The Service Optimization Challenge Business Paper

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1 The Service Optimization Challenge Business Paper

2 Table of Contents Introduction The Service Optimization Challenge Avoiding Losses from the Start Dividing Your Territories Is Not Enough Reducing Travel Time Are You Really Balancing the Load? Managing the Inevitable Change Staying Competitive Summary

3 Introduction One of the greatest challenges in service optimization is increasing customer service levels while reducing operational costs. When dispatching field resources, service operations must take into account geography, parts required, breaks, unforeseen emergencies, service levels, and other constraints. Some scheduling systems solve this problem by narrowing down the scheduling options in a sequential step-by-step approach to identify the best technician and time based on a single rule. But this approach often results in suboptimal schedules. Consider all the costs that a service organization incurs lost productivity in the field workforce, high cost of travel, labor spent managing and dispatching the schedule, and missed Service Level Agreement (SLA) penalties or lost customers due to missed commitments. It is easy to see how just a small percentage of inefficiency can create significant costs. This white paper discusses the major factors that contribute to operational inefficiencies in field service. It examines the traditional approaches to solving these issues and their impact on the service balance sheet. From making arrival commitments to customers through creating and maintaining a schedule, we identify the key challenges dispatchers face each day. This paper also provides an overview of how software applications for scheduling optimization can help eliminate these problems. We examine dynamic appointment booking, travel-based optimization, streetlevel routing, dynamic load balancing, and continuous background optimization. Finally, we discuss ways to avoid the costly application code and core changes associated with implementing a new enterprise application. 2

4 The Service Optimization Challenge Optimizing service simply means scheduling resources in the most productive and cost-effective way to ensure sufficient customer service levels. As service workloads increase, two things happen: the amount of time allowed for each scheduling decision decreases, and the number of options that should be considered for who can do what increases. The following two figures demonstrate why no field workforce is immune. Figure 1 compares call volume with the amount of time allowed to make each decision. Figure 1. Call Volume vs. Decision Time To simplify decision making, many companies dispatch any service call received within a certain territory to one of the technicians servicing the area. This does reduce the number of choices and decision-making complexity. However, it disregards the overall workload balance in the field and the possibility of scheduling a resource from a neighboring territory closer to the customer. The underlying assumption is that losing 5 or 10 percent of potential optimization is tolerable and maybe unavoidable. Considering that a 10 percent increase in productivity for a field workforce of 300 resources can be worth millions of dollars each year, competition makes any inefficiency intolerable. Figure 3. Complexity of Optimal Decision Making Calls per Scheduler per Day Figure 2 shows the mathematical complexity of making the optimal decision as the number of options increases. Figure 2. Decision Points vs. Options Minutes for Each Decision At 150 calls per day, each decision must be made in less than four minutes. Number of Decision Points Number of Options to Consider The service optimization challenge arises out of the complexity of finding the best overall field engineer for every job, every time the one who best balances the workload, minimizes overtime, and can arrive within the committed timeframe. This requires the dispatcher to consider the technician, customer, specific task, geography and travel time, parts pickup and inventory, and service-level agreements. Number of Decision Points Number of Options to Consider As the number of options increases, the capacity of the dispatcher decreases. Avoiding Losses from the Start Pre-Defined Capacity vs. Dynamic Appointment Optimization Most service scheduling applications provide customers with commitments for arrival based on a predetermined number of slots for each geographic area, product type, and time window. The problem with this approach is that it does not consider the location of previous service calls or the true availability or skills of individual field resources. When a scheduler does not consider the varying duration of different types of work, late arrivals and 3

5 idle time will be the result. But when appointment booking is optimized, service organizations can identify the shortest route between calls and determine a time that not only is convenient for the customer but also maximizes workforce efficiency. Optimization looks at the existing workload versus workforce capacity in terms of geography, skills, and time, rather than a predefined estimate. The schedule can be continually re-optimized throughout the day, creating further improvements in resource utilization while maintaining customer commitments. In figure 4, the scheduler allotted slots according to customer preferences without considering the location of existing commitments. In figure 5, a service optimization application automatically suggested appointments that would complete the same service calls, along with a 33 percent improvement in travel mileage. Figure 4. Simplistic Benefits of optimized appointment scheduling: Improved utilization by reducing driving time Increased customer satisfaction by providing reliable appointments Reduced time required for keeping customer commitments Dividing Your Territory Is Not Enough Grid-Based vs. True Travel-Based Optimization Some service operations try to reduce unnecessary travel by dividing their territories into zones, but this can lead to inefficiencies for customers near the zone borders. Assigning resources to fixed zones always results in loss of efficiency. As call volumes increase, it becomes impossible to identify and resolve these situations. Many automatic scheduling applications exacerbate the problem by requiring resources to be associated with a zone. Service optimization solutions use travel distance between calls, rather than pre-assigned grids, to look for the closest resource to complete a call. The resource with the minimum travel time to reach a call is consistently assigned, while resources generally remain in their own area. As a result, the optimization of travel time is significantly improved, regardless of geographic area or distribution of calls. Figure 6. Patterns for Consideration Morning Shortest route 36 miles Afternoon Figure 5. Intelligent Clustering These two diagrams demonstrate two resources in bordering territories that are required to travel an additional 16 miles to service four customers. Consider only 100 customers serviced each day, and more than $100,000 may be wasted each year. Morning Shortest route 24 miles Afternoon Benefits of true travel-based optimization: Decreased travel time to service the same number of customers Reduced travel mileage costs to service the same number of customers Increased workload capacity from the existing field workforce 4

