Lec 9 Work Schedule & Control (Lean Concepts
Lec 10 Rapid Changeover (setup)/SMED
Lec 13 Quality Systems
Tools To minimize sources of variability: 5S, Visual controls &
Changeover: Needed when products
Bad Product: Reduces Prod. capacity, inc prod-related cost, inc
graphical work instruc., Work, tool, & equipment
change
production variability (WIP, FT, & wait time), inc order lead time,
standardization, Production rate leveling (Takt Time), WIP
smaller batch sizes = more setups
wastes material/energy
control (CONWIP), Heijunka, Pull Systems Demand levelling
Issues of Changeover: during setup ->
1-10-100 Rule: as a product or service moves through production
5S - Sort (get rid of clutter), Straighten/Stabilize (organize),
not producing, setup delay intros
system/supply chain->cost of correcting an error multiplies by 10
Shine (system cleaning), Standardize (work standards),
variability (bad), quality costs, constrain for each stage of processing
Sustain
sequence of producing reducing
Process Capability: process capability studies, tolerance analysis
One Piece Flow (Continuous) - move through line one at a time,
flexibility to meet demand, larger lots to Defect Detection: control charts, sampling (SPC/SQC)
no process batching
try to reduce setup -> larger inventory
Defect Correction: brainstorming, pareto charts, cause & effect
Priorities for 5s Sorting: Low(<1/yr), Avg(1/month,1/week), High
produce in large batches to spread out diagrams, scatter diagrams, redesign, design of experiments, line
(1/day)
setup costs & reduce loss of capacity
stop on error, correct your own errors
Red Tag Technique for 5S Sorting: red labels to label items not
ELS: economic lot size/batch size;
Defect Prevention: Zero Defect Quality (ZDQ) & Poka Yoke (foolneeded, after a week toss
suboptimal bc min cost occurs when no proofing), supplier certification / management, setup improvement,
Metrics for 5s Straighten: Time saved in Searching/Material
setup bc no holding cost
housekeeping (5S), training
Handling
S= Setup cost>0
Continuous Improvement: TQM, Kaizen, Six Sigma
Requirements for 5s Sustain: Top Mgmt support, accountability,
D= Avg Demand Rate
Goal alignment: production responsibility for quality, Overall
performance measurement/rewards, regular audit
i= carry rate %
Equipment Effectiveness (OEE) metrics, quality circles, quality
5s Advantages: less time searching for items, waste reduced,
c= unit prod. cost
visibility
better-maintained equipment, improved motivation/safety,
Changeover steps: Preparation (ensure Total Quality Management (TQM): sustaining quality gains & gradual
better utilization
working tools & proper location, materials ongoing improvements by eliminating defects as they occur
5s is a STARTING POINT for Lean Improvements
are available), Extraction/Mounting
QS 9000: Supplier management, Materials control, Quality planning,
Visual Controls: means, devices, or mechanisms designed to
(removal of tools/materials after batch is
Process control\
manage or control the operations (processes) to make
completed and replace tools/materials for Kaizen Blitz: Area- or process-focused rapid improvement activity
problems visible, display progress status, provide
new), Establish Control Settings
designed to produce significant improvements within a short time
instruction/information/immediate feedback. Increases
(settings prior to production
period
efficiency, effectiveness, and coordination (Ex: Color-coded
run/calibrations), First Run Capability
Six Sigma: assumed to produce long-term defect levels below 3.4
equipment, painted floor areas, status boards, visual instructions)
(any adjustments after trial piece
defects per million opportunities (DPMO); improve quality, DMAIC
Push System: system schedules the release of work based on
produced)
Zero Defect Quality (ZDO): Inspect for defects at each step, rather
demand regardless of current loading of system.
