SlideShare uma empresa Scribd logo
1 de 30
Operations Management

       Examples
Agenda
• Takira Motors: Creating Assembly and
  Process Chart
• Grouping Activities
• Bakery Example
Predecessor Components
Sequencing the operations
Assembly
chart
Cycle time is the time taken by the longest activity (here it is 7 min).
Throughput time is 46 min.
Assignment-2 Q2
    Cycle Time and Deciding on stages
    0.1 min.          1.0 min.        A Simple Precedence
        a                                  Diagram
                          b


            c                    d             e
 0.7 min.                 0.5 min.           0.2 min.
Arrange tasks shown in the above Figure into three workstations.
    Use a cycle time of 1.0 minute
    Assign tasks in order of the most number of followers
Raw                                              Pack              Finished
                  Bread Making       WIP
    Material                                       Cycle Time:           Goods
                   Cycle Time:
                                                   3/4Hr /100
                 1 Hr/100 loaves
                                                     loaves
packaging operation will be idle for quarter-hour periods (15 mins)


                    Bread Making
                     Cycle Time:           The packaging operation is
                   1 Hr/100 loaves         now the bottleneck
     Raw                                WIP           Pack              Finished
    Material                                       Cycle Time:           Goods
                                                   3/4Hr /100
                                                     loaves
                   Bread Making
                    Cycle Time:      If we operated the packaging operation
                  1 Hr/100 loaves    for three eight-hour shifts, and bread
                                     making for two shifts each day, then the
                                     daily capacity of each would be identical
                                     at 3,200 loaves a day (800 loafs x 4 shifts).
Packaging 3 shifts Bread making 2
   shifts: work-in-process inventory
• If both bread-making operations start at the same
  time, at the end of the first hour, then the first
  100 loaves move into packaging while the second
  100 loaves wait—work-in-process inventory.
• The waiting time for each 100-loaf batch
  increases until the baking is done at the end of
  the second shift.
• What is the time that the bread is sitting in work-
  in-process?
• Average wip during first two shifts, inventory
  builds from 0 to 1,200 loaves (1,600 x .75). (Or
  800 loaves x 2 shifts x .75 pkg.) = 1200/2
• Average wip during third shift is again 1200/2
• The overall average WIP over the 24-hour period
  is simply 600 loaves of bread.
• Packaging process in batches.
  1) 0.75 hour per 100 loaves
  2) Throughput rate of 133.3 loaves/hour
  (100/0.75)
• Little’s Law calculates the average time that
  loaves are in work-in-process is 4.5 hours (600
  loaves/133.33 loaves/hour)
Our Restaurant
• Assume that we have designed our buffet so customers
  take an average of 30 minutes to get their food and eat.
• Assume the restaurant has 40 tables. Each table can
  accommodate four people.
• The Cycle Time for the restaurant, when operating at
  capacity, is 0.75 minute (30 minutes/table ÷ 40 tables).
• The restaurant could handle 80 customer parties per hour
  (60 minutes ÷ 0.75 minute/party) is the capacity or
  customer parties per hour.
• Assume that our customers eat in groups (or customer
  parties) of two or three to a table.
• How many customers can the restaurant serve if the
  average customer party is 2.5?
• During lunch time, Customers arrive as per the schedule
  given.
Solution
• If the average customer party is 2.5 individuals,
  then the average seat utilization is 62.5 percent
  (2.5 seats/party ÷ 4 seats per table) when the
  restaurant is operating at capacity.
• The Cycle Time for the restaurant, when
  operating at capacity, is 0.75 minute (30
  minutes/table ÷ 40 tables).
• The restaurant could handle 80 customer parties
  per hour (60 minutes ÷ 0.75 minute/party) is the
  capacity or customer parties per hour.
• 20 tables empty during each 15-minute interval
During Period                             End of Period
             IN     Out       To Serve Tables                      Wait   Wait Time
11:30-11:45      15         0
11:45-12:00      35         0
12:00-12:15      30        15
12:15-12:30      15        20
12:30-12:45      10        20
12:45-1:00        5        20
1:00-1:15         0
1:15-1:30         0
Total Served
Formula      Data

