Selection of technician in Field Services using a ...

Twenty Ninth National Convention of Production Engineers and National seminar on Green Manufacturing Strategies, 2014 Paper ID - NCPE/2014/16

Selection of technician in Field Services using a Mathematical Model G. Jegan Jose1, S Kumanan2 1

Research Scholar, Department of Production Engg. NIT, Tiruchirappalli - 620015 2

Professor, Department of Production Engg. NIT, Tiruchirappalli - 620015 Abstract

We live in a complicated world where multiple voices must be heard and understood from customer. Amplified voice of customer is making it very clear that readiness to serve customer demand at the field have a massive impact on the overall customer satisfaction. Alternative resource allocation is one of the most promising problems in the field service management. However, good dispatching solutions rely on human intelligence. Therefore, a decision making system under a dynamic and uncertain field service environment is needed. Exploiting the heuristics, traditional techniques as well as knowledge based analysis this paper presents a task-based resource allocation mathematical model to assist the decision makers for resource selection. The objective of this model is to optimally allocate resources, while considering job priorities using sub function analysis to prioritize jobs and to allocate resources. Numerical results show that the proposed task-based resource allocation model TBRAM has an advantage of providing more service with lesser resource in a minimal service time. Keywords: Field Service Management, Optimal Resource allocation, multi-skill resource scheduling, mathematical model

1. Introduction Field service management (FSM) is an attempt to firstly schedule and then secondly optimize for and thirdly dispatch for service processes and information needed by companies who send teams of technicians or other staff "into the field". Optimization is difficult since it involves multiple scheduling and dispatching of technicians to different locations, while minimizing cost and maintaining good customer service. FSM most commonly refers to companies who need to manage installs, service or repairs of systems or equipment. Researches confirm that leading organizations continue to strengthen the three pillars and ensure that they have right technicians in place powered by right tools to deliver the desired experience to customers. Figure 1 explains the process map of the field service business. Companies considering implementing enterprise technology to optimize field service scheduling and management have a wide range of options, with a variety of software applications available to help manage the route and schedule of field service technicians and others that need to be dispatched to remote sites in order to carry out their work. That would depend on a number of variables, including the number of technicians, the number of jobs each technician handles in the course of a day, the degree of time sensitivity of each call and the degree to which the schedule may change during the day. It is also important to consider that the demands placed upon a field service application today may change quickly due to business growth, customer demands or competitive pressures. It

is essential for a business or organization to select and implement field service scheduling technologies adequate for future as well as current needs.

Figure 1 Field services process flow An important factor which has the major impact on customer satisfaction is the service response time. It is the time difference between when the customer reports failure and the technician shows up at the customer site to take care of repair work, as shown in Figure 1.1. The elapsed time from when the technician starts to travel to the customer until he/she leaves the customer is noted as service time. Generally, a mean response time target is set in order to maintain a competitive edge in the market. A guaranteed response time may also be explicitly written in the service contract, and the service provider will be assessed a financial penalty if the customer’s request cannot be fulfilled as promised. In reality, there are a lot of different factors and uncertainties which have an impact on the response and service time, such as travel time, machine failure rate, customer location, on-site repair time, etc. It may be very difficult to determine accurate response times for a general field service system, and analytical results are only available for some special cases in the literature. A

number of researchers have investigated field services over recent years, and the present work discusses further investigation from a perspective of better resource utilization.

