This article is an excerpt from the report Geospatial Intelligence: Powering the Next Wave of Supply Chain Performance.
A copy of the full report can be downloaded here.
In Part Eight of this series, we examine how geographic intelligence is used to enable sustainable and socially responsible supply chains. Here in the Ninth and final installment of this series, we look at the characteristics to look for in a modern GIS system for supply chain applications.
What to Look for in a Modern Supply Chain GIS System
When selecting a Geographic Information System for supply chain applications, most companies will start with a specific use case in mind and that will drive most of their requirements. However, it is important to think about future-proofing, thereby selecting a platform that can be used in many different unanticipated ways to maximize your investment over the long run. It is not uncommon for a company to start with a specific supply chain application and then discover several other supply chain use cases that the GIS platform can be extremely valuable for, as well as additional uses in other functional areas of the company such as sales and marketing, business planning, real estate and facilities management, HR, legal, and so forth. There are potential GIS applications for practically every function in the company. Here are some of the criteria to consider using when evaluating a GIS system for supply chain applications, with an eye to broader use in the future:
Data Integration, Creation, and Management
- Importing geospatial data — The tool should make it quick and easy to import a large variety of formats of maps, schematic diagrams, and geocoded data, as well as the ability to manipulate that data such as mosaicking1 tools.
- CAD integration — Plants, warehouses, other facilities, and yards will often have 2D floor plans or 3D models in CAD format. Plot plans may also be represented in CAD files. If you need local mapping, see if the platform supports your CAD format.
- Authoring and manipulating geospatial data – rich set of tools for modeling, drawing, and manipulating maps and geospatial data sets (for example rubber sheeting).2
- Integration with enterprise systems — For supply chain use cases, a lot of the required data will reside in existing enterprise systems (ERP, logistics systems, purchasing, supplier management, asset management, and so forth). It is critical that the GIS platform supports a variety of ways to integrate that data, including both bulk uploads (regularly scheduled or ad hoc) and continuous near-real-time integration via web services or APIs. See what pre-built connectors are available on the platform, what data they are able to pull in, and what effort is required to make the integrations work, especially if you have customized to a non-standard schema with any of your enterprise solutions.
- Streaming, real-time, IoT data — Increasingly GIS platforms will use streaming/real-time data, often from sensors and IoT sources, such as regularly updated GPS data. This data arrives at much higher frequencies and volumes than traditional enterprise system data, so requires the right architecture to filter, absorb, and deal with it. This could include front-end tools to filter the data and ways to look for events of interest (such as truck started moving or exiting a geofence) as well as interfacing to IoT edge computing devices.
- Data quality and completeness checking and correcting — Data quality and completeness are perennial issues that often receive less attention than they should (they are too often considered to be boring jobs). Ask what tools the GIS system has for cleaning, normalizing, and enriching the types of data you expect to use and for demonstration or proof of how effective they are.3
- Attribute, metadata, and logical relationship tools – big part of what makes GIS maps deeply functional, beyond the geospatial data, is the richness of associated attribute data, metadata, and mapping of relationships between the various entities and objects.
Geospatial Data Sets
- Rich, extensible data sets — See if the geospatial and related data sets you need are already on the platform or will need to be separately purchased and integrated. Also check the depth and breadth of other data sets on the platform for future expanded use of the platform, as well as how broad the support for the platform is among geospatial data providers.
- Free or freemium data — Some resources, such as the Living Atlas, provide enormous collections of maps and data sets at no cost.
- Third party partners — There are large amounts of for-fee data sources and services. Check if the ones you need are already on the platforms you are evaluating. Example providers include insurance, risk analytic, and catastrophe modeling companies (Munich Re, Swiss Re, Veralytic, RMS, etc.); incident and event monitoring services (NC4, iJET, Global Incident Map, etc.); and business data providers (D&B, LexisNexis, Factiva, Semantic Visions, Hoovers, etc.).
- Government provided data — Governments provide an enormous amount of data, often at no cost. This data may or may not already be integrated onto a GIS platform.