  The first 15 tables empty after 30 mins.
  After that the restaurant can serve 20 tables every 15 minuets.
During Period                           End of Period
             IN     Out       To Serve Tables                    Wait
11:30-11:45      15         0        15                       15              0
11:45-12:00      35         0        50                       40             10
12:00-12:15      30        15        65                       40             25
12:15-12:30      15        20        60                       40             20
12:30-12:45      10        20
12:45-1:00        5        20
1:00-1:15         0
1:15-1:30         0
Total Served
Formula      Data

  . 12:00, we will have to keep 10 parties waiting: 15+35-40
  At
  Between 12:00 – 12:15, we have another 30 parties coming in and 15
  leaving. So we will have 15+35+30-40 -15 = 25 waiting
  12:15-12:30 we have 15 parties coming in and 20 leaving, so we will have
  15+35+30+15-40-15-20 = 20 waiting
During Period                         End of Period
               IN     Out       To Serve Tables                  Wait
11:30-11:45        15         0        15                     15        0
11:45-12:00        35         0        50                     40       10
12:00-12:15        30        15        65                     40       25
12:15-12:30        15        20        60                     40       20
12:30-12:45        10        20        50                     40       10
12:45-1:00          5        20        35                     35        0
1:00-1:15           0        20        15                     15        0
1:15-1:30           0        15         0                      0        0
Total Served                110
                      20 per 15 Table+IN- IF((To serve<40),To To serve-
Formula        Data mins        OUT       serve,40)              Table

  .
During Period                         End of Period
               IN     Out       To Serve Tables                  Wait     Wait Time
11:30-11:45        15         0        15                     15        0           0
11:45-12:00        35         0        50                     40       10         7.5
12:00-12:15        30        15        65                     40       25       18.75
12:15-12:30        15        20        60                     40       20          15
12:30-12:45        10        20        50                     40       10         7.5
12:45-1:00          5        20        35                     35        0           0
1:00-1:15           0        20        15                     15        0           0
1:15-1:30           0        15         0                      0        0           0
Total Served                110
                      20 per 15 Table+IN- IF((To serve<40),To To serve-
Formula        Data mins        OUT       serve,40)              Table    Wait*15/20

  Expected wait time = customers waiting x 0.75 minute
  Double up parties at the tables, thus getting a higher seat
  utilization. Might be the easiest solution to the problem. If 25
  out of the 40 tables were doubled up, our problem would be
  solved… (40+20)*2.5 > 40*4.. So?
Transit Bus Operation
• Logistics refers to the movement of things
  such as materials, people, or finished goods.
• A single bus takes exactly two hours to
  traverse the route during peak traffic.
• Wait time for the customer: Maximum is 2
  Minimum is 0, Average would be one hour.
• Bus capacity: seating 50 Standing 30
No of
           customers Av Time
8-9 AM           2,000      45
9-10 AM          4000       30
10-11 AM         6000       30
11-12 noon       5000       30
12-1 PM          4000       30
1-2 PM           3500       30
2-3 PM           3000       45
3-4 PM           3000       45
4-5 PM           3000       45
5-6 PM           4000       45
6-7 PM           3000       45
7-8 PM           1500       45


              How do we estimate number of passengers
              carried by a bus per trip? Per hour?
No of               Passenger Min No of Max No of
           customers Av Time Hours        Buses      Buses
8-9 AM           2,000      45       1500     18.75         30
9-10 AM          4000       30       2000        25         40
10-11 AM         6000       30       3000       37.5        60
11-12 noon       5000       30       2500     31.25         50
12-1 PM          4000       30       2000        25         40
1-2 PM           3500       30       1750 21.875            35
2-3 PM           3000       45       2250 28.125            45
3-4 PM           3000       45       2250 28.125            45
4-5 PM           3000       45       2250 28.125            45
5-6 PM           4000       45       3000       37.5        60
6-7 PM           3000       45       2250 28.125            45
7-8 PM           1500       45       1125 14.0625          22.5
Process Throughput Time Reduction




