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.
The Role of Geospatial Systems in Supply Chain
This is the first installment in a series of articles about the role that geospatial intelligence and systems play in supply chain management. Here in Part One of the series, we discuss why geospatial intelligence is so important in supply chains, what GIS systems are, how they generate value by integrating and visualizing supply chain and related data, and leveraging the GIS platform as a collaborative hub.
The Rise of Location-Aware Organizations
We are witnessing the increasing instrumentation of factories, vehicles, ports, distribution centers, and stores, with sensors and GPS or RTLS.1 When integrated into digital supply chains (where paper and manual processes have been replaced by digital automation), it creates the ‘location-aware enterprise’; one that has nearly complete real-time visibility of critical assets, inventory, personnel, facilities, and situations throughout its multi-tier supply chain. The location-aware enterprise employs machine learning and GIS2 technology to predict and preempt issues in the supply chain, mitigate disruptions before they happen, and continually improve supply chain performance. It moves from reactive firefighting to proactive innovation.
The reality is that most companies are far from this ideal. They struggle to get even basic visibility, such as where their assets are or when shipments will arrive. This does not mean that location awareness is some nirvana that is decades away. It is not difficult to take immediate incremental steps on the journey and start to get material improvements right now. In fact, taking these steps is critical for competitive survival in the twenty-first century for any company with a supply chain.
Physical Supply Chains Demand Location Awareness
Supply chains are inherently physical. They are all about making, storing, and moving physical commodities and products. This requires location awareness and context-awareness. The digitization of a supply chain does not magically remove the physical constraints, but it does increase the timeliness, accuracy and granularity of data about the supply chain. That is why in the supply chain, as in real estate, it’s all about location, location, location.
What is GIS?
A GIS (geographic information system) captures, normalizes, integrates, manages, analyzes, shares, and visualizes almost any type of information — all within a geospatial framework. The geospatial framework integrates 2D and 3D maps of a geography, building, campus, or other space with various data related to locations on the map. It usually allows a temporal component as well, to visualize and analyze changes over time.
The information being ingested by the GIS may come in many formats: raster and vector maps, tabular/relational data, streaming data such as GPS or temperature sensor feeds, unstructured text such as news feeds or descriptions, images, and more. Data can come from virtually any source including: spreadsheets, enterprise systems (ERP, Procurement, WMS, TMS, supply chain, CRM, etc.), map data providers, public data sources (such as government census data), third-party sources (such as D&B information, weather forecasts, traffic, industry-specific data like location of retail stores or oil wells, etc.). Supply-chain-related examples include:
- Supply Network — Location (address or lat-long)3 of suppliers’ factories, types and quantities of components and materials produced at each factory, which of your products are dependent on the material or component produced at each location (this requires an exploded BOM)4 type and level of various risks in the vicinity of factories (e.g. natural disaster, political, environmental, etc.) and more.
- Distribution Network — Location of current and potential distribution assets such as your DCs, your distributors’ DCs, dealerships, retail stores, and individual customer sites. Information about the inventory, equipment, people, and other assets at each location. This enables network planning, such as understanding where you have overlaps and gaps in coverage for your DCs and/or stores, and enables execution-time analytics, such as optimal order fulfilment.
- Service Network — This includes knowing the location of service facilities, location and disposition of inventory and repair equipment, location and equipment to be serviced at the customers’ sites, and the real-time location and status of technicians. When selling services with delivery-time SLAs, it is critical to know driving distances and typical driving times.
- Shipping and Delivery — Real-time feeds of the location of shipments, the planned route, geofence around the route to show deviations, planned and currently predicted ETA,5 the original purchase order for the shipment, origin and destination addresses, various carriers’ information, shipment information such as BOLs,6 customs information (duties, tariffs, customs documents), and so forth.
Virtually everyone involved with ensuring the success of supply chains takes location into consideration on a daily basis in one way or another. There are very powerful geospatial tools and applications available. The richness and precision of possible analysis increases every day. Those who are leveraging these tools are realizing a tremendous competitive advantage. Those who continue to rely on traditional approaches fall further and further behind.
Visualizing Supply Chain Data
One of the reasons geospatial systems are so valuable for supply chain applications is their ability to visualize information on a map. As described in Visual Explanations, by Edward Tufte, the way a given set of data or information is presented makes all the difference in how quickly and easily people absorb and make sense of it and reach the right conclusions. In fact, the incorrect presentation may cause incorrect critical decisions to be made with potentially disastrous results.7 For supply chain use cases, presenting data on a map is often far better than presenting it in other formats. GIS systems can combine live operational data, supply chain network data (such as the location of plants, ports, routes, etc.), and other multilayer geospatial data into a single view. This can provide a much better understanding of the supply chain network, its current and future operational status, and opportunities for improvement.
