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. Measurements

2.1. four dimensions

2.1.1. Productivity / costs efficiency

2.1.2. variety heterogeneity customer preferences

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

2.1.4. time Responsiveness to demand tradeoff responsiveness vs. labor productivy efficient forntier line where industry has current froniter

2.2. performence measurements

2.2.1. cumulative inflow

2.2.2. cumulative outflow

2.2.3. cumulative flow flow time ( horizontal) Inventory (vertical) example

2.2.4. flow unit (customer)

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

2.2.6. flow time time from beginning to end

2.2.7. inventory number of flow unit at a given moment

2.2.8. processing time / Activity time Time / flow Unit

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

2.2.10. bottle neck process with lowest capacity

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

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

2.2.13. utilization Flow rate / capacity

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

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

2.2.16. avg labor utilization labor content / (labor content + direct idle time) = eff. process time / total time paying

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

2.2.18. example

2.2.19. 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

2.2.20. 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

3. definition

3.1. process management

3.1.1. doing things repeatedly

3.1.2. != project management

3.2. quartile analysis

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

3.3. Flow Time Efficiency (or %VAT)

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

4. productivty

4.1. Units Output Produced / Input Used

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

4.1.2. output: pruductive time

4.1.3. input: total time

4.2. Multifactor productivity

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

4.3. kpi

4.3.1. key performance indicator

4.3.2. kpi tree PROFIT minus

4.3.3. break even point

4.4. Labor Productivity

4.4.1. Revenue / Labor Costs

5. waste

5.1. 7 sources of waste

5.1.1. overproduction match supply with demand

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

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

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

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

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

5.1.7. waiting understand the drivers of waiting

5.1.8. intellect intelligence of workers

6. overall equipment effectiveness (OEE)

6.1. Downtime Losses

6.1.1. Availability Rate

6.2. Speed Losses

6.2.1. Performance Rate

6.3. Quality Losses

6.3.1. Quality Rate

6.4. Overall people effectiveness

6.4.1. 100% total paid time

7. Takt Time

7.1. Time / #Units

7.1.1. sec/unit

7.2. determine

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

7.2.2. 2. Make sure that all tasks are assigned

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

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. Set Up

9.1. batch

9.1.1. number of units between setups

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

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

9.2. SMED

9.2.1. SIngle Minute Exchange of Die 6 stage approach

9.2.2. every set up can be broken up into internal external set up that can be done in parallel before machine is standing still

10. mixed Module Production

10.1. large badges leads to large inventories

10.2. Heijunka

10.3. calculate batch size

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

11. Variability of Demand / pooling

11.1. statistics

11.1.1. mean mü

11.1.2. standard deviation sigma s

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

11.1.4. 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.

11.1.5. 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

11.2. pooling

11.3. fragment

11.4. Delayed Differntiation

11.4.1. Modular Production

11.5. overwhelming of choice

11.6. priority rules

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

11.7. sequence based on importance

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

11.8. appointments

11.8.1. problem no shows solves wrong problem: shifts queue

11.9. waiting Problems, loss problems

11.9.1. Customers leaving while waiting.

11.9.2. due to limited buffers

11.9.3. impatient customers

11.9.4. Loss analyzation Percentage of Lost Customers r=p/a (p=processing time, a=interarrival time)

11.10. Implied Utilization with Loss

11.10.1. 1. Calculate Demand D begin from the end of process

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

12. Flexibilty

12.1. No Flexibility / Full Flexibility / Partial Flexibility

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

13. Process Mapping

13.1. Yves Pigneur

13.1.1. Customer Actions

13.1.2. Onstage Actions

13.1.3. Backstage Actions

13.2. Value Stream Mapping

13.3. improvements

14. Quality

14.1. yield

14.1.1. percentage according to specification

14.1.2. 1 - defect probabilty

14.2. Swiss Cheese Model

14.2.1. Redundency

14.2.2. ALL things have to go wrong

14.3. Defects

14.3.1. Scrap

14.3.2. Rework

14.3.3. Cost of Defects CATCH defects BEFORE bottleneck costs before bottleneck: costs of goods used costs after bottleneck: opportunity costs, full price charged!

14.3.4. 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!

15. Implementations

15.1. Six Sigma

15.1.1. ppm Parts Per Million Defects Per Million Parts

15.1.2. Variation assignable cause Statistical Process Control common cause

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

15.1.4. Manage Quality

15.2. The Three Enemies of Operatons

15.3. Toyota Production System

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

15.3.2. Jidoka Detect / Alert / Stop Andon Cord

15.3.3. Manage Quality

15.3.4. Kaizen Root Cause Analysis Ishikawa Diagram 5 Whys Pareto Chart interplay of reality and models, iterative problem solving

15.4. pull system / kanban

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

16. authors

16.1. original mindmap

16.1.1. @davidbaer