OPSM 301: Operations Management Session 12: Service processes and flow variability Koç University Graduate School of Business MBA Program Zeynep Aksin

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 6units/hr 4 or 8/hr 2 or 10 0 or 12 As variability increases, throughput (rate) decreases Capacity/hr:

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/hr4/hr 4 or 8/hr 2 or 6/hr 2 or 100 or 8 4/hr 3.5/hr 2.5/hr

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

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

Service Delivery System Customer Demand Limited Capacity Fundamental Problem: Variable Usage 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 15 Not served in order Process time-consuming incorrect diagnosis incorrect estimate F F F F

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 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

The Service Process  Customer Inflow (Arrival) Rate (R i ) ( ) –Inter-arrival Time = 1 / R i  Processing Time T p (unit load) –Processing Rate per Server = 1/ T p (µ)  Number of Servers (c) –Number of customers that can be processed simultaneously  Total Processing Rate (Capacity) = R p = c / T p (cµ)

Operational Performance Measures Flow time T=T w +T p (waiting+process) Inventory I= I w + I p Flow Rate R =Min (R i, R p  Stable Process= R i < R p,, so that R = R i Little’s Law: I = R  T, I w = R  T w, I p = R  T p Capacity Utilization  = R i / R p < 1 Safety Capacity = R p – R i Number of Busy Servers = I p = c  = R i  T p waiting processing ( ) R i e.g10 /hr R ( ) 10 /hr 10 min, R p =12/hr Tw?Tw?

Flow Times with Arrival Every 4 Secs (Service time=5 seconds) Customer Number Arrival Time Departure Time Time in Process What is the queue size? Can we apply Little’s Law? What is the capacity utilization?

Customer Number Arrival Time Departure Time Time in Process Flow Times with Arrival Every 6 Secs (Service time=5 seconds) What is the queue size? What is the capacity utilization?

Customer Number Arrival Time Processing Time Time in Process Effect of Variability What is the queue size? What is the capacity utilization?

Customer Number Arrival Time Processing Time Time in Process Effect of Synchronization What is the queue size? What is the capacity utilization?

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=  / 

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

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