• Perform activities in parallel

• Change the sequence of activities

• Reduce interruptions
Productivity and Efficiency
• Productivity is output to input. It may be total
  factor productivity or partial factor
  productivity.
  – The ratio is generally taken in monitory terms.
• Efficiency is the ratio of actual output of a
  process relative to some standard or best level
  of output.
Process Performance Metrics
Little’s Law
• Little’s Law—states a mathematical relationship
  between throughput rate, throughput time, and the
  amount of work-in-process inventory.
• Little’s Law estimates the time that an item will spend
  in work-in-process inventory, which can be useful for
  calculating the total throughput time for a process.
• Little’s Law = Throughput time = Work-in-process
                                     Throughput rate
• (Throughput rate is the output rate that the process is
  expected to produce over a period of time.)
Example
• if the assembly line has six stations with one unit of work-
  in-process at each station, and the throughput rate is 2
  units per minute (60 seconds/30 seconds per unit),
• then the throughput time is three minutes (6 units/2 units
  per minute).
• This formula holds for any process that is operating at a
  steady rate.
• Steady rate—means that work is entering and exiting the
  system at the same rate over the time period being
  analyzed.
• For example, our assembly line has 120 units entering and
  120 units exiting the process each hour. Not 150 units
  entering the system each hour but only 120 units exiting.
The McDonaldization of Society (1993)
          George Ritzer
• Efficiency – the optimal method for accomplishing a task.
  In this context, Ritzer has a very specific meaning of
  "efficiency". Here, the optimal method equates to the
  fastest method to get from point A to point B.
• Calculability – objective should be quantifiable (e.g., sales)
  rather than subjective (e.g., taste). McDonaldization
  developed the notion that quantity equals quality, and that
  a large amount of product delivered to the customer in a
  short amount of time is the same as a high quality product.
• Predictability – standardized and uniform services. “Their
  tasks are highly repetitive, highly routine, and predictable”.
• Control – standardized and uniform employees,
  replacement of human by non-human technologies.
MAC: Make to Stock                              Customer
                                                                                   Orders
     RAW                                               Finished
    Material       Cook             Assemble            Goods
                                                                                   Deliver


                      Burger King:
                      Hybrid                                          Assemble
    RAW
   Material                          WIP              Customer           Sta
                   Cook                                Orders                          Deliver
                                                                         nd
                                                                         ard

Wendy’s: Make to    Customer                                           Finished
Order                Orders                                             Goods
                                                      Assemble
       RAW
      Material
                      Cook                 Assemble         Deliver


                          Chili
Make to Order
 Process Analysis Issues and Metrics?
• ????
• Assignment

Mais conteúdo relacionado

Mais procurados

Donner case study om
Donner case study   omDonner case study   om
Donner case study omPratik Patel
 
Toyota Operations Case Study
Toyota Operations Case Study Toyota Operations Case Study
Toyota Operations Case Study Martin Massiah
 
The Case Analysis: Benihana of Tokyo
The Case Analysis: Benihana of TokyoThe Case Analysis: Benihana of Tokyo
The Case Analysis: Benihana of TokyoLuqman Praditio
 
Som case study - dont bother me i cant cope
Som   case study - dont bother me i cant copeSom   case study - dont bother me i cant cope
Som case study - dont bother me i cant copeRajendra Inani
 
Chad's Creative Concept
Chad's Creative ConceptChad's Creative Concept
Chad's Creative ConceptBishnu Kumar
 
Colgate- Palmolive: Managing International Careers
Colgate- Palmolive: Managing International CareersColgate- Palmolive: Managing International Careers
Colgate- Palmolive: Managing International CareersTajan Joseph
 
Shouldice Hospital
Shouldice HospitalShouldice Hospital
Shouldice Hospitaltarunkdl
 
National cranberry case study
National cranberry case studyNational cranberry case study
National cranberry case studyRanjeet Singh
 
Jose’s authentic mexican restaurant
Jose’s authentic mexican restaurantJose’s authentic mexican restaurant
Jose’s authentic mexican restaurantDaipayan Das
 
Cunningham motors
Cunningham motorsCunningham motors
Cunningham motorsvivek_shaw
 
Kristen's cookie company
Kristen's cookie companyKristen's cookie company
Kristen's cookie companyRahul Biradar
 
Donner case Operations Management
Donner case Operations ManagementDonner case Operations Management
Donner case Operations ManagementHarish B
 
Mrs. Fields Cookies Presentation
Mrs. Fields Cookies PresentationMrs. Fields Cookies Presentation
Mrs. Fields Cookies PresentationSteven Panek
 
Kristen Cookie case study
Kristen Cookie case studyKristen Cookie case study
Kristen Cookie case studyAmit Walawalkar
 
IKEA's Global Sourcing Challenge
IKEA's Global Sourcing ChallengeIKEA's Global Sourcing Challenge
IKEA's Global Sourcing ChallengePanos Anadiotis
 
