Field Service Scheduling Software and What You Need to Know

Scheduling software has long been a foundational technology for field service companies allowing them to meet customer demands.

This article initially appeared in Field Service News – September 7, 2018

 

Michael Blumberg, President of the Blumberg Advisory Group lifts the lid on all of the key aspects of this crucial tool…

If you have spent any time in Field Service, you probably understand the importance of managing service delivery functions against key performance indicators (KPIs). Among the most critical KPIs in the Field Service Leaders track are First Time Fix (FTF), Service Level Agreement (SLA) Compliance or Onsite Response Time (ORT), and Mean Time to Repair (MTTR). These KPIs measure the effectiveness of a Field Service Organization (FSOs) in delivering quality service in a timely manner.

The inability to meet KPI targets may result in exponential costs, customer attrition and loss of revenue; whereas the ability to exceed customer expectations can result in customer appreciation followed by an increase in profit margins and sales. To effectively schedule/dispatch the right technician to arrive on time with the right parts and skillset plays a significant role in meeting these outcomes. This is definitely not a small feat for your typical FSO.

Scheduling and dispatching Field Service Engineers (FSE) poses a challenge for most FSOs, particularly those with more than 5 FSEs. The reason behind this is there are many variables and factors involved.

An FSO with only one or two FSEs and a few customers may not perceive scheduling to be a major challenge. The volume of service requests may be relatively low while the options of who, when and where to send them may be rather limited. Scheduling becomes more of a challenge as the volume of service requests (i.e., customers) and the number of FSEs increases.

Adding to this complexity are the business objectives and/or constraints an FSO must optimize to meet its scheduling requirements.

With additional constraints or objectives, the more difficult it becomes to produce a solid schedule. For example, if the objective is to only meet a response time commitment to the customer, then the decision is easy – assign the FSE who can arrive in a timely manner at the customer’s site.

If FTF, MTTR, and/or SLA Compliance targets are also part of the equation, it becomes even more difficult to produce that solid schedule. Adding a profit margin objective, high call volumes, multiple geographies, and a sizable pool of FSEs, the decision becomes even more overwhelming.

The reason why scheduling is so excruciating of a task is that there are numerous factors that an FSO would need to create and evaluate to determine the optimal assignment for each FSE.

This is a time-consuming activity that requires an extensive amount of computational power to achieve. Many companies have suffered from a loss of time and resources in dealing with confusion and potential human error. The solution is Dynamic Scheduling Software.

Dynamic Scheduling Software provides FSOs with the feature-rich functionality that streamlines, automates, and optimizes scheduling decisions.

This technology ensures the FSO sends the assigned technician to the right job having the proper skill set and arriving on time. These applications typically leverage a scheduling engine that optimizes FSE job assignment. Scheduling engines vary in their complexity ranging from those based on business rules to Linear Programming (i.e. goodness of fit) techniques, Operations Research Algorithms (e.g., Quantum Annealing, Genetic Algorithms, etc.), or Artificial Intelligence (AI)/Self-Learning applications.

The complexity of the scheduling problem, number and types of resources involved, duration of tasks, and objectives to be optimized play a role in determining which scheduling engine is most functional.

Critical factors to consider may include whether the scheduling engine can handle:

  • Multi-day projects or short duration field service visits,
  • People and assets (e.g., tools, parts, trucks, equipment) or solely people,
  • The number and types of KPIs that are part of the objective, and
  • Route planning requirements.

In evaluating Dynamic Scheduling Software, FSOs are also advised to consider the following criteria:

  • Cloud versus On-Premise Deployment Options
  • Speed and Ease of Implementation
  • Integration with Back-office Systems
  • Availability of Real-time Visibility by the Customer
  • FSO Requirements for Best of Breed or Integrated Enterprise Solution
  • Total Cost of Ownership
  • Return on Investment
  • Vendor Industry Knowledge and Experience

There are over a dozen software vendors who offer some form of dynamic scheduling functionality for field service.

Obviously, no two Dynamic Scheduling applications are alike. Each one has their points of differentiation. The best solution is a function of the level of importance the FSO places on each criterion and how each vendor meets these criteria.

Regardless of which vendor is selected, the benefits of Dynamic Scheduling are clear.

In fact, industry benchmarks show that companies who implement these types of solutions can achieve a 20% to 25% improvement in operating efficiency, field service productivity, and utilization. The impact on bottom line profitability and customer satisfaction is substantial. To enable FSOs to provide customers with an Uber-like experience and significant profitability, FSOs should consider deploying Dynamic Scheduling Software as part of their service delivery infrastructure.

Avoiding the Four Biggest Mistakes FSOs make when using Contingent Labour

This article first appeared in the June 18, 2018 online issue of Field Service News.

Michael Blumberg, President of Blumberg Advisory Group  and founder of FieldServiceInsights.com discusses  some of the most crucial mistakes field service companies can make when utilising contingent or seasonal labour…

Field Service Organizations (FSOs) in North America, UK, and Europe are increasingly turning toward crowdsourcing platforms and subcontractors to augment their field workforce.

This type of outsourcing strategy enables FSOs to become more agile in meeting customer demands for service. As a result, they [FSOs] are able to reduce costs and improve service productivity. In addition, crowdsourcing and contingent labour helps solve the problem of finding skilled labour on a rapid basis.

