GIS Blog Series – Part 8: Performing Spatial Analysis of Field Work

This is number eight in a series of blogs designed to address Geographic Information Systems (GIS) in conjunction with SAP. We will do this by addressing the most important customer challenges.

Performing Spatial Analysis of Field Work

Since we have already covered the advantages of integrating GIS and SAP systems with regard to planning and executing work, this post will focus more on the benefits of spatial analysis as it applies to analyzing completed work.  As we have been preaching throughout the series, the ability to integrate SAP data with an organization’s enterprise GIS offers many benefits including: 

  • The ability to identify trends surrounding different aspects of asset maintenance giving management a powerful tool to identify and understand ways to more efficiently maintain assets.
  • The use of spatial analysis to help understand where time and money are being spent across an organization’s asset network. This analysis is paramount in being able to identify trends in the ways these two resources are being utilized. 
  • The location and attribute information of completed work can provide significant benefits to forensic work following any unforeseen incident within an asset network.

Let’s explore these benefits in a bit more detail.

Identifying Trends

Aggregating and reporting on the cost of maintaining a specific piece of equipment, or many pieces of equipment in a given functional location is a common practice and can be used to identify trends.  However, these reports tend to be very complex and although trend analysis using tabular reporting can easily identify spending trends for a specific equipment or functional location, it is difficult to identify trends in groups of objects that share the same geography.  Creating a spatial representation of Work Orders though integration with GIS allows dollars spent on maintaining infrastructure to be plotted on a map, easily uncovering trends and relationships between corrective maintenance and location. These analyses allow an organization to fine tune preventive maintenance schedules based on location to quickly identify chronic maintenance issues based on location, improve reliability, and ultimately save money on corrective maintenance costs. 

A popular method of displaying this data spatially is by using a heat map.  A heat map is created using point data.  In our example, we will use the location of completed work orders.  A continuous surface is created by analyzing either the density of the point features or based on an attribute value associated with each of the point features.  In the heat maps we use for our example below, the total cost of the work for each work order is used.  The example later on uses the amount of time spent above and beyond the planned time for each work order operation.

Heat map based on relative dollars spent on corrective work order operations where blue is lower spend and red is higher spend.  Looking at tabular data alone may show a list of objects with higher than normal spend, however, hotspots of dollars spent on corrective maintenance become obvious when viewed on a map and may be associated with the location in which they are installed, prompting further investigation.

Another way to use completed work order data to identify trends is by analyzing actual time spent on specific work order operations displayed on a map to determine if the location of work is affecting the time it takes to perform the same type work across a larger service area. Seeing this data visually will quickly reveal areas that require additional time due to any number of factors.  Although further investigation may be required, identification of this trend by seeing it on a map is very intuitive and far quicker than trying to find trends though complicated tabular reporting.  A slight variation on this analysis can be based on the number of occurrences over a given period which would identify whether a problem is getting worse, or better.  These discoveries can result in the adjustment of the planned time to complete specific operations on preventive maintenance plans in specific areas.  Updating plans with proven data will lead to more accurate planning, scheduling, and budgeting for work in these areas.

In addition to identifying areas where things may be going wrong, the same analysis could be used to determine where things are going right.  In organizations where different regional offices have some level of autonomy around managing their own territory and crews, trends that appear to be going in the right direction could indicate process improvements by territory managers in one territory that could be leveraged and made into a companywide initiative to improve processes across all territories. 

Heat map based on deviation from planned vs. actual time spent on work order operations where blue is a minimum deviation and red is a large deviation.  Immediately it is apparent that jobs that take place in more rural areas took longer than planned in general.  In addition, there is a clear anomaly in the center that will warrant additional investigation.  Given that there are quite a few areas where more time was spent than was estimated, the anomaly in the middle of the map would not stick out in a tabular report.  However, on a map, it is obvious.

Forensic Work

Something that we haven’t touched on much in this series is the idea of an integrated GIS and SAP system lending a great deal of valuable input to many types of forensic work.  Let’s take a look at an example from the rail industry.  In the event of a derailment, the question on everyone’s mind is “why?”.  Using asset and work data displayed in a GIS, along with reference data from other GIS sources, forensic analysts can find the answers to questions like:

  • When and what work was done in this area?
  • What known defects were present at the time of the derailment?
  • When was the last automated test run for this section of track?
  • What materials were used in this section of track?

In keeping with the environmental factors theme in an earlier blog post, answers to the following environment related questions can be added to the overall investigation to shed additional light on the root cause:

  • What were environmental monitoring devices reading at the time of the derailment?
  • Were there any recent weather/temperature events in this area?

These are all questions that will contribute to determining what did or did not cause the derailment.  With the SAP-GIS integration in place, the work-related data is readily available for any spatial analysis required to answer these questions, minimizing the time required to determine root cause.  Additional spatial analysis can then be used to determine where else along the rail line the same conditions exist, allowing maintenance crews to be proactive and address the root cause in other areas of track, mitigating the risk of an additional derailment.

In summary, the spatial analysis of completed work can greatly benefit any organization with assets spread over a geographic area.  This data will drive the identification of trends in asset failure, work order cost, and work order operation duration with relation to surrounding geography, ensuring these issues will no longer go undiscovered.  Finally, this data will greatly reduce the time spent on root cause analysis of asset related failures, including being able to take the results of the analysis and identify any other areas through the asset network with similar conditions, thus mitigating risk of additional failures and costly repairs.


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