. Segment Market

8.2.2. Taste Based, Fit Based

8.2.2.1. Cater to heterogeneous customers

8.2.3. Variety seeking customers

8.2.3.1. example

8.2.3.1.1. food, lunch

8.2.4. avoid competition

8.2.4.1. varietion in a product can make it the lowest price, because none other is the same

## 9. Takt Time

9.1.1. sec/unit

### 9.2. determine

9.2.1. 1. Assign task so that total processing times < Takt time

9.2.2. 2. Make sure that all tasks are assigned

9.2.3. 3. Minimize the number of people needed (maximize labor utiilization)

## 10. overall equipment effectiveness (OEE)

### 10.1. Downtime Losses

10.1.1. Availability Rate

### 10.2. Speed Losses

10.2.1. Performance Rate

### 10.3. Quality Losses

10.3.1. Quality Rate

### 10.4. Overall people effectiveness

10.4.1. 100% total paid time

## 11. Set Up

### 11.1. batch

11.1.1. number of units between setups

11.1.2. capacity given batch size= Batch Size / (Setup Time + Batch size*Time per Unit)

11.1.3. with large batches, processing time approaches 1/processing time, set up becomes more and more irrelevant

### 11.2. SMED

11.2.1. SIngle Minute Exchange of Die

11.2.1.1. 6 stage approach

11.2.2. every set up can be broken up into

11.2.2.1. internal

11.2.2.2. external

11.2.2.2.1. set up that can be done in parallel before machine is standing still

## 12. productivty

### 12.1. Units Output Produced / Input Used

12.1.1. example: 4 Units per labor hours (looks like processing time)

12.1.2. output: pruductive time

12.1.3. input: total time

### 12.2. Multifactor productivity

12.2.1. Output /(Capital\$ + Labor\$ + Materials\$ + Services\$ + Energy\$)

### 12.3. kpi

12.3.1. key performance indicator

12.3.2. kpi tree

12.3.2.1. PROFIT

12.3.2.1.1. minus

12.3.3. break even point

### 12.4. Labor Productivity

12.4.1. Revenue / Labor Costs

## 13. waste

### 13.1. 7 sources of waste

13.1.1. overproduction

13.1.1.1. match supply with demand

13.1.2. transportation

13.1.2.1. relocate processes, then introduce standard sequences for transportation

13.1.3. rework

13.1.3.1. repetion or correction

13.1.3.2. analyse and solve root causes of rework -> more quality in module

13.1.4. over processing

13.1.4.1. provide clear, customer-driven standards for every process

13.1.5. motion

13.1.5.1. arrange people and parts around stations with work content that has been standardized to minimize motion

13.1.6. inventory

13.1.6.1. improve production control system and commit to reduce unnecessary "comport stocks"

13.1.7. waiting

13.1.7.1. understand the drivers of waiting

13.1.8. intellect

13.1.8.1. intelligence of workers

## 14. authors

### 14.1. original mindmap

14.1.1. @davidbaer

## 15. Measurements

### 15.1. four dimensions

15.1.1. Productivity / costs

15.1.1.1. efficiency

15.1.2. variety

15.1.2.1. heterogeneity customer preferences

15.1.3. quality

15.1.3.1. product quality

15.1.3.1.1. how good

15.1.3.2. process quality

15.1.3.2.1. as good as promised

15.1.4. time

15.1.4.1. Responsiveness to demand

15.1.4.2.1. responsiveness vs. labor productivy

15.1.4.3. efficient forntier

15.1.4.3.1. line where industry has current froniter

### 15.2. performence measurements

15.2.1. cumulative inflow

15.2.2. cumulative outflow

15.2.3. cumulative flow

15.2.3.1. flow time ( horizontal)

15.2.3.2. Inventory (vertical)

15.2.3.3. example

15.2.4. flow unit (customer)

15.2.5. flowrate / throughput (Cust/h)

15.2.5.1. flow unit / time

15.2.6. flow time

15.2.6.1. time from beginning to end

15.2.7. inventory

15.2.7.1. number of flow unit at a given moment

15.2.8. processing time / Activity time

15.2.8.1. Time / flow Unit

15.2.9. capacity

15.2.9.1. 1/processing time

15.2.9.1.1. unit/sec

15.2.9.2. m = number of parallel workers

15.2.9.2.1. capacity = m/procesing time

15.2.10. bottle neck

15.2.10.1. process with lowest capacity

15.2.11. process capacity

15.2.11.1. capacity of bottleneck

15.2.11.1.1. Min(all capacities)

15.2.12. flow rate

15.2.12.1. Min ( demand rate, process capacity)

15.2.12.1.1. insufficient demand

15.2.13. utilization

15.2.13.1. Flow rate / capacity

15.2.14. process flow diagramm

15.2.14.1. triangle

15.2.14.1.1. waiting

15.2.14.2. box

15.2.14.2.1. processing time

15.2.14.3. multiple flow units

15.2.14.3.1. implied utilization

15.2.14.3.3. generic flow unit ("Minute work")

15.2.14.3.4. process with attrition loss

15.2.15. cycle time

15.2.15.1. CT

15.2.15.2. 1/flow rate

15.2.15.3. direct labor content

15.2.15.3.1. sum all processing times

15.2.15.4. direct idle time

15.2.15.4.1. sum (all CT - p)

15.2.16. avg labor utilization

15.2.16.1. labor content / (labor content + direct idle time)

15.2.16.1.1. = eff. process time / total time paying

15.2.17. cost of direct labor = total wages per unit of time / flow rate per unit of time

15.2.18. example

15.2.19. Little's Law

15.2.19.1. Inventory (I)[Units] = Flow Rate (R) [Units/h] * Flow Time (T) [h]

15.2.19.2. Weakness: Averages

15.2.19.3. inventory turns

15.2.19.3.1. 1/T = COGS / Inventory

15.2.19.3.2. per unit inventory cost = Annual Inventory / Inventory turns (per year)

15.2.19.4. buffer or suffer

15.2.19.4.1. make to stock approach

15.2.19.4.2. make to order approach

15.2.20. Five reasons for inventory

15.2.20.1. pipeline inventory

15.2.20.1.1. you wil need some minimum inventory because of the flow time >0

15.2.20.2. seasonal inventory

15.2.20.2.1. driven by seasonal variation in demand and constant capacity

15.2.20.3. cycle inventory

15.2.20.3.1. economies of scale in production (purchasing drinks)

15.2.20.4. safety inventory

15.2.20.4.1. buffer against demand (Mc Donalds hamburgers)

15.2.20.5. decoupling inventory / buffers

15.2.20.5.1. buffers between several internal steps

15.2.20.6. where there is inventory there are supply / demand missmatches

## 16. definition

### 16.1. process management

16.1.1. doing things repeatedly

16.1.2. != project management

### 16.2. quartile analysis

16.2.1. compare top 25% with bottom 25% processing times

### 16.3. Flow Time Efficiency (or %VAT)

16.3.1. (Total value add time of a unit) / (Totla time a unit is in the process)