Koç University Graduate School of Business MBA Program OPSM 301: Operations Management Session 12: Service processes and flow variability Zeynep Aksin zaksin@ku.edu.tr Recall the smiley face 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 The role of variability Capacity/hr: 6units/hr Capacity/hr: 6units /hr 6 4 or 8/hr 4 or 8/hr 5 2 or 10 2 or 10 4 0 or 12 0 or 12 3 As variability increases, throughput (rate) decreases 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 Compounding effect of variability and unbalanced task times 6/hr 4/hr 4/hr 4 or 8/hr 2 or 6/hr 3.5/hr 2 or 10 0 or 8 2.5/hr Resource interaction effects In a serial process downstream resources depend on upstream resources: can have temporary starvation (idleness) 6/hr 6/hr 6/hr 6/hr 4 or 8/hr 4 or 8/hr 4 or 8/hr 6/hr 2 or 10 2 or 10 2 or 10 6/hr 0 or 12 0 or 12 6/hr 0 or 12 6/hr 4.5/hr 3/hr 1.5/hr As variability increases, the impact of resource interaction increases 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 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 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) What differentiates services Customer contact: the physical presence of the customer in the system – Service systems with a high degree of customer contact are more difficult to control The product is the process: the work process involved in providing the service itself Structuring the Service Encounter: Service-System Design Matrix Fundamental Problem: Customer Demand Variable Usage Service Delivery System Limited Capacity Services cannot be produced in advance and stored for later consumption; they must be produced at the time of consumption. Designing Service Organizations We cannot inventory services In services capacity becomes the dominant issue – Too much capacity leads to excessive costs – Insufficient capacity leads to lost customers Managing waiting lines is a central issue in services Service Blueprinting and Fail-Safing The standard tool for service process design is the flowchart – Called a service blueprint A unique feature of the service blueprint is the distinction made between the high customer contact aspects of the service and those activities that the customer does not see – Made with a “line of visibility” on the flowchart Process Blueprint Example: Automotive Service Operation F F F F Not served in order Process time-consuming incorrect diagnosis incorrect estimate 15 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 Components of the Queuing System Visually Customers come in Customers are served Customers leave Specifications of a Service Provider Reneges or abandonments Arriving Customers Waiting Pattern Demand Pattern Service Provider Waiting Customers Served Customers Service Time Resources • Human resources • Information system • other... Leaving Customers Satisfaction Measures The Service Process Customer Inflow (Arrival) Rate (Ri) () – Inter-arrival Time = 1 / Ri Processing Time Tp (unit load) – Processing Rate per Server = 1/ Tp (µ) Number of Servers (c) – Number of customers that can be processed simultaneously Total Processing Rate (Capacity) = Rp= c / Tp (cµ) Operational Performance Measures () Ri waiting processing R () e.g10 /hr 10 /hr Tw? 10 min, Rp=12/hr Flow time T = Tw + Tp (waiting+process) Inventory I = Iw + Ip Flow Rate R = Min (Ri, Rp) Stable Process = Ri < Rp,, so that R = Ri Little’s Law: I = R T, Iw = R Tw, Ip = R Tp Capacity Utilization = Ri / Rp < 1 Safety Capacity = Rp – Ri Number of Busy Servers = Ip= c = Ri Tp Flow Times with Arrival Every 4 Secs (Service time=5 seconds) Customer Number Arrival Time Departure Time Time in Process 1 0 5 5 2 4 10 6 3 8 15 7 4 12 20 8 5 16 25 9 6 20 30 10 3 7 24 35 11 2 8 28 40 12 9 32 45 13 10 36 50 14 10 9 Customer Number 8 7 6 5 4 1 0 10 What is the queue size? Can we apply Little’s Law? What is the capacity utilization? 20 30 Time 40 50 Flow Times with Arrival Every 6 Secs (Service time=5 seconds) Arrival Time Departure Time Time in Process 10 1 0 5 5 9 2 6 11 5 8 3 12 17 5 4 18 23 5 5 24 29 5 6 30 35 5 7 36 41 5 2 8 42 47 5 1 9 48 53 5 10 54 59 5 What is the queue size? What is the capacity utilization? Customer Number Customer Number 7 6 5 4 3 0 10 20 30 Time 40 50 60 Effect of Variability Customer Number Arrival Time Processing Time Time in Process 1 0 7 7 2 10 1 1 3 20 7 7 4 22 2 7 5 32 8 8 6 33 7 14 7 36 4 15 8 43 8 16 9 52 5 12 10 54 1 11 10 9 8 Customer 7 6 5 4 3 2 1 0 10 20 30 40 50 60 70 Time Queue Fluctuation 4 What is the queue size? What is the capacity utilization? Number 3 2 1 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 Time Effect of Synchronization Customer Number Arrival Time Processing Time Time in Process 1 0 8 8 2 10 8 8 8 3 20 2 2 7 4 22 7 7 6 5 32 1 1 5 6 33 1 1 4 7 36 7 7 3 8 43 7 7 2 9 52 4 4 1 10 54 5 7 What is the queue size? What is the capacity utilization? 10 9 0 10 20 30 40 50 60 70 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 A measure of variability Needs to be unitless Only variance is not enough Use the coefficient of variation C or CV= s/m Interpreting the variability measures Ci = coefficient of variation of interarrival times i) constant or deterministic arrivals Ci = 0 ii) completely random or independent arrivals Ci =1 iii) scheduled or negatively correlated arrivals Ci < 1 iv) bursty or positively correlated arrivals Ci > 1 Why is there waiting? the perpetual queue: insufficient capacity-add capacity the predictable queue: peaks and rush-hourssynchronize/schedule if possible the stochastic queue: whenever customers come faster than they are served-reduce variability