Using Mapping Technology for Supply Chain Planning
Updated: May 21
Mapping has progressed from being just a visual medium to depict geospatial information to a full-fledged 'visual + analytics + data management' solution. The ability to query large geospatial datasets and get meaningful insights in quick time can be greatly attributed to the advances in computing. With 5G expected to trigger an industrial revolution of sorts - IoT being the driver, it would not be far-fetched to assume that the capability of mapping technology could reach Minority Report proportions.
Even in the present day, Mapping Technology is quite powerful. Mapping & Optimizing a Supply Chain Network and its associated Flows can be a good example to demonstrate this; After all, managing a supply chain entails making 'location-based decisions' all the time which a map-based solution, by its essence, can accomplish effectively. Here's a video on how General Motors (GM) goes about doing so. Through the results of my training study captured below, I aim to highlight some of the capabilities of map-based solutions for Supply Chain Planning across 3 Nodal Connections.
Node 1: Consider any company which is into the manufacturing of a packaged food product - for eg., cornflakes. It can procure the Raw Material (Maize) from multiple farmers near its plant(s). Transportation cost is directly proportional to the Distance traveled. Let us see how we can use mapping to plan the RM Procurement better.
Tool used - Ring Buffers
As the name of the tool implies, I have plotted the farmlands where we procure RM (maize) from as well as the manufacturing plants on a map and created a Distance Buffer, denoted by rings of 15, 30 and 50 kms respectively. This piece of visual information can help us, for example, to prioritize which farmlands we can procure from, among other uses.
Tool used - Drive Time
While Distance Buffers computes distance on the basis of Euclidean Distance (straight-line approach), we know that in reality, the time taken to travel between two locations depends on road connectivity and terrain as well. The Drive-Time tool helps address this shortcoming. For the three plants, I have plotted above a 45-minute Drive-time for a Truck i.e. how much distance can a truck be expected to cover in 45 minutes from the plants. As you would observe, this coverage output is not the same across the 3 plants (unlike the Ring Buffer output). 45-minutes would only cover a short distance from one plant whereas it would cover a much longer distance from another plant due to road-connectivity differential. A farmland may be located within the 15 km Distance buffer technically (innermost ring) but it may take much longer than 45 minutes to reach there.
Node 2: In this example, the corn flakes co. is affected by a customer-facing disruption and uses a map-based solution to address it. The finished goods needs to be transported to the Warehouses (currently 2 of them). Due to floods in a nearby area, one W/H has gone offline and therefore, another location needs to be shortlisted quickly.
While I am not showing the processing steps, I will explain the output generated below-
The new feature added to the map is the Wagon Wheel - The large wagon wheel is the existing W/H location while the two small wagon wheels represent the location of the proposed W/Hs. Each feature, i.e. each W/H, has certain attributes (vector data) - this can be seen at the bottom of the image. Certain attributes are readily available and summarized, such as cost and capacity constraints whereas other attributes are computed using Combine tool - for eg. for the 'Sum Output' field, I have used a distance constraint to calculate how much of finished product is within convenient reach of the new W/H location (which is a function of the plants' location within the Ring buffer). A decision maker can compare several other attributes to answer questions such as How much capacity is required at the new W/H?, What are the Cost v/s Capacity considerations?, What should be the Safety Stock threshold?, Should the new W/H be made permanent considering the Risk outlook as well as Growth plans?, and so on. Refer my detailed work on this topic, here.
Moreover, a map-based output also stimulates visual analysis and look at the situation holistically. This is a major benefit of using visual analysis over a pure spreadsheet-based output.
Node 3: In this example, the Corn flakes co. needs to optimize the logistics flow from its 2 W/Hs to 7 Wholesalers. basis the latter's demand pattern. The co. has just 2 trucks available - one at each W/H. One mandatory criteria (i.e. constraint) is that the trucks need to return to the W/H after making all the deliveries. Each stoppage takes 20 minutes.
Routing is a mathematical technique to solve network optimization problems - you may have learnt this technique during your academics - we can use the same technique on a GIS / mapping technology platform to generate a visual output. Having fed the constraints onto the map-based Solver tool, the output is generated as above. The red rhombuses are the wholesaler locations whereas the green and purple lines denote the individual truck routes from the W/H to the Wholesalers and back. The Solver-tool minimizes time (of which cost is a function of) to identify the optimal route by which each of the wholesaler orders can be fulfilled by the resources (2 trucks) available.
Alongside the map-based visual output, the attributes are also updated in the map layer in the form of a Route Plan. For example, the Route plan of Truck No: 1 (Blue Route) is as below -
It is possible to add many more constraints in Routing - Type of Freight, Vehicle Dimensions, Carbon Footprint, Customer Demand Patterns being a few. One can add pickup plans in the delivery route as well so that the trucks do not run empty during the journey, thereby promoting efficiency. Also, by integrating GPS and related technologies / sensors, it is also possible to monitor and adjust the logistics plan basis the changes in traffic conditions, customer requirements and business disruptions on a near-real-time basis.
To conclude, map-based technology can help visualize, manage and optimize the supply chain. The examples discussed above use just some of the geoprocessing tools - there is a vast repository of geoprocessing tools available which can aid you in processing location information effectively and gather new, previously unknown, insights to make better supply chain decisions.
When P.M. Narendra Modi announced the massive Financial Package in his address to the nation on May 12th this year, he stressed on supply chain 8 times during his speech! This reflects the importance he places on this field for a successful India in the post-Covid world order. Mapping Technology, in my view, represents a scientific way of accomplishing this objective.
Intelloc Mapping Services | Mapmyops is engaged in providing mapping solutions to organizations which facilitate operations improvement, planning & monitoring workflows. These include but are not limited to Supply Chain Consulting, Drone Services, Location Analytics & Applications, Site Characterization, Satellite Imagery Analytics & Polluted Water Remediation. Projects can be conducted pan-India and overseas.
Several other mapping and operations workflows are documented on this Geo-blog. Reach out to us via email - firstname.lastname@example.org or book a paid consultation (video meeting) from the link placed at the footer of this site's landing page.