1. OPSM 501: Operations Management Week 6: The Goal Koç University Graduate School of Business MBA Program Zeynep Aksin zaksin@ku.edu.tr

2. How do you keep track of the goal?  Accounting measures –Profits (absolute) –ROI (relative) –Cash flow (survival)  What are leading indicators of financial performance? –Throughput –Inventory –Operating expense

3. Making Money - How do we measure it? The GOAL : To Make Money  Bottom line measurements Net Profit Return on Investment Cash Flow (Absolute)(Relative)(Survival) What is the bridge? ACTIONS

4. Use Global Operational Measures Throughput (T) The rate at which the system generates money through sales Inventory (I) All the money the system invests in purchasing things the system intends to sell Operating expense (OE) All the money the system spends in turning inventory into throughput

5. The Direct Impact Operational Measurements and the Bottom Line Net Profit Return on Investment Cash Flow ThroughputInventoryOperating Expense

6. The Indirect Impact Inventory and Holding Costs Net Profit Return on Investment Cash Flow ThroughputInventoryOperating Expense Holding costs

7. The Competitive Edge Impact Net Profit Return on Investment Cash Flow ThroughputInventoryOperating Expense Competitive edge

8. Role of Reduced Inventory  Product –Quality –Engineering  Price –Higher margins –Lower investment per unit  Responsiveness –Due-date performance –Shorter quoted lead time

9. Increasing Process Capacity in The Goal  “is to increase the capacity of only the bottlenecks” –“ensure the bottlenecks’ time is not wasted” increase availability of bottleneck resources eliminate non-value added work from bottlenecks –reduce/eliminate setups and changeovers synchronize flows to & from bottleneck –reduce starvation & blockage –“ the load of the bottlenecks (give it to non- bottlenecks)” move work from bottlenecks to non-bottlenecks need resource flexibility – unit capacity and/or #of units. invest

10. Drum-Buffer-Rope  The drum is the constraint-sets the speed  The buffer is a time buffer used to protect the drum from disruptions in the preceding production steps  The rope is a schedule that dictates the timing of the release of raw materials, or jobs, into the system

11. Theory of Constraints 1.Identify the System’s Constraints 2.Decide how to exploit the system’s constraints 3.Subordinate everything else to the above decision 4.Elevate the system’s constraints 5.If in the Previous steps a constraint has been broken, go back to step 1)

12. Lessons from the Goal  Identify the goal: making money  Making money requires clear operational measures  Management systems (accounting, incentives, measurement) often get in the way of good plant management  There are typically only a few bottleneck resources: every other resource should be subordinated to them  Balance flow, not capacity  Once you identify the bottleneck, you can elevate its capacity: continuous improvement  How do you protect the bottleneck? –Effective scheduling (drum-buffer-rope) –Effective lot sizing (transfer versus order lotsize) Work smarter, not harder

13. Process management  Strategic positioning-establish product capabilities  Determine appropriate process capabilities: time, quality, cost, flexibility  Process design, appropriate selection of resources  Process documentation: flowchart  Analyze at macro level –Where is the bottleneck? –Is capacity enough? –How is time performance? –Where do quality problems occur?  Analyze at micro level –Scheduling: focus on the bottleneck –Set-up times, lotsize –Reduce variability

14. Framework for Process Flow Management Competitive? No Flow Chart Process Identify Bottlenecks Maximal Flow Rate Identify Critical Path Minimal Flow Time Demand Pattern Macro Average Performance Process Re-Design Competitive? No Micro Variability Performance Demand & Supply Mgt Continuous Improvement mean variability Yes

15. 15 Example: What would you produce? Product A Product B  Sales price90TL100TL  Materials45TL40TL  Time per unit55 min.50 min.  Demand unlimited unlimited  Total time available40 hours per week  Weekly operating expense 5000TL

16. 16 Process information Product A Product B R4 13 min/unit R4 8 min/unit R3 10 min/unit R1 15 min/unit R3 2 min/unit R2 15 min/unit R1 10 min/unit R2 15 min/unit 5 TL 10 TL 20 TL 20 TL 10 TL Component X Component Y Component Z

17. 17 Analysis of production alternatives Product A Product B Resource unit Load Th. Capacity unit Load Th. Capacity (min) (2400min/week) R11516010 240 R21516030 80 R3122002 1200 R4131848 300 80 units of B 80x60TL=$4800 per week200TL weekly loss 160 units of A 45TLx160=$7200 per week 2200TL weekly profit What if weekly demand is 60 units per week for both A and B?