- Industry-specific data sets — You may need industry-specific data, such as the locations of oil wells and pipelines, specific types of manufacturing plants, natural resources, rail and port, and so forth.
- Accuracy and currency — Just because a map or dataset exists on the platform you are evaluating doesn’t mean it is accurate or current. If certain data are critical to your needs, it is worth spending time assessing how accurate and up-to-date those are, such as evaluating the methods and frequency for collecting, quality checking, and updating the data.
UI/UX, Mobile, and Collaborative Capabilities
- UI/UX — Does the platform do a good job of supporting multiple roles and levels of expertise? Does the platform provide the depth of functionality and tools needed by serious cartographers, data analysts, and system administrators? Does it also provide extreme ease-of-use in a UI/UX designed specifically for casual users? In many cases, a map will only be used once by an individual, so it must be immediately obvious how to interpret it and what kinds of things they can do with it.
- Mobile first — Mobile capabilities are becoming ever more important for geospatial applications. How rich are the tools for gathering data from the field, to provide ‘crowd-sourced’ data input and checking of data quality? This should include automated GPS position recording (upon specific actions and events), excellent forms authoring and presentation, integration of workflow for step-by-step interactions, and so forth.
- Collaboration capabilities — How well does the platform support teams of people working together to achieve a common set of goals. For example, creating and maintaining a ‘single-version-of-the-truth’ or shared system-of-record; the ability for many parties to view, contribute to, and solve problems, such as a supply chain disruption; shared scorecards and process improvement tracking; sharing of data across broad diverse communities, and so forth.
- Presentation capabilities — How rich and easy to author are the story-telling and presentation capabilities? Story Maps is a great example.
Figure 1 – Example Story Map (Source: ArcGIS Story Map)
Visualization and Analytics
- Visualization capabilities — One of the primary benefits of a GIS system is the ability to visualize data geospatially. This includes how attributes and metadata are displayed through various means such as colors, shapes, pop-up data boxes, auxiliary tables and graphs, and so forth. The intuitiveness of the visualization when default settings are used is important, as in many circumstances those will be used.
- Navigation, Filtering, Slicing and Dicing — Supply chain users will often want to look at subsets of data or navigate to different views. This includes ease of filtering, such as selecting a set or range of attributes (including multiple different attributes) on a chart or on the map, lassoing an area on the map (creating a 2D or 3D polygon), clicking on notes, and so forth.
- Traditional Analytics — Look for pre-built analytics for the types of problems you may want to solve, such as optimal path and routing algorithms, risk analytics including composite risk scoring, incidence analytics (such as location and frequency of types of equipment failure), and so forth. The platform should have built-in analytic tools and the ability to integrate with popular third-party analytic systems.
- Big Data, Machine Learning, AI, CEP — Can the platform scale to handle huge volumes of data? This includes both the tools to manage large volumes and the horsepower to process large volumes of data. What built-in machine learning capabilities are there? Does it have geospatially-optimized
Figure 2 – Example Geospatial Visualization (Source: ArcGIS Developer’s Guide: Visualization Overview)
- Scalability, availability, security, cloud-based — Supply chain applications require many disparate remote parties to connect, scenarios where cloud-based systems have advantages. The cloud platform should be highly scalable, fault-tolerant, and have strong security measures5 in place.
- Workflow — Supply chain applications often require workflow to drive predictable, repeatable execution. The workflow should provide intuitive forms, workflow logic authoring tools that can be used by non-programmers, and good integration with mobile apps.
- Presentation platform flexibility — Support for native, web, mobile, and augmented reality user interfaces and platforms. As mentioned, strong mobile device support is particularly important for geospatial systems.
- Alerting and communications capabilities – bility to send alerts in a wide variety of formats (text, email, voice message, API/webservice, custom devices such as flashing red light and/or audio alarm). Bi-directional communications through a variety of channels.
- Integration — The ability to integrate and embed GIS data and GIS windows in other systems and applications. Tools for cleansing, mapping, and ingesting data from other systems. Streaming data integration capabilities.