External: Production Time
than periodic inspect stations
Pull System (Kanban): authorizes release of work based on WIP
Internal: Production stopped
Types of Inspection: Judgement (end of line), Informative (uses data,
at each station, controls Station WIP, Closed queueing
Techniques: internal to external, staged
SPC/SQC, done intermediate), Source (each stage at source, not best
systems w/ blocking, reduces inventory, WIP, & batching but
tooling, eliminate “adjustments” through
info)
leads to longer FT, and low utilization
visual controls/ ratchets/automation,
Point of Origin Inspect: passive methods (not high tech,parts
Production leveling involves: Takt Time (Pace prod. rate to
Parallel processing (more people working designed w odd shape) proximity/property sensors, vision systems
demand, not capacity), CONWIP (Pace sys, loading), Heijunka
on same thing), Automation, Standardize
(Pace prod. cycle to demand), demand leveling ( taking action to
Lec 14 TPM/OEE
Design
reduce demand changes)
Total Productive Maintenance (TPM): maintenance/reliability mgmt
Things to look for: no advance prep,
Takt Time: sets pace of productions, heartbeat of lean system
system forms the foundation, emphasis on autonomous maintenance,
movement after machine stopped,
(available production time/rate of customer demand)
improved training & higher skill level, performance
mistakes, excessive removal
Heijunka: scheduling of orders, level by volume/type/product,
monitoring/benchmarking
time/adjustments, many movements
requires reducing setup times/costs
Features: Goal alignment through OEE (overall equipm. effectiveness),
during setup
CONWIP: release of work in to the sys., real time control
planned maintenance, maintainability, team based approach,
Cost/Changeover: Line
strategy, tries to maintain constant WIP, closed queuing
speed(parts/min)*Contribution($/part)*A continuous improvement
network
Big “6” Productivitiy losses: Breakdowns, Setup/adjustment losses,
vg CO Time
Demand Leveling: focus on reduction demand (order arrival)
Idling/minor stops, Speed losses, defects/rework, Startup / yield losses
Annual Cost of CO:
variability, can improve predictability/demand visibility
Process Design Principles: minimize distance, one piece flow/no
Cost/Changeover*Changeover/yr
batching, minimize equipment size, Poka Yoke/ZQD, Linked
Lec 17 Variability & Factor Physics
Cell/UShape
Controllable Variation: Caused by decisions (product mix, setup/changeover times, batch size)
Supplier Management: Lean suppliers
Random Variation: caused by uncontrolled factors (demand, natural processing time variation, machine
Lean procurement: automated, integrated purchasing
breakdown)
Supplier commo./collab.: demand visibility to prevent bull-whip issues
Why do we reduce variability?: more variability longer wait/FT,reduced system capacity, need for more
Lean warehousing: automation/integration; eliminate defects or return
inventory. customers want faster responsetime, consistency, lower costs
shipments, excess inventory, extra space to reduce warehousing
Factory Physics: understanding impact of variability in a sequence of operations (bottlenecks),
efficiency, Excess motion/ handling, Inefficiency/unnecessary, wait
approximate formula calc for system metrics
times
Flow Variability: variability of one process affects other downstream processes (upstream if blocking)
Serial Production line: departures become arrivals to next, only first station sees arrival variability undistorted OEE= Availability*Performace*Quality
Availability = Actual Operating Time/Planned Operating Time = Uptime
Downstream stations see combo of arrival variability & upstrea process variability
Performance = Actual Production/(Expected Production over the Actual
FV: If process is highly utilized, output (departure) variability (CVd) will reflect the process's inherent
Operating Time)
variability (CVe), and arrival variability (CVa) has little impact. low utilization, its output variability (CVd) will
Quality = Good Output/Actual Output = 1 – (Scrap & Rework / Actual
largely mirror its arrival rate variability (CVa)
Output)
Flow Variability Behavior: processes that are both highly loaded AND highly variable have a much greater
Lec 16 Value Stream & Process Mapping
impact on system variability (and performance) in general and particularly if occur earlier rather than later in
VSM: used to analyze flow of materials and info currently required
the production or service process flow
to bring product/service to a consumer, emphasis on wait/transpo
Factory Phys Laws:
times (NVA), useful for high volume; rough sketch tool (use
Variability: Inc. variability degrades performance system)
Process map for detail)
Capacity: system with any variability, processes will release work at avg rate that is strictly less than their avg
VSM Method: Identify main problems, set process boundaries, do
capacity
Gemba walk (waste identification exercise), document in VSM
Utilization: If station inc utilization w/o other changes, avg WIP & CT will inc in a nonlinear fashion
Process Maps: show time at each step, quality rates, setup times,
Variability Placement: variability early in routing inc CT more than equivalent variability later in routing
merging/assembly operations
Variability Buffering (or "you can pay now or pay later"). Variability in a production system is buffered by
Spaghetti Diagram: visual description of distances/paths for a
combinations of Inventory, Capacity, Time
process to see waste in transpo/steps
Lead Time:The manufacturing LT for routing is an inc function of both the mean and std dev of the CT of the
routing
Rework/Scrap: For given throughput level, rework inc both the mean and Std Dev of the CTof a process.
Batching: Inc variability and usually mean processing times as well.
Setup time: Inc variability and mean processing times.
Reduce process variability: Automation, better tools, fixtures, layout, training
Reduce arrival variability: Work with customers/suppliers to smooth arrivals, Improved demand forecasting
and info sharing
Improve maintenance and maintainability: Increase TTF, reduce TTR, and reduce variability
Improve Process Flexibility: Eliminate batching(move to single piece flow), Reduce/eliminate
of both planned maintenance rather than reactive maintenance).
setup/changeover
Implement variability pooling: Shared queue Material/parts standardization – safety stock
aggregation
Lec 18 Material Handling
Lec 19 Verification & Validation
80-85% of items total time in system is on material handling or waiting. Verification - process of determining the model operates as intended / specified.
