operational excellence -...
TRANSCRIPT
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Operational Excellence Variability & cycle time reduction Prof.dr.ir. Marcel van Assen
www.vanassen.info
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Basic Measures
• Throughput (TH): for a line, throughput is the average quantity of
good (non-defective) parts produced per unit time.
• Work in Process (WIP): inventory between the start and
endpoints of a product routing.
• Cycle Time (CT): time between release of the job at the
beginning of the routing until it reaches an inventory point at the end of the routing.
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Cycle Time
•Definition (Station Cycle Time): The average cycle
time at a station is made up of the following components:
• cycle time = move time + queue time + setup time +
process time + wait-to-batch time +
wait-in-batch time + wait-to-match time
•Definition (Line Cycle Time): The average cycle time
in a line is equal to the sum of the cycle times at the
individual stations less any time that overlaps two or more
stations.
delay times
typically
make up
90% of CT
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Reducing Queue Delay
CTq = V U t
2
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ea cc
u
u
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Reduce Variability
• failures
• setups
• uneven arrivals, etc.
Reduce Utilization
• arrival rate (yield, rework, etc.)
• process rate (speed, time,
availability, etc)
Theme’s
1 What is Operational Excellence
2 Impact of variability on Operational Excellence
3 Lead time reduction as the ultimate internal
performance indicator
4 Sandcone model: continuously building on the
fundaments
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What is utilization? In relation with lead time (cycle time)?
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Cycle
tim
e
Utilization
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A Manufacturing Law
Little's Law: The fundamental relation between WIP, CT,
and TH over the long-term is:
Insights: Fundamental relationship
Simple units transformation
Definition of cycle time (CT = WIP/TH)
hrhr
partsparts
Little’s Law: TH = WIP/CT, so same throughput can be obtained
with large WIP, long CT or small WIP, short CT. The difference?
C TT HW I P
High
Profitability
Low
Costs
Low Unit
Costs
High
Throughput
Less
Variability
High
Utilization
Low
Inventory
Quality
Product
High
Sales
Many
products
Fast
Response
More
Variability
High
Inventory
Low
Utilization
Short
Cycle Times
High Customer
Service
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Hierarchy of (conflicting) operational objectives
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Sigma = a measure for the extent to which a process is in control
• Sigma () is een statistical measure for the distribution of a
process
• A Six Sigma-level indicate that 99,99966% of all units (parts) are
within the specifications limits, taking into account a drift of max.
1,5
• Six Sigma is also a structured method to eliminate variability of a
process in order to increase process control
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DPMO % fout % goed niveau
3,4 0,00034% 99,99966% 6
233 0,02330% 99,97670% 5
6.210 0,62100% 99,37900% 4
66.807 6,68070% 93,31930% 3
308.537 30,85370% 69,14630% 2
690.000 69,00000% 31,00000% 1
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Sigma = maat voor beheerst proces
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Sigma-niveau Aantal afwijkingen per mil-joen nog steeds toelaatbaar
(DPMO)
1 690.000
2 308.537
3 66.807
4 6.210
5 233
6 3,4
The relationship between utilization and lead time
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The relationship between utilization and lead time
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Impact of arrival variability on queues and cycle times
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3
2
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In systeem A arriveren
klanten met een
aankomstintensiteit van
gemiddeld 12 klanten
per uur, maar met hoge
mate van variatie
tijd
tijd
4
3
2
1
tijd
Aantal klanten
in systeem B
B
A
Aankomsten van klanten in de loop van de tijd
Aantal klanten
in systeem A
Aankomsten van klanten in de loop van de tijd
In systeem B arriveren
klanten met een
aankomstintensiteit van
gemiddeld 12 klanten
per uur, maar met lage
mate van variatie
0 5 10 15 20 25 30 35 40 45 50 55 60
0 5 10 15 20 25 30 35 40 45 50 55 60
tijd
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Impact of variability in production systems
Variability always has a negative impact on the performances of a production system
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Influence of Variability
Variability Law: Increasing variability always
degrades the performance of a production
system.
Examples: • process time variability pushes best case toward worst case
• higher demand variability requires more safety stock for
same level of customer service
• higher cycle time variability requires longer lead time quotes
to attain same level of on-time delivery
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Variability Buffering
Buffering Law: Systems with variability must be
buffered by some combination of:
1. inventory
2. capacity
3. time.
Interpretation: If you cannot pay to reduce variability,
you will pay in terms of high WIP, under-utilized capacity,
or reduced customer service (i.e., lost sales, long lead
times, and/or late deliveries).
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Variability Buffering Examples •Ballpoint Pens:
can’t buffer with time (who will backorder a cheap pen?) can’t buffer with capacity (too expensive, and slow) must buffer with inventory
•Ambulance Service: can’t buffer with inventory (stock of emergency services?) can’t buffer with time (violates strategic objectives) must buffer with capacity
•Organ Transplants: can’t buffer with WIP (perishable) can’t buffer with capacity (ethically anyway) must buffer with time
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Buffer Flexibility
• Buffer Flexibility Corollary: Flexibility reduces the amount
of variability buffering required in a production system.
• Examples: Flexible Capacity: cross-trained workers
Flexible Inventory: generic stock (e.g., assemble to order)
Flexible Time: variable lead time quotes
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Flexibility: the effect of cross-skilled teams
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Flexibility: the effect of using floaters
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The use of floaters and cross skilled teams to structurally hedge for uncertainty
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Basic Variability Takeaways
• Variability Measures: CV of effective process times CV of interarrival times
• Components of Process Variability failures setups many others - deflate capacity and inflate variability long infrequent disruptions worse than short frequent ones
• Consequences of Variability: variability causes congestion (i.e., WIP/CT inflation) variability propagates variability and utilization interact pooled variability less destructive than individual variability
Assignments of the workgroups during these days
• Analysis and redesign of your real-life process
1. Discuss eachother ‘problem process’ with the help of internal and external developments regarding that process
2. Map the process on brown paper c) Describe the process with the help of the process mapping method d) Analyze the process (use real life data such as times, variances, TBV’s,
etc.) e) Analyze bottlenecks
3. Redesign the process on brown paper f) Generate possible solutions and rank them g) Choose and redesign the process accordingly
4. Evaluate this group-based analysis and redesign trajectory:
what was good about it and what went wrong?
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Theme’s
1 What is Operational Excellence
2 Impact of variability on Operational Excellence
3 Lead time reduction as the ultimate internal
performance indicator
4 Sandcone model: continuosly building on the
fundaments
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The positive influence of short lead times
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Delivery reliability: Negotiating longer, more certain (?) lead times versus lead time reduction
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Dupont scheme
Theme’s
1 What is Operational Excellence
2 Impact of variability on Operational Excellence
3 Lead time reduction as the ultimate internal
performance indicator
4 Sandcone model: continuosly building on the
fundaments
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Build continuously on the fundaments Sandcone-model: from effective to efficient
Sustainable efficiency is only gained as a cumulative result of
improvements on quality, reliability and flexibility.
AND IN THAT ORDER!
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r = 1/3 1- p t = 1
p
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Processes & quality Rework in a line
p = 1 – y = yiled loss
Processes & quality Rework in a line
2/3 1- p
2/3
p
1 2/3
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Redesigning an production organization via the PBOI
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Process mapping & Rasci-model
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Effective and efficient planning and control