Dimensions of Performance Improvements
Geospatial platforms can help improve supply chain performance across many dimensions:
- Building brand equity, customer loyalty, and revenue — Via more consistent delivery performance, provenance tracking to build customer confidence and awareness of sources, connecting consumers more intimately with sources (artisans, farmers, regions), improving sustainability throughout the supply chain and ability to demonstrate it transparently and confidently, ability to identify upsell opportunities, positioning DCs and service centers more optimally to cover more customers — together these strengthen a brand’s recognition and value and grow the top line.
- Improved yields and quality — Monitoring suppliers’ and farmers’ practices, tracking yields and quality, identifying best practices that lead to superior results, and propagating those best practices back throughout the supply chain.
- Reduction in supply chain disruptions, faster time to recovery – n accurate live global operating picture (locations of production, shipments, employees) combined with a more complete assessment and awareness of potential risks enables focused mitigation efforts on highest impact risks, monitoring and earlier awareness of impending disruptions, more comprehensive situational awareness, and better coordination of multiple parties to bring production back online quicker. Fewer disruptions and faster recovery creates a more competitive organization with higher revenues and profitability. In fact, when a group of competitors are all disrupted at the same time by some event (like the Japanese Tsunami, for example), the firms that recover sooner often gain market share (sometimes permanently) because the others are unable to deliver.
- Reductions in inventory, while maintaining or improving service levels — Optimization of locations and quantities of service parts inventory can achieve lower inventory levels, while maintaining or improving service levels in the service supply chain. Dynamic precise ETAs provide much earlier warning to delays, allowing managers to find alternatives. Thereby, they can reduce the safety stock levels at plants and DCs across the supply chain without increasing out-of-stock rates. Lowering inventory levels frees up working capital and improves RoWC,6 which investors and managers care about.
- Driver and vehicle productivity, delivery timeliness — More accurate maps, better route optimization, and more precise delivery information increase driver productivity, enable more stops per hour, and decrease driving distances and fuel use.
- Delivery timeliness – ccurate maps and route optimization also enable higher on-time/in-window delivery rates. For those times when deliveries are running late, the dispatcher will know sooner. The customer can be notified earlier and given a more precise updated estimate on expected delivery time. If logistics and delivery is an important part of your organization, these can make a material difference in customer satisfaction and retention.
- Reduction in capital expense and labor — Reducing the number of assets through better visibility and higher utilization, less time spent searching for the right tool or equipment.
- Reducing service costs while increasing uptime — Better scheduling and utilization of a fixed set of service resources (technicians, equipment, parts). Predictive condition-based maintenance leads to fewer service calls and fewer breakdowns (higher uptime).
- Reduction in cargo spoilage, damage, theft — By monitoring cold chain conditions, shock and vibration, geofencing and unexpected stops, you can reduce damage, spoilage, and theft, often dramatically.
Implementing supply chain applications of GIS does not have to be an intimidating or daunting prospect.
Here are some pointers on how to get started.
Think Big, Start Small
In life, and in implementing a GIS platform, it is important to know what you are trying to accomplish and where you want to go. Your immediate motivator may be solving a specific pain point, but you should also know the possibilities, where these new capabilities can take you. Having a vision of where you want to take your company over the medium term and long run can help set priorities and guide your roadmap, as well as sustain momentum and investments through challenging times.
While having a compelling long-term vision is highly valuable, we recommend starting small with implementation, getting value quickly, and making incremental improvements. Software engineering organizations have embraced agile development. Startups seek to ship the ‘minimum viable product,’ as soon as they are able. Similarly, an agile business should seek to implement the ‘minimum viable implementation,’ to get the fastest time-to-value without waiting until they have the perfect system. From that foundation, they make adjustments and add capabilities in ongoing, rapid, incremental steps. Here it helps to remember “don’t let perfect become the enemy of good” and be willing to use out-of-the-box configurations to start, rather than customizing too much in early stages.
Identify, Adjust to, and Fix Data Issues
It is also important to identify, understand, and fix your data limitations. It is common that some key needed data is either not available or is in such bad shape as to not be useable for high consequence applications. In that case, you may need to adjust your initial goals until those problems can be fixed. The project can be used as an impetus to clean up sources of data that you need.
