SYSTEM ANALYSIS AND SIMULATION (IE 621)MODEL 5.1

ABSTRACT

In the recreated model the approaching calls land with between entry times being exponentially appropriated with a mean of 0.857 moment. This focal number sustains 26 trunk lines. In the event that every one of the 26 lines are being used, a guest gets an occupied flag and is arranged. An addressed guest hears a recording portraying three alternatives: exchange to specialized bolster, deals data, or request status request (76%, 16%, and 8%, separately). The evaluated time for this movement is UNIF(0.1, 0.6); all times are in minutes. On the off chance that the guest picks specialized bolster, a second recording solicitations which of three item sorts the guest is utilizing, which requires UNIF(0.1, 0.5) minutes. The rate of solicitations for item sorts 1, 2, and 3 are 25%, 34%, and 41%, separately. In the event that a qualified specialized bolster individual is accessible for the chose item sort, the call is consequently directed to that individual. In the event that none are as of now accessible, the client encounters a postponement. The ideal opportunity for all specialized bolster calls is assessed to be TRIA(3, 6, 18) minutes paying little heed to the item sort. Endless supply of the call, the client leaves the framework. Deals calls are naturally steered to the business staff. On the off chance that a businessperson is not accessible, the client call holds up in the line. Deals calls are evaluated to be TRIA(4, 15, 45). Guests asking for request status data is naturally taken care of by the telephone framework, and there is no restriction on the number the framework can deal with. The assessed time for these exchanges is TRIA(2, 3, 4) minutes, with 15% of the clients selecting to talk with the client deals officials. These calls are steered to the business staff where they hold up with a lower need than deals calls. This implies if a request status call is in a line sitting tight for a salesman and another arriving deals call enters, the business call will be given need over the request status call and addressed first. These subsequent request status calls are assessed to last TRIA(2, 3, 4) minutes. These guests then leave the framework.

The call focus hours are from 8 AM until 6 PM, with a little extent of the staff on obligation until 7 PM. Despite the fact that the framework closes to new calls at 6 PM, all calls that enter the framework at that point are addressed and served. Throughout a day there are eight specialized bolster workers to answer specialized bolster calls. Two are committed to item sort 1 calls; three, to item sort 2 calls; and three, to item sort 3 calls. There are four deals workers to answer the business calls and those request status calls that pick to identify with a genuine individual. The quantities of calls rejected are likewise stayed informed regarding. The craved yield for these frameworks are : number of client dismissals (occupied signs), aggregate time on hold by client sort, time sitting tight for a genuine individual by client sort, number of calls sitting tight for administration by client sort, and work force usage.

PRROCESS LOGIC

DASHBOARD

MODEL:- 5.2In this section the existing process logic has been improved to bring in the effect of the real call center operation. The call-arrival rate to this system varies over the course of the day according to a nonstationary Poisson process, which is typical of these types of systems. So the call arrival data has been expressed in calls per hour for each 30-minute period during which the system is open. The call-arrival rates are given in the table below.

Time Rate Time Rate Time Rate Time Rate

8.00-8.30am 20 10.30-11.00am 75 1.00-1.30pm 110 3.30-4.00pm 90

8.30-9.00am 35 11.00-11.30am 75 1.30-2.00pm 95 4.00-4.30pm 70

9.00-9.30am 45 11.30-12.00pm 90 2.00-2.30pm 105 4.30-5.00pm 65

9.30-10.00am 50 12.00-12.30pm 95 2.30-3.00pm 90 5.00-5.30pm 45

10.00-10.30am 70 12.30-1.00pm 105 3.00-3.30pm 85 5.30-6.00pm 30

The staffing level varies for the technical and the sales department. It turns out that there are six sales people with the daily schedules is summarized in the table below.