Kristen cookie case
Kristen cookie caseKristen cookie case
Kristen cookie casejldolan1
 
National cranberry cooperative
National cranberry cooperativeNational cranberry cooperative
National cranberry cooperativePallavi Bharti
 
2012 04 11 lives of foxconn workers revised
2012 04 11 lives of foxconn workers revised2012 04 11 lives of foxconn workers revised
2012 04 11 lives of foxconn workers revisedEconomicPolicyInstitute
 

Mais procurados (20)

Donner case study om
Donner case study   omDonner case study   om
Donner case study om
 
Toyota Operations Case Study
Toyota Operations Case Study Toyota Operations Case Study
Toyota Operations Case Study
 
Process control polaroid
Process control polaroidProcess control polaroid
Process control polaroid
 
The Case Analysis: Benihana of Tokyo
The Case Analysis: Benihana of TokyoThe Case Analysis: Benihana of Tokyo
The Case Analysis: Benihana of Tokyo
 
Som case study - dont bother me i cant cope
Som   case study - dont bother me i cant copeSom   case study - dont bother me i cant cope
Som case study - dont bother me i cant cope
 
Chad's Creative Concept
Chad's Creative ConceptChad's Creative Concept
Chad's Creative Concept
 
Colgate- Palmolive: Managing International Careers
Colgate- Palmolive: Managing International CareersColgate- Palmolive: Managing International Careers
Colgate- Palmolive: Managing International Careers
 
Shouldice Hospital
Shouldice HospitalShouldice Hospital
Shouldice Hospital
 
National cranberry case study
National cranberry case studyNational cranberry case study
National cranberry case study
 
Jose’s authentic mexican restaurant
Jose’s authentic mexican restaurantJose’s authentic mexican restaurant
Jose’s authentic mexican restaurant
 
Cunningham motors
Cunningham motorsCunningham motors
Cunningham motors
 
Kristen's cookie company
Kristen's cookie companyKristen's cookie company
Kristen's cookie company
 
Donner case Operations Management
Donner case Operations ManagementDonner case Operations Management
Donner case Operations Management
 
Mrs. Fields Cookies Presentation
Mrs. Fields Cookies PresentationMrs. Fields Cookies Presentation
Mrs. Fields Cookies Presentation
 
Kristen Cookie case study
Kristen Cookie case studyKristen Cookie case study
Kristen Cookie case study
 
IKEA's Global Sourcing Challenge
IKEA's Global Sourcing ChallengeIKEA's Global Sourcing Challenge
IKEA's Global Sourcing Challenge
 
Kristin Cookie
Kristin CookieKristin Cookie
Kristin Cookie
 
Kristen cookie case
Kristen cookie caseKristen cookie case
Kristen cookie case
 
National cranberry cooperative
National cranberry cooperativeNational cranberry cooperative
National cranberry cooperative
 
2012 04 11 lives of foxconn workers revised
2012 04 11 lives of foxconn workers revised2012 04 11 lives of foxconn workers revised
2012 04 11 lives of foxconn workers revised
 

Último

Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesThousandEyes
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...panagenda
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Alkin Tezuysal
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Manual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditManual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditSkynet Technologies
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Data governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationData governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationKnoldus Inc.
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 

Último (20)

Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Manual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditManual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance Audit
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Data governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationData governance with Unity Catalog Presentation
Data governance with Unity Catalog Presentation
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 

