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)
1.2. 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
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
2.2. 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
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
3.2. 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
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
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)
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. 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
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. tradeoff
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
15.2.14.3.2. add up
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)