Seminar_1_WIP_TP_CycleTime

Seminar_1_WIP_TP_CycleTime

IE450 Models Relating Cycle-time, Throughput, WIP and Batch Sizes Planning manufacturing capacity Dr. R. A. Wysk Learning Objectives To be able to name the most important factors that contribute to the increase in the cycle time of a production system. To be able to explain the Littles Law and its application to the operations of a production system. To be able to explain the fundamental relationship between resource utilization, cycle time and process and arrival variability in a production system. Any Production System Input Resources

WIP Output Any Production System Output Input WIP Input = Output [ defects] (1st Law of Factory Physics) WIP - Work-In-Process Idle time - % of time a resource is not working Any Production System Output Input Throughput the average output per unit time (a rate) Lead time the time needed to process a part through a facility Cycle time, flow time or sojourn time the average time from release of a job to

completion Relating Throughput and WIP One unit in WIP Lead time ? Idle time ? Throughput? Assuming each process takes 1 minute. More WIP (everything else the same) ... Lead time ? Idle time ? Throughput? More WIP - keep all machines busy ... Lead time ? Idle time ? Throughput? More WIP - diminishing returns ...

Lead time ? Idle time ? Throughput? Active Exercise: Diminishing return? At some point more WIP does not achieve anything except for longer lead times Take 3 minutes to complete the following task. Draw graphs relating WIP to throughput. Throughput Relating WIP and Throughput 100% What is the limiting throughput? WIP A very useful relationship Littles Law: WIP = (Throughput) x (Lead Time) Littles Law is a fundamental law of system dynamics Gives good results for a variety of scenarios

Throughput (Units/time). Example: A facility can produce 200 units per week, and the average lead time is 2 weeks. According to Littles law the average WIP = 200 x 2 = 400 units. Scenario 1: No Variability (Ideal World) Data and Calculations Planning Horizon 30 hours Processing time 8 hour/unit Inter-arrival time 10 hours Utilization 80% Average queue time 0 hours Average lead time 8 hours 1st part arrives 2nd part arrives

1st part processing 0 3rd part arrives 2nd part processing 8 10 1st part departs 3rd part processing 18 20 2nd part departs 28 30 3rd part departs

Scenario 2: Processing Variability SCENARIO Planning Horizon Processing time Inter-arrival time Utilization Average queue time Average lead time 1st part arrives 2nd part arrives Parts waiting in queue 2 same 12, 9, 3 Same average !!! same same 1 hour 9 hours

3rd part arrives 10 12 1st part departs 20 Same utilization but . . . More queue time 2nd part processing 1st part processing 0 1 30 hours 8 hour/unit 10 hours

80% 0 hours 8 hours 3 More lead time 21 24 2nd part departs 3rd part departs 30 Scenario 3: Arrival Variability SCENARIO Planning Horizon Processing time Inter-arrival time Utilization Average queue time

Average lead time 1st part arrives 1 30 hours 8 hour/unit 10 hours 80% 0 hours 8 hours 13.5 hours 2 same as 1 12, 9, 3 same same as 1 1 hours 9 hours 2nd part arrives 3

same as 1 same as 1 13.5, 6.5 Same average !!! same as 1 Again, same 0.5 hours 8.5 hours but . . . 6.5 hours utilization More queue time 3rd part arrives More lead time Part 3 waiting in queue 1st part processing

0 2nd part processing 8 10 1st part departs 13.5 20 2nd part departs 3rd part processing 21.5 29.5 3rd part departs

Scenario 4: Increased utilization SCENARIO Planning Horizon Processing time Inter-arrival time Utilization Average queue time Average lead time 1st part arrives 2nd part arrives 1 30 hours 8 hour/unit 10 hours 80% 0 hours 8 hours Parts waiting in queue 2

same as 1 12, 9, 3 same as 1 same as 1 1 hours 9 hours 3 same as 1 same as 1 13.5, 6.5 same as 1 0.5 hours 8.5 hours 3rd part arrives 4 same as 1 Larger (2) + 1 hour Batch Sizes same as 1 90%

2 hours 11 hours Increased utilization but more queue time 1st part processing 0 10 3 2nd part processing 13 1st part departs 20 23 2nd part

departs longer lead times 27 30 3rd part departs What would happen if the processing time variability is eliminated? Example Summary SCENARIO Planning Horizon Processing time Inter-arrival time Utilization Average queue time Average lead time 1 30 hours 8 hour/unit

10 hours 80% 0 hours 8 hours 2 same as 1 12, 9, 3 same as 1 same as 1 1 hours 9 hours 3 same as 1 same as 1 13.5, 6.5 same as 1 0.5 hours 8.5 hours 4 same as 1 (2) + 1 hour same as 1

90% 2 hours 11 hours Utilization alone is not sufficient to estimate the lead-time performance One must also consider the products arrival and processing variability. A mathematical model is needed to study the system. Questions??

Recently Viewed Presentations

  • What a Wonderful World - DBSA San Diego

    What a Wonderful World - DBSA San Diego

    I see trees of green Red roses too I see them bloom And I think to myself What a wonderful world I see skies of blue And clouds of white The bright blessed day The dark sacred good night And...
  • A Back-End Design Flow for Single Chip Radios

    A Back-End Design Flow for Single Chip Radios

    Wm. Rhett Davis Last modified by: Ken Goldberg Created Date: 1/27/1999 7:25:34 AM Document presentation format: Custom Company: Slartibartfast Bistromathics Other titles: Times New Roman Arial Default Design 1_Default Design Statistical Adhesion Analysis of Sandwich Creme Cookies Odessa Goldberg April,...
  • The Transition from Acute to Chronic Pain

    The Transition from Acute to Chronic Pain

    The relative paucity of data on the relationship between preoperative opioid use and clinical outcomes in the Workers' compensation (WC) population necessitates further study of this unique population. ... Diffuse Noxious Inhibitory Control Definition.
  • Leveraging WPF in Windows Embedded Standard Code Name 'Quebec'

    Leveraging WPF in Windows Embedded Standard Code Name 'Quebec'

    Jukebox. The Silverlight Option. ... Analysis of target machine. Configuration of embedded run-time image. Building of embedded run-time image based on configuration. Online tweaking and testing of embedded run-time. Capture of final embedded run-time image to be deployed.
  • NANO WHISKERS - Ning

    NANO WHISKERS - Ning

    NANO WHISKERS General Definition WHISKERS- One of the long stiff tactile bristles or hairs that grow near the mouth and elsewhere on the head of most mammals METALLURGY Metal whiskering is a crystalline metallurgical phenomenon involving the spontaneous growth of...
  • NVM Set Endurance Group Management

    NVM Set Endurance Group Management

    Key risks and uncertainties include volatility in global economic conditions, business conditions and growth in the storage ecosystem, impact of competitive products and pricing, market acceptance and cost of commodity materials and specialized product components, actions by competitors, unexpected advances...
  • Information-Centric Networking & the  Architecture George C. Polyzos

    Information-Centric Networking & the Architecture George C. Polyzos

    Forwarding based on Bloom filter (called zFilter) that contains all the link IDs through which a packet has to travel
  • Closing the word gap: a whole-school approach  Oxford

    Closing the word gap: a whole-school approach Oxford

    OUP's research found that the word gap represents a significant and widespread challenge to both primary and secondary schools. Teachers surveyed believe the word gap is already large and increasing:. Primary respondents believe that 49% of year 1 children have...