Figure1.1 Field Service Cycle Time

1.1 Literature Review Watson et al. [1] developed a simulation met model to improve response-time planning and field service operations Collins and Sisley [2] conducted optimization and constrained heuristic search to schedule resources. They concluded that field service cost should be lowered by making operational decisions carefully and efficiently. Lin et al. [3] developed a simulation model for field service to estimate the value of a conditioned-based system. Duffuaa et al. [4] described a generic conceptual model, which can be used to develop a discrete event maintenance simulation model. Hori et al. [5] developed a diagnostic case-based reasoning system, which infers possible defects in a home electrical appliance. Szczerbicki and White [6] demonstrated the use of simulation to model the management process for condition monitoring. They described how to use simulations to estimate the performance of the field service model. Lin et al. [7] presented a design and simulation model for field technician dispatching. Tang et al. [8] developed an approximation manpower planning model for after-sales field services support. They proposed a travel distance approximation model over the classical “square root law”. Aberdeen’s research group [9] and [10] provided an in-depth and comprehensive look into process, procedure, methodologies, and technologies with best practice identification and actionable recommendations. According to the Aberdeen’s group, in a field service survey of 220 organizations, 65% of incoming service requests require a field visit or a dispatch. Nearly 26% of these dispatches require secondary or additional follow up visits, thereby making the effective management of field resources and the overall field service organization extremely vital in the pursuit of service excellence. Julien et al. [11] suggested that scheduling algorithms be characterized as having result comprehensibility or algorithm comprehensibility. The findings stress, on the one hand, that result comprehensibility is necessary to achieve good

production performance and to limit complacency. On the other hand, algorithm comprehensibility leads to poor performance due to the very high cost of understanding the algorithm. Narongrit Wongwai et al. [12] presented resource substitution step where an activity with higher priority can claim any resource regardless of its concurrent activities' resource requirements. Fowler et al. [13] implemented a generic algorithm as an alternative method to obtain better solutions. The heuristics were applied to realistic manufacturing systems with a large number of machine groups In spite of the many research results, experienced dispatchers mainly make dispatching decisions in current field service department practice. Veteran dispatchers have developed many empirical or heuristic rules for both situation assessment and task priority determination over the years. However, there are a few problems with current practices: (1) Good practices only rely on human intelligence; (2) Dispatching knowledge is implicit; (3) Training new dispatchers is not an efficient use of veteran dispatchers' time; (4) Dispatching knowledge documentation has been important but often overlooked. The purpose of the present work is to investigate the detailed structure of scheduling and hence to optimize the process so as to allocate the resources optimally. In other words, present work aims at to allocate right resource to right task. 2. Problem Description Companies often fail to utilize their resources fully and end up in a situation where it becomes challenging to manage and assign enough technicians for repair jobs. While a company continues to rely on manual processes in the face of complexity and volatility, dispatchers adopt strategies to make decisions simpler and easier to cope with, by focusing on a subset of the requirements. One mental shortcut that many dispatchers rely on is, for example, to simply send the nearest technician to each job. Dispatchers may also wind up making scheduling decisions based on personal matters, and not focus on the overall business objectives. This places a great deal of emphasis on the relationship between the dispatcher and the technicians, and a lot of reliance on the skills and knowledge of the dispatcher him or herself. Optimal resource allocation to provide more service with lesser resource in a minimal service time with least repair cost. Minimize Cr = ∑

subject to all possible i’s

Cr: Field Services Total Repair Cost Ci: the cost of ith factor contributing towards Cr

3. Task Prioritization and Resource Allocation To help dispatchers make service task tradeoffs and to minimize the total Field Services Repair Cost, we propose a two-step methodology “Task-Based Resource Allocation” 1) Task Priority 2) Optimal manpower allocation. To prioritize waiting jobs and allocate resources, Sub-Function and Preference Score analysis is done. Task priority takes care of situations where managing enough technicians to perform a certain repair job becomes difficult and gives more importance to critical waiting jobs. Optimal manpower allocation allows dispatcher to fully utilize resources in an effective manner to perform task efficiently in minimum possible time, avoiding rework and hence minimizing repair cost. This also ensures maximum possible technician allocation in case of insufficient available service team. 3.1 Sub Function Analysis Logical sub function analysis is used to perform optimal resource allocation and task priority determination. Function analysis is a decision making approach in which a problem is broken down into its component functions, called as sub-functions and subsub-functions. Sub function tree with ten sub functions is built, shown in Figure 2.We turn now to study the sub functions. A Technician of desired skills for the repairs’ job is looked, subject to the condition that the job is finished before the customer promised date. Technician location and transportation mode plays an important part as farther the distance travelled, the longer it takes to complete the task. As time costs money, hence it causes increase in repairs cost. Distance to travel is calculated based upon the longitude and latitude information of the technicians and task location.