Modeling Types:
Validation - process of developing an acceptable level of confidence that inferences drawn from the performance of
Transporters: Free Space (Unconstrained shortest distance movement; the model are correct and apply to the real world system.
e.g., hand trucks, people in open layouts), Network (Transporter must Verification Activities: Incorporate outside “doubters”, model walkthroughs, Perform test runs to check for output
follow aisles, roads or other constrained paths. e.g., Forklifts), Zone
correctness (Boundary conditions – resources, buffer sizes go to 0, etc.; Incremental inc or decn arrival / service
controlled (Network with controlled zones limiting how many
rates, buffer sizes, number of resources, failure rates have appropriate effects)
transporters can be on a segment of the network at a time. e.g., trains, Watch for: Do queues or WIP continue to grow overtime?; Are flowtimes / wait times absurdly long? Look at max
Automated Guided Vehicles (AGVs)
values in report (same reasons as above); Utilizations absurdly low or zero?; No or few entities leaving the system?
Conveyors: Accumulating (when blocked, items pack together/
Or leaving along a particular path?; Incorrect processing times, entities leaving with resources, incorrect logic
distance between items gets smaller ), Non-accumulating (fixed
expressions in holds, or decision/routing blocks.
spacing between items)
Verification Tools: Use numeric displays/ plots to track performance metrics, Use reports, pivot grid, responses to
Transporters: are RESOURCES; if not at station, then more wait
review end of simulation statistics; SIMIO: Model Trace, Breakpoints, Watches
time/travel time; must define initial position, selection rules; consider
COMMON ERRORS: Data Parameter, Math expression, Intilization, Statistic recording, units of measures,
delays/load/unload times
Blockages/ deadlooks, Flow control/connection, Failure to release/seize appropriate resources, Failure to dispose
Networks: Congestion on network grid combined with control logic
(entities never leave system), Overwriting State variable/entity attribute values by accident, Conceptual errors
results in a condition where no transporter can move on all or part of a /misunderstood operation)
network; Fix: Fewer carts to reduce # of conflicts, Avoid bidirectional
Validation Types:
links/spurs, or allow passing on the link, Add bypasses, More complex
Conceptual Validity: Does the model structure represent the real world system (logical premises are
control logic to detect and "break" deadlocks
correct/rational)?
Conveyors: the TRANSPORTER; fixed path device
Operational Validity: model's behavioral data characteristic of real world system's behavioral data? empirically
For accumulating conveyors:
correct?
Conveyor keeps moving even when parts are loading / unloading
Believability (Face Validity): Do the ultimate users (decision makers) of model have confidence in results?
Entities attempting to access the conveyor are held in a queue until Validation Tests for Resonableness:
the required amount of unoccupied space is available, The conveyor
Continuity: Small changes in parameters yield small appropriate changes in outputs. EX: incr arrival rate ->
is blocked (accumulates) upstream until this space is free
longer avg queue lengths.
At the destination station, the entity stops and blocks the conveyor
Consistency:Similar runs of the same model should yield similar results.
until it exits the conveyor.
Degeneracy: Removal of resources should be reflected in model results. EX: dec machine capacity from 2 to 1 ->
Accumulating entities pack together more tightly then when moving
reduced throughput, longer lines
For non-accumulating:
Absurd Conditions: no absurd conditions like zero flow times, neg weights/ lot sizes, “infinite” (very large) values
Whole conveyor generally stops when loading or unloading (must be
for flow times, queue sizes
an empty "cell" in front of station to get on conveyor)
Tests for Structure & Data:
Synchronizing Converyors: •When one conveyor flows into another, or
Face validity: Ask persons familiar and knowledgeable w/ system to judge model’s behavior. Often used w/
entities are redirected from conveyor onto another, the entity must get
animation.
space on the second conveyor before releasing space on the first
Parameters & Relationships Verification. Conduct statistical analysis on input data (input data analysis).
conveyor. Otherwise, can build up an infinite queue in the space
Sensitivity Analysis: How sensitive are models’ outputs to small changes in inputs? If small changes in input
between two conveyors!
values yields change in decision on what alternative to adopt, may call into question models usefulness.
Structural and Boundary Verification: Does model structure obviously contradict reality?
Tests of Model Behavior (LEC 19):
Behavioral Comparison. Compare simulation model output to real world output for similar conditions. ( ChiSquare/Goodness of Fit tests, etc)
Symptom Generation: Does the simulation model recreate difficulties occurring in the real system? (long lines,
blocking, etc)
Behavior Anomaly: Does the model recreate flukes (anomalies) of the physical system for certain inputs?
Behavior Prediction: helpful in building confidence in the model. Use the simulation to predict behavior /
performance of the system for the near future
VSM Steps
↑
* Draw
this
example
Input @Good
.
.
Input @Bad
2