Operations management

  • 2. Agenda • Takira Motors: Creating Assembly and Process Chart • Grouping Activities • Bakery Example
  • 4.
  • 6. Cycle time is the time taken by the longest activity (here it is 7 min). Throughput time is 46 min.
  • 7. Assignment-2 Q2 Cycle Time and Deciding on stages 0.1 min. 1.0 min. A Simple Precedence a Diagram b c d e 0.7 min. 0.5 min. 0.2 min. Arrange tasks shown in the above Figure into three workstations. Use a cycle time of 1.0 minute Assign tasks in order of the most number of followers
  • 8. Raw Pack Finished Bread Making WIP Material Cycle Time: Goods Cycle Time: 3/4Hr /100 1 Hr/100 loaves loaves packaging operation will be idle for quarter-hour periods (15 mins) Bread Making Cycle Time: The packaging operation is 1 Hr/100 loaves now the bottleneck Raw WIP Pack Finished Material Cycle Time: Goods 3/4Hr /100 loaves Bread Making Cycle Time: If we operated the packaging operation 1 Hr/100 loaves for three eight-hour shifts, and bread making for two shifts each day, then the daily capacity of each would be identical at 3,200 loaves a day (800 loafs x 4 shifts).
  • 9. Packaging 3 shifts Bread making 2 shifts: work-in-process inventory • If both bread-making operations start at the same time, at the end of the first hour, then the first 100 loaves move into packaging while the second 100 loaves wait—work-in-process inventory. • The waiting time for each 100-loaf batch increases until the baking is done at the end of the second shift. • What is the time that the bread is sitting in work- in-process?
  • 10. • Average wip during first two shifts, inventory builds from 0 to 1,200 loaves (1,600 x .75). (Or 800 loaves x 2 shifts x .75 pkg.) = 1200/2 • Average wip during third shift is again 1200/2 • The overall average WIP over the 24-hour period is simply 600 loaves of bread. • Packaging process in batches. 1) 0.75 hour per 100 loaves 2) Throughput rate of 133.3 loaves/hour (100/0.75) • Little’s Law calculates the average time that loaves are in work-in-process is 4.5 hours (600 loaves/133.33 loaves/hour)
  • 11. Our Restaurant • Assume that we have designed our buffet so customers take an average of 30 minutes to get their food and eat. • Assume the restaurant has 40 tables. Each table can accommodate four people. • The Cycle Time for the restaurant, when operating at capacity, is 0.75 minute (30 minutes/table ÷ 40 tables). • The restaurant could handle 80 customer parties per hour (60 minutes ÷ 0.75 minute/party) is the capacity or customer parties per hour. • Assume that our customers eat in groups (or customer parties) of two or three to a table. • How many customers can the restaurant serve if the average customer party is 2.5? • During lunch time, Customers arrive as per the schedule given.
  • 12. Solution • If the average customer party is 2.5 individuals, then the average seat utilization is 62.5 percent (2.5 seats/party ÷ 4 seats per table) when the restaurant is operating at capacity. • The Cycle Time for the restaurant, when operating at capacity, is 0.75 minute (30 minutes/table ÷ 40 tables). • The restaurant could handle 80 customer parties per hour (60 minutes ÷ 0.75 minute/party) is the capacity or customer parties per hour. • 20 tables empty during each 15-minute interval
  • 13. During Period End of Period IN Out To Serve Tables Wait Wait Time 11:30-11:45 15 0 11:45-12:00 35 0 12:00-12:15 30 15 12:15-12:30 15 20 12:30-12:45 10 20 12:45-1:00 5 20 1:00-1:15 0 1:15-1:30 0 Total Served Formula Data The first 15 tables empty after 30 mins. After that the restaurant can serve 20 tables every 15 minuets.
  • 14. During Period End of Period IN Out To Serve Tables Wait 11:30-11:45 15 0 15 15 0 11:45-12:00 35 0 50 40 10 12:00-12:15 30 15 65 40 25 12:15-12:30 15 20 60 40 20 12:30-12:45 10 20 12:45-1:00 5 20 1:00-1:15 0 1:15-1:30 0 Total Served Formula Data . 12:00, we will have to keep 10 parties waiting: 15+35-40 At Between 12:00 – 12:15, we have another 30 parties coming in and 15 leaving. So we will have 15+35+30-40 -15 = 25 waiting 12:15-12:30 we have 15 parties coming in and 20 leaving, so we will have 15+35+30+15-40-15-20 = 20 waiting
  • 15. During Period End of Period IN Out To Serve Tables Wait 11:30-11:45 15 0 15 15 0 11:45-12:00 35 0 50 40 10 12:00-12:15 30 15 65 40 25 12:15-12:30 15 20 60 40 20 12:30-12:45 10 20 50 40 10 12:45-1:00 5 20 35 35 0 1:00-1:15 0 20 15 15 0 1:15-1:30 0 15 0 0 0 Total Served 110 20 per 15 Table+IN- IF((To serve<40),To To serve- Formula Data mins OUT serve,40) Table .