Figure2. Sub function tree We can have various modes of travelling, road, air, say. Time taken to travel form one location to another involves two costs, the cost of transportation and for the technician. The transportation mode should be chosen in

such a way that the total cost should be least. The distance to travel calculation and the cost comparison of road and air route is achieved by Distance (in miles) = (R * c) / 1.6 and X1 + R1* δ1 + T1 * (δ1/50) < = > X2 + {(δ2400) / [α + β*(δ2)ϒ]}

Where: c = 2 * atan2 ( √ , ( √

);

a = Sine2 (|δ (Latitude)/2|) + (Cosine (Latitude1)) * (Cosine (Latitude2)) * (Sine2 δ (Longitude)/2) R = Earth’s radius (= 6371 km, mean radius) and prefix 1 and 2 corresponds to Technician and service location respectively x1: x2: δ1: δ2: R1: T1: α,β,ϒ:

Fixed taxi fare Fixed air fare Ground distance Air distance Taxi fare per mile Billing rate of technician Air Fare per mile parameters

Let’s say δ0 satisfies the above equation. Then if distance to travel is more than δ0, ideally air route should be followed otherwise road transportation. Performance of the Service team depends upon the skills of technicians. Technician performance and its related experience take care of productivity. To look at productivity or service window accuracy in isolation is dangerous as it doesn’t take into account the ability of the service organization to ultimately resolve the customer issue. Hence, resource allocation and scheduling, that ultimately ensures that the appropriate technician is selected for a specific job based on skills plays an important part and should be done efficiently. All the terms and conditions of after-sales service are written in the Service Level Agreement (SLA) and the service provider will be assessed a financial penalty if the customer’s request cannot be fulfilled as promised. The onset of fatigue while at work can decrease a person’s alertness. Fatigue reduces work performance mainly by interfering with concentration and increases the time needed to accomplish tasks. Research studies have shown that the chance of making mistakes at work increases significantly due to fatigue. Multi skill selection plays an important part in an optimal utilization of the available resources. If a technician with multi skill, say winder and welder, is used instead of welder for a job which requires only welding skills, then this means that the available resource is not fully utilized.

Depending upon the field service situation and hence various sub functions, waiting tasks are prioritized. Different weightages are assigned to various sub functions, which are used to calculate the preference score. Once the tasks are prioritized, technicians are allocated based upon the technicians preference score corresponding to the task priority ranking. Task's and Technician’s attribute values, sub-function weights are multiplied with Function values and sum all together to get a preference score for each task and technician. The task performing decision is made by ranking the preference scores of all the waiting tasks and for each task, available field technicians are allocated based upon the preference score of each technician. 3.2 Preference Scores Different dispatchers will assign unlike set of weights in the sub function top-down tree. Different weight settings result in varied dispatching results. However, a good dispatching strategy, by an experienced dispatcher, with a good weight setting under a specific service situation achieves high customer satisfaction and low service cost. An improper weight setting is selected by a new dispatcher or a dispatcher's mistaken operation. Therefore, a score analysis is required. In a specific filed service situation PS (i) =

∑

PSP (l) =

∑

I J L N M T A (i,j) B (l,j) W (j) Ώ(i,j) ῼ(l,j) PS (i) PSP(l)

Index of waiting Task Index of Sub Function Index of the Available Technician Total number of waiting tasks Total number of sub-functions Total number of Available Technicians Attribute Value of the ith waiting tasks corresponding to jth Sub-Function Attribute value of lth Technician corresponding to jth Sub-Function Weight of jth sub-function Attribute Value of the ith waiting tasks corresponding to jth Sub-Function Function value of lth Technician corresponding to jth Sub-Function preference score of ith waiting task preference score of lth Technician

The dependence of “ ” on distance to travel δ, is cubic in nature, say, with a, b, c and d be the constants and the boundary conditions be 1, 1, and 1 corresponding to , and respectively. Mathematically this can be represented as:

Combining above equations, we have: (

)

(

) ( )

3.3 Top-down decision tree A decision tree is a decision support tool that uses a tree-like model of decision and their possible consequences. Figure 3 shows a top down decision tree of TBRAM. We start with all the waiting jobs and all the technicians. Functional analysis is done by calculating preference scores to prioritize the various waiting jobs. From the pool of technicians, based upon the availability and skill analysis, two possibilities are shown in the decision tree. In one condition, sufficient technicians are available to perform the repair’s task or in other case enough technicians might not be available. In the former case sub function analysis is done by calculating preference score of all the available technicians and hence optimal manpower is allocated to the waiting tasks.

Figure 3.3 Top-down decision tree In the latter case, one possibility is to go with multi-skill technicians or we have to wait until the technicians with desired skills become available/accept the job. Even if after going with the multi-skill option, enough number of technicians might not be available, so in that case also we have to wait until the next available technician

accepts the job. If in case of multi-skill selection, enough technicians are available then sub function analysis is done so as to get the optimal manpower for the waiting task 3.3 TBRAM Algorithm Step 1. Step 2.

Step 3.

Step 4.

Step 5.

Step 6. Step 7.

Update the list of pending jobs using “Pending Jobs Update Form”. Based upon the sub functions, weights and attribute values, calculate preference scores of all the waiting tasks. Calculate preference score of all the available technicians’ corresponding to the task having highest preference score. Assign resources based upon the preference score. If enough technicians are not available, go to Step 5, otherwise go to Step 6. Go for the multi skills resource allocation. If enough technicians are available, assign technicians based upon the preference score. If enough technicians are not available go to Step 6. Wait until the next available technicians accept the job. Repeat step 2 to 6 for all the tasks in order of their preference scores

4. Numerical Example To evaluate how close a TBRAM conforms to an experienced dispatcher’s decision, a case study from a manufacturing industry is given below. The dispatcher has to assign technicians for three waiting: Job1, Job2 and Job3, say. Different pending jobs require different set of technicians with different skills. For Job1 and Job3 all the three skills, Winder, Welder and Mechanist are required but for Job2 only Mechanist is required. The assignment should be in an optimized way so as to reduce the repair cost to least possible value. In a specific considered field service situation, various constraints and their values are as under: Skills:

Winder, Welder and Mechanist

Transportation Mode:

Road, Air

Technician Performance Factor ϕ_t:

0-10

Technician Experience Factor ϕ_e: 0-10 Fatigue level:100% for no rest and 0% for 2 days rest Certification:

Yes or No

x1:

$2

x2:

$ 100

R1

$ 3/mile

T1:

$ 30/hr.

The constraints for parametric equation for distance travelled are as below. (0) = 10; (60) = δ0 = 9; (1000) = 7; (10000) = 1 Further we have (

)

the

cubic

(

dependence

as

) ( )

5. Conclusion and Future Scope The dynamicity and uncertainty of field service environment are very high. Excellent field service management is the key to the best service at the lowest cost. The emerging research of reducing field service cost and increasing field service performance becomes more and more important. In this paper, we deal with manpower allocation in the field service department. Most of the heuristics, traditional and knowledge based techniques are either extremely cumbersome or become seldom possible for multi-project environment. So the good dispatching practices heavily rely on human intelligence. Hence, a decision tree based TBRAM for field technician assignment has been proposed. It adopts knowledge engineering method to extract expert knowledge for job prioritization and multi-functional analysis to calculate preference score for task priority and technician assignment. Numerical example is solved using the proposed technique and is found to be competent. While developing TBRAM, we have considered that for a particular repair task, all the technicians require same type of skills but in a real scenario, technicians with different skills might be required. In this case it is required to run TBRAM repeatedly so as to assign technicians with desired skills. TBRAM can be extended to develop a model with flexible “technician – skill” selection. Other attempts should be made to develop an optimal resource selection model using analytical hierarchy process for after-field services support.

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