Change Management — Remembering the People
It is one of those clichÃ©s that happens to be true — people really matter. Starting at the top, it is vital to have a passionate and involved executive sponsor, to ensure that the right messages (at sufficient frequency) are being given by top management, and that the project is properly funded and has the executive support needed to make it through any rough patches. As well, management must be willing to commit the resources and allot the time needed for the stakeholders from all the various impacted functions and IT personnel, as well as (potentially) persuading trading partners to commit adequate resources too.
Most important is to connect to the people on the front lines, those who will actually be using the system. An angry rebellious user base that felt they were never consulted can kill a project in no time. It is critical to clearly explain why the company is doing what it is doing and what the goals are. Fear of the unknown has doomed many initiatives. When people hear about a change and are left in the dark, they start speculating (e.g. ‘If they are automating this, there will probably be layoffs,’ etc.). Furthermore, their input and feedback are invaluable. The front-line workers are the ones that know how things actually work on the ground. It is better to communicate early and often, involve the workers, solicit feedback, and be very open to hear out concerns.
It is good to have a representative from each key area that will be impacted and/or using the system, whether it is the supply chain planners, truck drivers, dispatchers, network operators, or service technicians. They are the ones that can help you figure out what will and won’t work early on (rather than creating a crisis by only discovering major problems at the end of the project during rollout). Prototyping often works better than trying to explain in words what you are trying to do. Functionality is incrementally added to the prototype until it is ready for a broader group to try out.
Hiding Complexity, Building in Metrics and Incentives
The great thing about a well-designed GIS supply chain application is that most of the complexity can be hidden. For example, a driver or repair technician who is receiving step-by-step instructions does not have to be exposed to the overwhelming complexity and volume of data or the details of the algorithms being used behind the scenes. Those powerful analytics are used to make the workers’ jobs simpler and, in many cases, reduce the grunt work. To them, it should appear simply as a very intuitive aid helping them do their jobs better, faster, easier — and critically, with less administrative work, not more.
Another way to maximize results and engagement is to build in KPIs for proper use of the system, measuring the quality of the data being entered or captured, and how well people are following the system’s recommendations. If people are measured on the right things, and scores are made visible, and awards handed out, it can capitalize on people’s competitive nature to help everyone work towards common goals of improved overall performance for the business unit and company.
The possibilities for supply chain applications of GIS are endless and may seem overwhelming. This is why we advocate picking modest initial goals and just getting started with something. There are probably one or two specific supply chain challenges that are driving you to consider the need for a GIS platform. If you just start, do something, even if it’s not perfect and even if it’s an experimental small step, then you will start learning what works and what doesn’t. That process will make you and your team smarter and you will start to see the fruits of labor. This is within your grasp and you can create real meaningful changes and improvements for your organization. A good GIS platform, coupled with the right team, strategy, and implementation, can be a powerful driver of improvements for your organization in your supply chain journey.
1 Mosaicking is the ability to ‘stitch’ together multiple overlapping images (such as aerial photographs of an area) to create a single seamless image. — Return to article text above
2 Rubber sheeting is the alignment of two or more maps and/or geospatial data sets by stretching or reorienting them to align known equivalent points on each. This can be used to adjust for errors or distortions in either data set. — Return to article text above
3 Automation of data cleansing and data completion is difficult, and no tool will get it 100% right or 100% complete.
A tool that gets you 90% of the way is preferable to one that only does 50% of the job. — Return to article text above
4 CEP = Complex Event Processing, where the platform continuously analyzes large volumes of many different events to trigger alerts and actions when certain conditions exist. A geospatially optimized CEP can efficiently monitor tens of thousands of moving 3D polygons (such as a polygon around moving aircraft) and execute rules and analytics on those observations. — Return to article text above
5 The Cloud Security Alliance has some good resources including the Security Guidance for Critical Areas of Focus in Cloud Computing. — Return to article text above
6 RoWC = Return on Working Capital — Return to article text above
To view other articles from this issue of the brief, click here.