However, turning to subcontractors and crowdsourcing platforms does involve relinquishing some level of control over the labour force. Naturally, questions emerge about the reliability, expertise, and quality of technicians that are sourced through these options.

Over the last two years, we have spoken with dozens of companies who have or currently utilize contingent labour to either augment their existing workforce or gain greater agility and efficiency over the entire field service delivery process. The majority are satisfied with their external providers and report positive results on key performance metrics such as First Time Fix and SLA Compliance/Onsite Arrive Time.   On the other hand, a few anomalies exist where the performance of contingent labour did not meet the FSOs expectations.

Quite often, FSOs who experience subpar performance make critical mistakes when retaining and managing contingent labour.

Here is our perspective on the biggest mistakes they need to avoid:

1. Failure to fully vet individual technicians doing the work

Don’t assume that every contract technician (e.g., subcontractor, freelance, crowdsource) you dispatch has the skills, training, and experience necessary to complete the work properly and in a timely manner. Insist on viewing background checks, certifications, and credentials of every contract technician assigned to your company.

2. Failure to train and onboard technicians

Quite often companies issue work orders without to contract technicians without training or guiding them on how they’d like the work to be performed.

For example, they do not explain how they’d like the tech to greet the customer and/or notify the customer when the work is complete.  Fortunately, Internet-based learning systems make it possible for companies to train and onboard contractors in a cost-effective and rapid manner.

3. Failure to communicate with contractors

This is the biggest mistake that a company can make is hand off work orders as if they were tossing a hot potato over a fence.

This will result in problem with respect to key service performance metrics such as SLA compliance, First Time Fix, and No Fault Found.  It is important that companies provide contractors with detailed and specific instructions about the activities they need to perform on each assignment.

At the same time, contractors also need to communicate with the companies that hire them on the status of calls, issues or problems they are experiencing, and results of their actions.

4. Failure to integrate contract or crowdsourced technicians into their service delivery process

Problems can occur when there is too much of an arm’s less relationship between the company and the contractor.  In other words, there is little accountability, visibility, and control between the company and contractors/technicians, and vice versa.

The key to success lies in treating contractors as an extension of your company.  Companies can achieve this outcome by leveraging communication technology, collaboration tools, and workforce automation software.  Relying on these systems will ensure the company achieves best in class service performance through its contractor network.

In summary, FSOs experience challenges to crowdsourcing when they underestimate the level of due diligence, systems, and processes they need to put in place when utilizing this type of labour. This does not necessarily mean that they must make huge capital investments.

Rather, they are urged to design and implement processes and procedures by leveraging existing infrastructure when they can.

Devoting the time and effort to this initiative will pay off. Our research suggests that FSOs who have an unpleasant experience with contingent labour do so because they rush into the decision without much thought, planning, and preparation.

Basically, they are looking to solve an immediate problem with no consideration to future. In other words, they are taking a tactical approach to labour shortages where a strategic solution is required.

IoT’s surprising impact on revolutionizing inventory management

Sarah Hatfield directs OnProcess Technology’s strategy for products and core service offerings, including the OPTvision platform. She brings more than 15 years of leadership expertise from previous roles in supply chain, product and program management for Comcast, Asurion and ADT.

You know disruptive technologies have reached the tipping point when non-IT pros build business plans around them. This is exactly what’s happening with IoT. Because of its ability to drive wide-ranging, game-changing improvements, IoT is starting to be used across all aspects of business operations. One of the newest, and most impactful, areas is spare parts inventory management, a key aspect of the post-sale supply chain.

Maintaining the right level of spare parts is critical. As you can probably guess, carrying excessive inventory can be prohibitively expensive. But if you have too little, you’ll slow product repairs, hurt customer experience and end up spending more money purchasing new parts for stock replenishment. The problem is, traditional best practices for managing spare parts — using time-series algorithms combined with sales forecasting, seasonality, gut instincts and simple rules of thumb to determine how many parts to stock — are woefully inaccurate because:

  • They’re static, “review-and-stock” endeavors based largely on historical demand data
  • The algorithms don’t account for variables resulting from failed parts in the field

Knowing this, many companies hedge their bets by purposefully overstocking. Others think they’re maintaining the right levels, but unknowingly overstock. In either case, they’re wasting a lot of money.

New IoT-driven inventory planning

The key to accurately stocking parts is knowing which ones are likely to fail and when they’ll need to be replaced. Some businesses have attempted to use IoT data to understand product failure impacts on inventory planning. However, most of the IoT monitoring programs are designed to respond to signal failures. Plus, IoT data collection is often haphazard and emphasizes the few pieces of equipment that are starting to fail, rather than the whole. This makes it impossible to generate a sound baseline for analyzing product performance and predicting failures — which, in turn, makes it impossible to accurately forecast spare parts needs.

The good news is there’s a new inventory planning algorithm that builds IoT-based failure data directly into the equation. Developed at Massachusetts Institute of Technology Center for Transportation and Logistics, it enables businesses to accurately forecast needs. By using this methodology and analyzing historical failure data on the entire installed base, businesses can predict the exact spare parts they’re likely to need, when and in what quantity.

The better news is that it doesn’t take a huge team to capture IoT data because not much data is needed…. Read More