6 Reducing Travel Time Linear Distance-Based vs. Street-Level Routing- Based Optimization Some scheduling applications consider travel time using the linear distance between service calls, based on postal codes or pre-defined travel times between service areas. While this approach works well in principle, linear distance solutions cannot take into account important details that can have a large impact on arrival times. This can create a disaster on a call-by-call basis when one-way streets, bridges, bodies of water, or other obstacles stand between customers. Linear travel estimations result in excessive travel costs and unhappy customers. Advanced service optimization solutions include streetlevel routing features that incorporate detailed GIS (Geographic Information System) data. They take into account bodies of water, bridges, one-way streets, parks, campuses, and posted speed. In addition to reducing travel time, this optimization method ensures that you can keep customer commitments. Figure 7. Street-level Routing Optimization Benefits of street-level routing: Reduced number of missed calls and late arrivals due to inaccurate travel times Less time and money lost related to travel by scheduling the actual closest resource Fast identification of closest resources for emergency calls using a schedule map display Are You Really Balancing the Load? Grid-Based vs. Dynamic Load Optimization Balancing the workload among resources presents a significant challenge to many service operations. Often, the number of service calls per technician is based on average call distribution but distribution varies from day to day. When this happens, the problem impacts the entire organization, from service technicians who are underused or overutilized to customers who are upset about waiting for service appointments that were not kept. The same applications that use grid-based scheduling to minimize travel time typically use the same grid-based method of load balancing with the same inconsistent results and consequences. By dynamically balancing the workload without the constraints of grids, service optimization solutions can always schedule based on the true and current workload for each resource. Benefits of dynamic load balancing: Increased utilization of resources in the overall schedule Greater number of completed service calls per day Higher employee satisfaction as a result of not being overloaded or underutilized Figure 8. Dynamically Balancing the Workload Without Grid Constraints Street-level routing takes into consideration bodies of water, bridges, one-way streets, parks, campuses, and posted speed. When this service call was assigned using linear travel, it did not take into account that the red support technician would have to travel to the next bridge and back. This resulted in more than twice the travel time of the blue support technician. Street-level routing would have prevented the waste of four miles and saved 10 to 15 minutes and the associated costs. The first diagram demonstrates how giving one resource more work than can be handled in one day leaves another underutilized. The second diagram shows how dynamic load balancing results in 100 percent utilization and happy customers. 5

7 Managing the Inevitable Change Daily Batch vs. Continuous Background Optimization How does a service operation manage its schedule in light of unexpected events that occur every day? Even the best person or application can only consider the information available at the time of schedule preparation. When changes occur on the day of service, a schedule that was initially optimized now suffers from work gaps due to cancellations or missed customer commitments as resources get delayed in traffic or onsite. The solution is an advanced architecture that enables the entire schedule to be continuously optimized without impacting the dispatching process. This capability takes into account newly received calls and other unexpected events, and reschedules calls t o ensure that resources do not return to the same area twice. How to address planned and unplanned changes that occur while deploying an optimization solution How much development, additional cost, and support will be needed to adapt the solution to business changes How to accommodate changes in scheduling policies and market conditions quickly and cost-effectively Traditionally, the core logic of scheduling applications has been inaccessible to service organizations. One solution is a graphical user interface (GUI) that configures the logic of the scheduling system, including which scheduling policies are considered and how each one behaves. If your organization has varying scheduling policies for different territories, product lines, or businesses, a GUI can help you apply special scheduling policies to specific calls and resources. Figure 9. Service Calls and Commitments This simple diagram of three separate service calls shows how a lack of optimization resulted in a missed commitment for one resource and only 50 percent utilization for the other resource. Benefits of continuous optimization: Increased overall utilization of resources More service calls completed each day Less need for manual or supervisory schedule adjustment Staying Competitive Code and Core Changes vs. Configured and Component Changes Companies that want to cut costs while improving customer service levels will need to make some difficult decisions. They must consider: Whether to invest time and money to build a solution or make significant code changes to an existing application 6

8 Summary Choosing the appropriate scheduling optimization solution can enable your organization to achieve significant advantages in many areas. These include: Less travel time required for completing calls Improved levels of customer service through reliable and responsive schedules Increased utilization of your resources Reduced time to manage the schedule Flexibility to handle short- and long-term dynamic changes within your business Many other factors influence the quality and business value of service workforce management. Putting in place asolution that includes monitoring, data analysis, forecasting, and planning can enable your organization to run more efficiently and more productively. 7

9 About Us planning, scheduling, mobile workforce Available via the cloud or on-premises, our process, while providing them with tools to drive number one choice to deliver superb business service sectors. Contact us Americas +1 (888) (from US or Canada) or +1 (781) ,+55 (11) (from Brazil) Western Europe +44 (0) , Central and Eastern Europe +49 (0)

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