Time Capacity Time Capacity

8.00-9.00am 1 1.30-3.00pm 5

9.00-10.00am 3 3.00-4.30pm 6

10.00-11.30am 4 4.30-5.00pm 5

11.30-12.30pm 5 5.00-6.00pm 3

12.30-1.30pm 6 6.00-7.00pm 2

The technical support employees work an eight-hour day with 30 minutes off for lunch (lunch is not included in the eight hours). There are 11 technical support people work schedules and the product types they are qualified to handle are updated in the schedule module and are defined in the set for each product type. Among them Charity and Noah are only qualified to handle calls for product Type 1; Tierney, Aidan, and Emma are only qualified to handle calls for product Type 2; Mya, Ian, and Christie are only qualified to handle calls for product Type 3. Molly is qualified to handle product Types 1 and 3, and Anna and Sammy are qualified to handle calls for all three product types. One more improvement from our existing model was that four percent of technical calls require further investigation after completion of the phone call. The questions raised by these callers are forwarded to another technical group, outside the boundaries of the model that prepares a response. The time to prepare these responses is estimated to be EXPO(60) minutes. The resulting response is sent back to the same technical support person who answered the original call. This person then calls the customer, which takes TRIA(2, 4, 9) minutes. These returned calls require one of the 26 trunk lines and receive priority over incoming calls. If a returned call is not completed on the same day the original call was received, it’s carried over to the next day. Counter has been modified to count the rejected calls every hour to observe the number of rejected calls during peak hour.

DASHBOARD

RESULTS

MODEL 5.3

This model spotlights on ascertaining the holdup time expense, aggregate expense caused for the week and approaches to enhance the general execution of the call focus. In the meantime, a few choices are added to the model to set the stage for examination of choices and for ideal looking for. To show signs of improvement measurable exactness, five replications was done, speaking to a work week and will concentrate on week after week costs. There are two essential regions in which expenses show up: (1) staffing and asset costs, which are very unmistakable and effortlessly measured, and (2) costs because of poor client administration, which are less substantial and can be hard to evaluate.

From the results of model 5.2 it is evident that number of rejected calls per hour is higher during the noon time i.e. 12.00pm to 4.00pm. In order to offer a better service the only way to solve this issue is to apply more employees during the busy hours but the other factor that the call center manager takes into is that is it going to cause him a loss. So in order to simulate this process employees who are qualified to solve tech call of different types and the employees qualified for sales calls are employed during the peak hours. A call that is subjected to wait time also constitutes cost for the call center. This model also includes the cost that costs for each type of call. For the tech type calls each entity that is subjected to a wait time more than three minutes, For sales calls each entity that is subjected to wait time more than one minute and for order-status calls each entity that are subjected to wait time more than two minutes are added to the total cost. REPLICATION-1

REPLICATION-2

REPLICATION-3

REPLICATION-4

REPLICATION- 5

MODEL 5.4

Bucky, a multi-national holding company, carries inventory of one kind of item called widgets. I(t) denotes the inventory and therefore it is an integer, where t is time (in days) past the beginning of the simulation. Initially, at I (0) = 60 i.e. sixty widgets are in hand. Customers arrive with inter-arrival times distributed as exponential with mean 0.1 day with the first arrival occurring not at time zero but after EXPO(0.1). Customers demand 1, 2, 3, or 4 widgets with respective probabilities 0.167, 0.333, 0.333, and 0.167. If a customer’s demand can be met out of on-hand inventory, the customer gets the full demand and goes exits the system. But if the on-hand inventory is less than the customer’s demand, the customer gets whatever is on hand (which might be nothing), and the rest of the demand is backlogged and the customer gets it later when inventory will have been sufficiently replenished; this is kept track of by allowing the inventory level I(t) to go negative. It is also assumed that Customers with backlogged items are infinitely patient and never cancel their orders. If the inventory level is already negative (i.e., we’re already in backlog) and more customers arrive with demands, it just goes more negative. The details of the customers who experiences backlog are not tracked in this simulation At the beginning of each day (including at time zero, the beginning of day 1), the remaining inventory level is taken into account and based on this information the company decides whether to place an order with the widget supplier at that time. If the inventory level is (strictly) less than a constant “s” i.e. 20 then the company orders another constant “S” i.e. 40.

Inventory Level: At any time in the simulation, this is the inventory level (positive, zero, or negative), and is initialized to 60 here. This variable is the function I(t).

Little s: This denotes the safety stock, initialized to 20. Big S: This denotes the replenishment stock level, initialized to 40. Total Ordering Cost: A statistical-accumulator variable to which all ordering costs are

added; not initialized (so is implicitly initialized to 0). Setup Cost: The fixed cost of ordering. It is initialized to 32. Incremental Cost: The variable (per-widget) ordering cost, initialized to 3. Unit Holding Cost: The cost of holding one widget in inventory for one day, initialized to

1. Unit Shortage Cost: The cost of having one widget in backlog for one day, initialized to

5. Days to Run: The length of the simulation (in days), initialized to 119.9999 Inter-demand time: Denotes the time between every customer arrival. Demand size: Demand per order. Evaluation interval: Time gap between determining the stock level. Delivery lag: Time taken for the replenishment stock to arrive.