For example, suppose a hypothetical company wants visibility into home delivery from their five distribution centers to the greater San Francisco area, including an understanding of the hours required to do the deliveries, how many deliveries fit in the 9:00 to 5:00 window, and which orders may not be accommodated by the current plan. Below we show and contrast four different ways of presenting data about this scenario: 1) Prose description, 2) Table, 3) Charts, 4) Map.
Figure 1 – Prose and Tabular Presentation of Delivery Data
Prose is usually the hardest way to glean insights from this information. It takes many paragraphs and a lot of diligence by the reader to understand what is being said. The table is an improvement — the data is organized, and we can start to make comparisons. Listing all 189 stops in the table, however, becomes impractical.
Putting the same data in charts, provided they are well-designed and organized, can make it quick to compare and contrast different stores. However, as shown on the next page, a map is by far the best of all these techniques for visualizing the spatial relationship between the DCs, the delivery points, routes taken,
and unfulfilled orders.
A good GIS system allows maps, tables, and charts to appear together in one view, providing the best combination of information presented as needed. It also provides interactivity on the map, such as hovering over or double-clicking on a DC, route, or delivery point to find out more details about it or providing various intuitive ways to filter the data.
In the map in Figure 3 above, deliveries are color-coded by which DC is serving each one, making it easy to tell at a glance where each order is fulfilled from. In addition, unassigned orders (light grey) are easy to quickly identify. The map rapidly communicates much more information in a context-rich way, giving the viewer levels of understanding they couldn’t get with the other forms of data presentation.
Geospatial Platform as a Collaboration Hub
Without a GIS, geospatial data is usually used in siloed ways. Each user only uses one or two types of data for a narrow purpose, often within a specific application. In order to share the data with others, they will typically take some sort of ‘snapshot,’ which may be literally a screen capture image, pasting it into a PowerPoint slide or word document, or a spreadsheet into which they’ve imported the various data they need to communicate their insights or points. Without a GIS, it is very difficult to create a shared visual data, on which all can collaborate on. It can be difficult for diverse and geographically scattered users to contribute diverse types of data. Thus, a lot of potential collaboration, communication, and insight-surfacing is left unrealized.
Geospatial platforms often play a role as a collaboration hub, ingesting very diverse data from numerous sources and letting those data be mixed and matched on different map layers, in a visually intuitive way. Different participants can develop and use myriad different views of the same data, to accomplish each of their own goals within the overall end-to-end process or ecosystem. As well, users who are onsite or have otherwise gathered specific relevant information can straightforwardly contribute it to the platform. The platform becomes a vehicle for collaboration across functions with an enterprise, and between enterprises across multiple tiers of a supply chain or ecosystem, providing a shared single-version-of-the-truth.
Taylor Shellfish Farms’ Digital Transformation with GIS-based Collaboration
Taylor Shellfish Farms (TSF) is the largest aquaculture8 producer of shellfish in the U.S. and provides a good example of the use of GIS as a collaboration hub. They are vertically integrated — in addition to farming, they also do the processing of the shellfish, and own several retail oyster bars. A family business for five generations (since 1890), Taylor Farms has 10,000 acres of oyster farms under the care of about 30 different farmers. They let the farmers innovate, but also provide them with best practice and sustainability guidelines. They are the only U.S. company certified by the Aquaculture Stewardship Council for operating in a way that minimizes impacts on the environment and other critical species of the Puget Sound (such as salmon and forage fish) while providing a living wage, benefits, time off, and safe conditions for employees.
Taylor is undergoing a digital transformation. It started with the implementation of an online ArcGIS system which they used to replace the labor-intensive hand-drawn maps they had been making for all the permits they had to submit. Now they use GPS to more precisely locate their farms and drones to get better imagery of their farms.
Once they had a GIS system and digital maps in place, they realized they could use it for operational improvements as well. Each of their farmers operates independently, with their own crew. TSF recognized that having all that information and expertise in each farmer’s head or on paper was not the best way to improve operations. Using GIS as a collaborative information hub, they started gathering data. While the farmers and their workers are out in their oyster farms, they use a mobile app to record information such as the date and quantity, and location of the crop planted, the exact number of oysters and pounds harvested from each location, treatments applied for each location, and various maintenance tasks they perform. TSF also operates its own oyster bars where shuckers open 1,000s of oysters every day and provide feedback into the system about the quality of the oysters. With all that information from the entire end-to-end operation now in one centralized system, executives can get insights they never had before. Some farmers do certain parts of the job better than the others.
