Public Safety GIS and Lean Six Sigma’s Shared Mission for Quality

Lean Six Sigma and public safety geographic information systems (GIS) are two completely disparate fields, each with volumes of research published on their respective topics, but they share a fundamental value for quality output. Between these disciplines, there are several types of certifications to ensure a high standard of performance: GISP, ENP, and the four progressive (and colorful) belt levels of Lean Six Sigma. Even though each of these fields has its own separate depth of knowledge, there are some inspirational connections between them.

Lean and Six Sigma are actually two separate processes, with Lean focusing on eliminating waste in the workflow and Six Sigma focused on achieving consistent results. Not surprisingly, both are critical in a public safety GIS environment, and both Lean and Six Sigma concepts individually contribute to better results.

Lean as a Concept in Public Safety GIS

As mentioned, at a high level, Lean is focused on eliminating waste in a process. The diagram below illustrates the traditional way to think about the elements of a workflow in Lean. The process stage is where value is added, transforming product elements to a usable end product.In public safety GIS, it can be helpful to think about the GIS workflow in this framework. Often, GIS data comes from many disparate suppliers (various city and county agencies) and includes addresses from various databases, so to ensure quality results, a process (or value transformation) must be in place to standardize and aggregate this data for a 9-1-1 dispatch center, or for Next Generation 9-1-1 (NG9-1-1), a statewide ESInet.

The term “waste” has a very specific meaning in Lean: it is defined as something that is used in the process that is not required for a satisfactory outcome. It usually adds cost, unnecessary time, labor, or materials to the overall process. Lean eliminates waste by identifying inefficiencies and relies on a culture of continuous improvement.

Value Stream Mapping

One of our favorite Lean tools is called Value Stream Mapping. GIS folks, did any of you look at this picture and notice any similarities to ArcGIS ModelBuilder? They are similar concepts in that they each identify granular elements of a process, visually articulate how they fit together, and then outline the tasks needed to accomplish the end result.

The goal of Value Stream Mapping is to lay out all the steps in a process and identify where there are redundant steps, out-of-order steps, or unnecessary steps. In other words, it is a way to find waste in the process. Once the current state is mapped out and wasteful elements are identified, the team then can design their future state. In one case, a County Addressing Authority found that it took a mere 8 steps to create a new address in its database compared to a remarkable 24 steps to correct a bad address. This is a clear argument for additional QA/QC in the address creation stage, and highlights how small adjustments can have great impact. A great deal of waste can be eliminated by assigning the address correctly in the initial stages.

Six Sigma Means Near-Perfect Quality

While Lean is more of a workflow mindset, Six Sigma is very much a methodology that focuses on near-perfect quality. Six Sigma is an approach to reduce variation within a process, the idea being that variation leads to errors, and errors lead to defects. By reducing variation, there will be a more consistent, reliable, and high-quality product.

Six Sigma is a statistical term which refers to degree of variation (standard deviation for those of you who can recall your statistics classes). Mathematically, it works out to a process that over time will result in no more than 3.4 errors per million opportunities, which is an extremely high standard of quality.

Public safety GIS is the perfect example of an industry that is concerned with high quality. For example: there are many moving parts to the location component of each 9-1-1 call: getting the caller connected to the correct dispatch center, identifying the location of the incident, finding the best route for responders to travel to the incident, and more. Every piece of this geographic puzzle needs to work perfectly for the ideal emergency response.

Error-Proofing and Standardization

In order to reduce errors, public safety GIS can take advantage of Six Sigma tools, including error-proofing and standardization. Error-proofing can be designed into process steps to simply prevent users from making manual errors. A good example of this: the use of domains (a pre-populated list of appropriate options) rather than using a free-form field which increases the possibility for error from information being manually entered.

Our DATAMARK VEP product offers error-proofing during the address creation process. The user is presented with a drop-down list of nearby street segment selections, and the appropriate fields will be automatically populated in the address point layer when the user makes a selection.

Standardization ensures that everyone involved is following the same repeatable, documented process. One of the tools that we’ve developed here at DATAMARK is our “Purposeful Order” process which ensures that agencies are stepping through a logical sequence of steps in order to make their data as high quality as possible. For example, we recommend that agencies work with their neighbors to agree on their boundaries before normalizing their address and road data. The reason for this is that boundaries are a key attribute of address and road data and must be finalized prior to assigning responsibility for normalization. Learn more about our Purposeful Order here: https://datamarkgis.com/app/uploads/2018/06/Datamark-Beyond-98pct.pdf

Lean and Six Sigma are popular concepts in the manufacturing world, but clearly the concepts and methodologies can be applied to improve quality in the public safety GIS world too. Whether you are an addressing authority, a GIS expert, or a public safety guru, these principles can help guide your public safety GIS work.

/ December 1, 2018

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