Public Operations Management

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Public Operations Management by Mind Map: Public Operations Management

1. The Waiting Time Formula

1.1. Waiting Time Formula for Multiple, Parallel Resources

1.1.1. U=FR/CAP (1/a)/(m/p)=p/(a*m)

1.2. summary

1.3. CV = Std Dev / avg

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 Parts Per Million Defects Per Million Parts

2.1.2. Variation assignable cause Statistical Process Control common cause

2.1.3. Process Capability / Capability Score Cp = (USL - LSL)/6*std USL = Upper Specification Limit LSL = Lower Specification Limit NORMDIST in excel

2.1.4. Manage Quality

2.2. The Three Enemies of Operatons

2.3. Toyota Production System

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

2.3.2. Jidoka Detect / Alert / Stop Andon Cord

2.3.3. Manage Quality

2.3.4. Kaizen Root Cause Analysis Ishikawa Diagram 5 Whys Pareto Chart 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 mü

3.1.2. standard deviation sigma s

3.1.3. Coefficient of Variation CV=mü/s doubling size leads to 2^(1/2) CV

3.1.4. positive correletion 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 a= average inter arrival time CV = Coefficent of Variation CVa = St-Dev ( inter arrival times)/(Avg(inter arrival times) Poisson ditribution CVa = 1 Constant hazard times (no memory) Exponential inter arrivals Exponential inter-arrivals

3.2. pooling

3.3. fragment

3.4. Delayed Differntiation

3.4.1. Modular Production

3.5. overwhelming of choice

3.6. priority rules

3.6.1. FCFS = FIFO easy to implement fairness lowest variance of waiting time

3.7. sequence based on importance

3.7.1. shortest Processing Time Rule (SPT) minimize average wiating time Hard to hav true processing time

3.8. appointments

3.8.1. problem no shows 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 Percentage of Lost Customers r=p/a (p=processing time, a=interarrival time)

3.10. Implied Utilization with Loss

3.10.1. 1. Calculate Demand D 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 CATCH defects BEFORE bottleneck costs before bottleneck: costs of goods used costs after bottleneck: opportunity costs, full price charged!

4.3.4. Buffer or Suffer Starved vs. Blocked Starve = nothing recieved from up stream Blocked = no place to put downstream Buffers can reduce variability in Quality (prevent starving or blocking) Toyota: Inventory prevents quality problems from being reocognized expose rocks!

5. Process Mapping

5.1. Yves Pigneur

5.1.1. Customer Actions

5.1.2. Onstage Actions

5.1.3. Backstage Actions

5.2. Value Stream Mapping

5.3. improvements

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

7.1. large badges leads to large inventories

7.2. Heijunka

7.3. calculate batch size

7.3.1. B/(S+B*p) = Demand or Capacity (Units/t) B= Batch Size S= Total Setup Time p = Processing Time

8. Variety

8.1. Forms

8.1.1. Fit Based Variety Horizontal Variety distribution examples T-shirts, Shoes Opening Hours Departure Times for planes, trains

8.1.2. Perfomance Based Variety Vertical Customers differntiate on quality distribution examples more features better quality

8.1.3. Taste Based Variety customers differ in their preferences or taste distribution examples taste of food art colors

8.2. economic motives

8.2.1. performance based Segment Market

8.2.2. Taste Based, Fit Based Cater to heterogeneous customers

8.2.3. Variety seeking customers example food, lunch

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

9. Takt Time

9.1. Time / #Units

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 6 stage approach

11.2.2. every set up can be broken up into internal external 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 PROFIT 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 match supply with demand

13.1.2. transportation relocate processes, then introduce standard sequences for transportation

13.1.3. rework repetion or correction analyse and solve root causes of rework -> more quality in module

13.1.4. over processing provide clear, customer-driven standards for every process

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

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

13.1.7. waiting understand the drivers of waiting

13.1.8. intellect intelligence of workers

14. authors

14.1. original mindmap

14.1.1. @davidbaer

15. Measurements

15.1. four dimensions

15.1.1. Productivity / costs efficiency

15.1.2. variety heterogeneity customer preferences

15.1.3. quality product quality how good process quality as good as promised

15.1.4. time Responsiveness to demand tradeoff responsiveness vs. labor productivy efficient forntier 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 flow time ( horizontal) Inventory (vertical) example

15.2.4. flow unit (customer)

15.2.5. flowrate / throughput (Cust/h) flow unit / time

15.2.6. flow time time from beginning to end

15.2.7. inventory number of flow unit at a given moment

15.2.8. processing time / Activity time Time / flow Unit

15.2.9. capacity 1/processing time unit/sec m = number of parallel workers capacity = m/procesing time

15.2.10. bottle neck process with lowest capacity

15.2.11. process capacity capacity of bottleneck Min(all capacities)

15.2.12. flow rate Min ( demand rate, process capacity) insufficient demand

15.2.13. utilization Flow rate / capacity

15.2.14. process flow diagramm triangle waiting box processing time multiple flow units implied utilization add up generic flow unit ("Minute work") process with attrition loss

15.2.15. cycle time CT 1/flow rate direct labor content sum all processing times direct idle time sum (all CT - p)

15.2.16. avg labor utilization labor content / (labor content + direct idle time) = 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 Inventory (I)[Units] = Flow Rate (R) [Units/h] * Flow Time (T) [h] Weakness: Averages inventory turns 1/T = COGS / Inventory per unit inventory cost = Annual Inventory / Inventory turns (per year) buffer or suffer make to stock approach make to order approach

15.2.20. Five reasons for inventory pipeline inventory you wil need some minimum inventory because of the flow time >0 seasonal inventory driven by seasonal variation in demand and constant capacity cycle inventory economies of scale in production (purchasing drinks) safety inventory buffer against demand (Mc Donalds hamburgers) decoupling inventory / buffers buffers between several internal steps 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)