By tracing where each oyster originated, the company can find out which locations and times of year are producing the largest quantity and highest quality of oysters. They examine the data on quality from their oyster bars, trace it back to the source farms, look at yields of each location, and combine it with the operational information recorded by each farmer. All this information is brought together in the GIS platform to drive a data-driven approach to discovering why one farm is performing better than another. They can see the differences in the yields and quality and the way each specific location is run, quantify the gains from specific approaches, and push those newly discovered best practices to the other farms.
Taylor Farms would like to get to the point where they can trace every single oyster back to where it was farmed and who its parents were. By analyzing which oysters grew the fastest, and which had the highest survival rates, and measuring other desirable qualities, they can improve their breeding program to create better oysters, combining the best qualities such as high survival rates and high growth rates, and high quality.
GIS Across Supply Chain Phases and Time Horizons
Applications of GIS are used across different time horizons (strategic, tactical, execution, and real-time) and across four major categories of supply chain uses: 1) Planning and optimizing the network, routes, etc., 2) Monitoring geopolitical risks, the competition, supply and demand, shipments, etc., 3) Responding to events by building new facilities, identifying alternate sources, responding to emergency situations, etc., and 4) Improving, continually applying lessons and improving processes, culture, and structure to remain competitive over the long run.
There are a number of ways that GIS systems help with these various supply chain use categories:
- Geospatial Visualization — Maps with layers are great for visualizing and understanding the circumstances on the ground, and for assessing impacts and alternatives.
- Optimizing Resources — Optimization algorithms often benefit from or require spatial data, such as route lengths and drive times, and location-specific attributes. Optimization algorithms are only as good as the accuracy and precision/granularity of the underlying model and data. GIS systems can be used to make location data much more precise and up-to-date, such as providing the exact location and available paths from the driveway entrance to a delivery point on a large site.
- Continuous Situational Awareness — GIS platforms are a great tool for always-on monitoring, seeing the whole picture during fast-moving situations. They are very commonly used as the primary consoles in operations control centers and emergency response war rooms.
- Bi-directional Communications — These platforms can become communication hubs, allowing operators and response coordinators to quickly establish communications with those in the field and nearly effortlessly share critical information back and forth (such as automatically geocoding any information sent by the field personnel and continuously monitoring their location).
- Historical Replay — When a GIS system continuously collects real-time data, it can also be used to replay events, so that scenarios and responses can be analyzed, lessons can be learned, and improvements made.
- Data Accuracy and Currency — Mobile data collection capabilities can be used to update, verify, and ensure that information about facilities, assets, and events around the world is accurate and up-to-date. Often data collection tasks can piggyback on other activities being conducted by someone at a remote site, such as when they are already there anyway to make a delivery or conduct an inspection or audit. When the process and app are designed properly, data collection and verification tasks can be highly automated and take minimal extra time and effort.
In Part Two of this series, we look at the characteristics required of commercial-grade maps, decentralized approaches to data input and quality control, and what it takes to build out and maintain a live, multi-tier, global, supply chain map.
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1 RTLS = Real-Time Locating System to track location of items, vehicles, or people within a building, yard, or local space. — Return to article text above
2 GIS = Geographic Information System. For more details, see the next page “What is GIS?” — Return to article text above
3 Lat-long = latitude and longitude. Lat/long/alt adds altitude to provide global X, Y, Z coordinates. — Return to article text above
4 BOM = Bill-of-Materials, a hierarchal list of all subassemblies, components, and materials for a manufactured item. — Return to article text above
5 ETA = Estimated Time of Arrival. The currently predicted ETA deviates from the planned ETA based on when the shipment is running ahead of or behind schedule, including expected delays between its current location and the destination. — Return to article text above
6 BOL = Bill of Lading, a document issued by a transportation carrier, listing and acknowledging receipt of the items to be shipped. It may also serve as evidence of the contract of carriage and a document of title for the goods shipped. — Return to article text above
7 In Tufte’s book, he describes how the explosion of the Challenger space shuttle and the deaths of all seven astronauts aboard could have been avoided if the data were presented differently to more clearly illustrate the risks of launching at the unusually low ambient temperature that day. — Return to article text above
8 Aquaculture refers to farming of seafood as opposed to ‘capture fisheries’ where fish are captured in the wild. — Return to article text above
To view other articles from this issue of the brief, click here.