Using Mapping for Supply Chain Planning
Updated: Jul 1, 2021
Mapping has progressed from being just a visual medium to depict geo-spatial information to a full-fledged 'visual + analytics' solution. The ability to query problems at a geo-spatial level and get meaningful insights from it can be largely attributed to the advances in the processing power of computing. With 5G expected to trigger an industrial revolution of sorts - IoT (Internet of Things) being the driver, it would not be far fetched to assume that the capability of mapping could reach Minority Report proportions.
Even in the present day, Map based solutions are quite powerful. Mapping a supply chain is an ideal example for 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 exercise below, I aim to highlight some capabilities of map based solutions along three nodal connections in a supply chain.
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, prioritize which farmlands we can procure from, among other uses.
Tool used - Drive Time
While Distance Buffers computes distance on the basis of latitude-longitude differential of two locations (straight line), we know that in reality, the distance between two locations depends on road connectivity and terrain. Drive Time tool helps bridge that gap. For the three plants above, I have plotted a 45 minute drive time for a truck i.e. how much distance can a truck be expected to cover in 45 minutes. As you would observe, the coverage is not the same across the 3 plants due to the reasons mentioned above. 45 minutes would only cover a short distance from one plant whereas it would cover a much longer distance from another plant. There is also a considerable difference when compared to the Ring Buffer output. For example, 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 (assuming 1 km can be traveled in 3 minutes by a truck).
Node 2: In this example, the corn flakes co. is affected by a business disruption and uses map based technique to resolve 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 it would be difficult to show the entire processing steps here, I have explained one output below-
Tools Used - Summarize & Combine
The new feature added in this 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/H candidates. Each feature, i.e. 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 - in this case for the 'Sum Output' attribute, I have used a distance constraint to calculate how much of finished product is within convenient reach of the new W/H location (a function of the plants' location within the ring). A decision maker can compare as many attributes as he can procure or compute to answer questions such as How much capacity is required at the new W/H? What are the Cost v/s Capacity considerations? What safety thresholds to keep? Should the new W/H be made permanent considering the risk outlook as well as future growth plans?
Moreover, a visual output also stimulates additional mental comparisons for making better choices. This is a major benefit of using visual analytics over a pure spreadsheet based output.
Node 3: In this example the Corn flakes co. needs to optimize the outflow from its 2 W/Hs to 7 Wholesalers basis their orders. It has just 2 trucks available - one at each W/H. One mandatory criteria (constraint) is that the trucks need to return to the W/H after making all the deliveries. Each stoppage takes 20 minutes.
Tool Used - Routing
Routing is a mathematical technique to solve network optimization problems - you may have come across this method during your student days - this is the same technique with a visual output. Having fed the constraints into the map based solver tool, the output is generated as above. The red rhombuses are the wholesaler location whereas the green and purple lines denote the individual truck routes from the W/H to the Wholesaler and back. The solver minimizes time (which cost is often a function of) to identify the optimal route by which each of the wholesaler orders can be fulfilled.
Alongside the map based output, the attributes are also updated in the map layer which chart out the 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 are some of the constraints which can also be factored in. 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, with GPS and related technologies / sensors, it is also possible to monitor and adjust the plan basis the changes in traffic conditions, customer requirements and business disruptions on a near real time basis.
To conclude, a powerful map based solution can help visualize, manage and optimize the entire chain of business operations. The above examples use just some of the tools - there is a vast repository of geo-processing tools available which can help you process 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 represents a scientific and technology driven way of accomplishing this objective.
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Regards,
Arpit