1. OPSM 301 Operations Management Class 2: Business process flows: Measurement Koç University Zeynep Aksin zaksin@ku.edu.tr

2. Announcements  Wrap-up: King Sooper’s video  New module: business process flows  From notes and handouts  Study questions  Also: cases at the end –Kristen’s Cookie –Benihana

3. Performance Measurement  External performance strongly depends on output, input, and resource markets, and transformation effectiveness of the process  Internal performance measures: processing cost, flow time, variety, service availability...

4. The Dynamics of a Process  We examine processes from the perspective of flow  To study process flows, we first answer three important questions: –On average, how many flow units pass through the process per unit time? –On average, how much time does a typical flow unit spend within process boundaries? –On average, how many flow units are within process boundaries at any point in time?

5. Operational Measures  On average, how many flow units pass through the process per unit time? THROUGHPUT RATE (TH)

6. Operational Measures  On average, how much time does a typical flow unit spend within process boundaries? FLOW TIME (FT)

7. Operational Measures  On average, how many flow units are within process boundaries at any point in time? INVENTORY (I)

8. LITTLE’S LAW  Relating throughput rate, flow time, and inventory Inventory I [units] Throughput Rate [units/hr]... Flow Time T [hrs]

9. ... Inventory I [units] Throughput Rate [units/hr]... Flow Time T [hrs] Time=0... Inventory I [units] Throughput Rate [units/hr]... Flow Time T [hrs] Time=t... Inventory I [units] Throughput Rate [units/hr]... Flow Time T [hrs] Time=FT Understanding Little’s Law: Consider a first come first served Queue

10. An Intuitive Argument for Little's Law  Consider a process with the FCFS queue discipline  An order departs the process: At this moment there are I (Inventory) orders within the process  The orders that are in the process now are the ones that came after our departing order had arrived, in other words, they arrived during the waiting period of the departing order  Since order arrival rate is equal to the throughput rate, we have the following relationship: Inventory = Throughput Rate x Flow Time

11. Once again... Inventory = Throughput x Flow Time

12. Little’s Law basics  Little’s Law is for a system in steady state: input rate = output rate  Applies to most systems, even those with variability  Uses AVERAGE values

13. Example: flow unit is material  Fast food restaurant processes an average of 5000kgs, of hamburgers per week. Typical inventory of raw meat in cold storage is 2500kg.  Throughput R=5000kg/week  Average Inventory I=2500 kg.  Average flow time T=I/R=2500/5000=0.5 weeks

14. Example: flow unit is customers  A café in Beyoglu serves on average 60 customers per night. A typical night is about 10 hours. At any point there are on average 18 customers in the café.  Throughput R=60 customers/night; 6 customers/hour  Average Inventory I=18 customers  Average flow time T= I/R= 3 hours

15. Example: flow unit is cash  A steel company processes $400 million of iron ore per year. The cost of processing is $200 million per year. The average inventory is $100 million. How long does a typical dollar spend in the process?  R=$600 million/year  I=$100 million  T=I/R=1/6 year or 2 months

16. Example  Koç University 3000 students enrolled 600 new students admitted on average

17. Example: MBPF Finance Inc. adapted from Managing Business Flows, by Anunpindi et al.  MBPF Finance Inc. provides loans to qualified customers. The company receives about 1000 loan applications per 30-day working month and makes accept/reject decisions based on an extensive review of each application

18. MBPF Finance Inc.  Currently, MBPF Finance Inc. processes each application individually. On average, 20% of all applications received approval. An internal audit showed that, on average, MBPF Finance Inc. had about 500 applications in process at various stages of the approval procedure, but on which no decisions had yet been made.  In response to customer complaints about the time taken to process each application, MBPF Finance Inc. called in OPSM Consulting Inc.

19. Example: cont’d  OPSM Consulting found out that although most applications could be processed rather quickly, some took a disproportionate amount of time because of insufficient and/or unclear documentation. They suggested the following Process II:  Because, the percentage of approved applications is fairly low, and Initial Review Team should be set up to pre-process all applications according to strict but fairly mechanical guidelines.

20. MBPF Finance Inc.  Each application would fall into one of three categories: type A (looks excellent), type B (needs more detailed evaluation), and type C (reject summarily). Type A and B applications would be forwarded to different specialist subgroups  Each subgroup would then evaluate the applications in its domain and make accept/reject decisions

21. Example: (cont’d)  Process II was implemented on an experimental basis. The company found out that, on average, 25% of all applications were of type A, 25% were of B, and 50% were of C. Typically, about 70% of type A and 10% of B were approved on review.

22. Example (cont’d)  Internal audit checks showed that, on average, 200 applications were with the Initial Review Team undergoing preprocessing. Only 25 were with the Subgroup A Team undergoing the next stage of processing and approximately 150 were with the Subgroup B Team  MBPF Finance Inc. would like to determine if the implemented changes have improved service performance.

23. (Initial Process) FlowLoan Inc. (Initial Process) Accept Review 200/month 500Reject800/month 20% 80% 1000/month

24. (Modified Process) FlowLoan Inc. (Modified Process) 30% 90% Initial Review 200 Subgroup A Review Subgroup B Review Accept Reject 200/month 800/month 25% 70% 10% 25 150 1000/month 50%