18. Recall the house game: an unbalanced line  if average task times are different, will have an unbalanced line will have idleness  in unbalanced case, slowest task determines output rate bottleneck is busy idleness in other stages

19. The role of variability 6units/hr 4 or 8/hr 2 or 10 0 or 12 As variability increases, throughput (rate) decreases Capacity/hr: 65436543

20. The role of task times: a balanced line  if task times are similar will have a balanced line in the absence of variability (deterministic) complete synchronization is possible in a balanced line idleness is minimized, though in the presence of variability full synchronization cannot be achieved

21. Compounding effect of variability and unbalanced task times 6/hr4/hr 4 or 8/hr 2 or 6/hr 2 or 100 or 8 4/hr 3.5/hr 2.5/hr

22. Resource interaction effects 6/hr 4 or 8/hr 2 or 10 0 or 12 6/hr 4 or 8/hr 2 or 100 or 12 6/hr 4.5/hr 3/hr 1.5/hr In a serial process downstream resources depend on upstream resources: can have temporary starvation (idleness) As variability increases, the impact of resource interaction increases

23. Variability in multi-stage processes  We have seen how variability hurts performance in a multi-stage process –Worse with unbalanced task times and resource interference  Note that –We assumed a very simplistic form of processing time variability –We assumed there is no variability in arrivals  We now know variability hurts, but can’t say how much yet

24. Want to eliminate as much variability as possible from your processes: how?  specialization in tasks can reduce task time variability  standardization of offer can reduce job type variability  automation of certain tasks  IT support: templates, prompts, etc.  Incentives  Scheduled arrivals to reduce demand variability  Initiatives to smoothen arrivals

25. Want to reduce resource interference in your processes: how?  smaller lotsizes (smaller batches)  better balanced line  by speeding-up bottleneck (adding staff, changing procedure, different incentives, change technology)  through cross-training  eliminate steps  buffers  integrate work (pooling)

26. Flow Times with Arrival Every 4 Secs (Service time=5 seconds) Customer Number Arrival Time Departure Time Time in Process 1055 24106 38157 412208 516259 6203010 7243511 8284012 9324513 10365014 What is the queue size? Can we apply Little’s Law? What is the capacity utilization?

27. Customer Number Arrival Time Departure Time Time in Process 1055 26115 312175 418235 524295 630355 736415 842475 948535 1054595 Flow Times with Arrival Every 6 Secs (Service time=5 seconds) What is the queue size? What is the capacity utilization?

28. Customer Number Arrival Time Processing Time Time in Process 1077 21011 32077 42227 53288 633714 736415 843816 952512 1054111 Effect of Variability What is the queue size? What is the capacity utilization?

29. Customer Number Arrival Time Processing Time Time in Process 1088 21088 32022 42277 53211 63311 73677 84377 95244 105457 Effect of Synchronization What is the queue size? What is the capacity utilization?

30. Conclusion  If inter-arrival and processing times are constant, queues will build up if and only if the arrival rate is greater than the processing rate  If there is (unsynchronized) variability in inter-arrival and/or processing times, queues will build up even if the average arrival rate is less than the average processing rate  If variability in interarrival and processing times can be synchronized (correlated), queues and waiting times will be reduced

31. Why is there waiting?  the perpetual queue: insufficient capacity-add capacity  the predictable queue: peaks and rush-hours- synchronize/schedule if possible  the stochastic queue: whenever customers come faster than they are served-reduce variability

32. Components of the Queuing System Visually Customers come in Customers are served Customers leave

33. A measure of variability  Needs to be unitless  Only variance is not enough  Use the coefficient of variation  C or CV=  / 

34. Interpreting the variability measures C i = coefficient of variation of interarrival times i) constant or deterministic arrivals C i = 0 ii) completely random or independent arrivals C i =1 iii) scheduled or negatively correlated arrivals C i < 1 iv) bursty or positively correlated arrivals C i > 1

35. To address the “how much does variability hurt” question: Consider service processes  This could be a call center or a restaurant or a ticket counter  Customers or customer jobs arrive to the process; their arrival times are not known in advance  Customers are processed. Processing rates have some variability.  The combined variability results in queues and waiting.  We need to build some safety capacity in order to reduce waiting due to variability

36. Specifications of a Service Provider Service Provider Leaving Customers Waiting Customers Demand Pattern Resources Human resources Information system other... Arriving Customers Satisfaction Measures Reneges or abandonments Waiting Pattern Served Customers Service Time

37. Upcoming events  Next week we will have an in-class activity, don’t miss class!  Midterm exam will be Week 10-proposed date November 26 Monday