  • 16. During Period End of Period IN Out To Serve Tables Wait Wait Time 11:30-11:45 15 0 15 15 0 0 11:45-12:00 35 0 50 40 10 7.5 12:00-12:15 30 15 65 40 25 18.75 12:15-12:30 15 20 60 40 20 15 12:30-12:45 10 20 50 40 10 7.5 12:45-1:00 5 20 35 35 0 0 1:00-1:15 0 20 15 15 0 0 1:15-1:30 0 15 0 0 0 0 Total Served 110 20 per 15 Table+IN- IF((To serve<40),To To serve- Formula Data mins OUT serve,40) Table Wait*15/20 Expected wait time = customers waiting x 0.75 minute Double up parties at the tables, thus getting a higher seat utilization. Might be the easiest solution to the problem. If 25 out of the 40 tables were doubled up, our problem would be solved… (40+20)*2.5 > 40*4.. So?
  • 17. Transit Bus Operation • Logistics refers to the movement of things such as materials, people, or finished goods. • A single bus takes exactly two hours to traverse the route during peak traffic. • Wait time for the customer: Maximum is 2 Minimum is 0, Average would be one hour. • Bus capacity: seating 50 Standing 30
  • 18. No of customers Av Time 8-9 AM 2,000 45 9-10 AM 4000 30 10-11 AM 6000 30 11-12 noon 5000 30 12-1 PM 4000 30 1-2 PM 3500 30 2-3 PM 3000 45 3-4 PM 3000 45 4-5 PM 3000 45 5-6 PM 4000 45 6-7 PM 3000 45 7-8 PM 1500 45 How do we estimate number of passengers carried by a bus per trip? Per hour?
  • 19. No of Passenger Min No of Max No of customers Av Time Hours Buses Buses 8-9 AM 2,000 45 1500 18.75 30 9-10 AM 4000 30 2000 25 40 10-11 AM 6000 30 3000 37.5 60 11-12 noon 5000 30 2500 31.25 50 12-1 PM 4000 30 2000 25 40 1-2 PM 3500 30 1750 21.875 35 2-3 PM 3000 45 2250 28.125 45 3-4 PM 3000 45 2250 28.125 45 4-5 PM 3000 45 2250 28.125 45 5-6 PM 4000 45 3000 37.5 60 6-7 PM 3000 45 2250 28.125 45 7-8 PM 1500 45 1125 14.0625 22.5
  • 20. Process Throughput Time Reduction • Perform activities in parallel • Change the sequence of activities • Reduce interruptions
  • 21. Productivity and Efficiency • Productivity is output to input. It may be total factor productivity or partial factor productivity. – The ratio is generally taken in monitory terms. • Efficiency is the ratio of actual output of a process relative to some standard or best level of output.
  • 23. Little’s Law • Little’s Law—states a mathematical relationship between throughput rate, throughput time, and the amount of work-in-process inventory. • Little’s Law estimates the time that an item will spend in work-in-process inventory, which can be useful for calculating the total throughput time for a process. • Little’s Law = Throughput time = Work-in-process Throughput rate • (Throughput rate is the output rate that the process is expected to produce over a period of time.)
  • 24. Example • if the assembly line has six stations with one unit of work- in-process at each station, and the throughput rate is 2 units per minute (60 seconds/30 seconds per unit), • then the throughput time is three minutes (6 units/2 units per minute). • This formula holds for any process that is operating at a steady rate. • Steady rate—means that work is entering and exiting the system at the same rate over the time period being analyzed. • For example, our assembly line has 120 units entering and 120 units exiting the process each hour. Not 150 units entering the system each hour but only 120 units exiting.
  • 25.
  • 26. The McDonaldization of Society (1993) George Ritzer • Efficiency – the optimal method for accomplishing a task. In this context, Ritzer has a very specific meaning of "efficiency". Here, the optimal method equates to the fastest method to get from point A to point B. • Calculability – objective should be quantifiable (e.g., sales) rather than subjective (e.g., taste). McDonaldization developed the notion that quantity equals quality, and that a large amount of product delivered to the customer in a short amount of time is the same as a high quality product. • Predictability – standardized and uniform services. “Their tasks are highly repetitive, highly routine, and predictable”. • Control – standardized and uniform employees, replacement of human by non-human technologies.
  • 27. MAC: Make to Stock Customer Orders RAW Finished Material Cook Assemble Goods Deliver Burger King: Hybrid Assemble RAW Material WIP Customer Sta Cook Orders Deliver nd ard Wendy’s: Make to Customer Finished Order Orders Goods Assemble RAW Material Cook Assemble Deliver Chili
  • 28.
  • 29.
  • 30. Make to Order Process Analysis Issues and Metrics? • ???? • Assignment