Can data science mitigate the supply chain crisis impact?

The current global crisis in supply chains is forcing companies to manage their distribution methods by adopting a proactive approach based on Big Data and advanced analytics.

Wednesday, October 27, 2021

The supply chain crisis has resulted in restaurant chains and fast food outlets running out of key ingredients (e.g. NandosMcDonald'sKFC, and Greggs), shortages on supermarket shelves (e.g. ASDASainsbury's and Morrisons), disruptions to retailers' product lines (e.g. IKEA) and fuel shortages.

Companies should view this crisis as an opportunity to justify the priority of adopting digital transformation within their supply chain processes using Big Data, location intelligence, mobility analytics, machine learning models, and predictive models, in order to optimize their means of distribution and successfully gauge the impact a shortage will have on operations.

You may be interested in: "Global supply chain crisis?"


Using real-time location intelligence and mapping of manufacturing and logistics centers to model responses and obtain more accurate supply arrival times, businesses can save time by proactively anticipating and identifying problems deep in the supply chain, and leveraging recommendations based on machine learning models to find alternative sources of supply.

Here are some of the solutions these technology tools provide to improve supply chain processes:

Inventory visibility: Enables you to better model and predict demand and adjust your inventory from procurement to transportation to warehouse to direct supplies to locations ahead of demand.

Supply forecasting: Helps operators predict the arrival time of supplies, enabling them to take pre-emptive action to mitigate the impact of shortages.

Adaptation: Enable companies to flexibly adapt to different scenarios and better prepare for factors that may affect their ability to ship and receive materials.

Demand modeling: Helps logistics managers have better visibility into different areas of the supply chain and improve maneuverability when problems occur.

Also read: "Spatial data to optimize supply chains"

Location analytics can be used to stimulate demand and can be enriched with data streams related to events (such as port closures and labor shortages) or weather, providing information to predict and test future demand scenarios.

Route optimization: Enable finding the best routes and determining the sequence in which each vehicle should deliver assigned orders, using custom-designed algorithms to find the shortest and most efficient path that allows drivers to visit a number of locations only once.

"Image representing the vehicular flow of a highway in Mexico."


Last-mile logistics: provide information on existing constraints, inefficient allocations, and much more, solving the transportation problem, showing pickup and delivery locations, optimal routes calculated for each order, etc.

"Image representing Walmart's distribution centers and relationship with its customers."


Pickup and delivery site selection: The use of location intelligence allows supply chain companies to predict demand using new data streams (such as human mobility or e-commerce propensity), as well as understand catchment areas.


"Image depicting the relationship of Walmart's distribution centers with their respective customers."


Fleet Management: grants the ability to visualize existing fleets and optimize their activity over their lifetime is becoming increasingly important, with data streams such as traffic, time, and route data enabling improved efficiency and reduced costs.

Today, the industry knows the way forward, with more and more companies embarking on the search for a production analytics solution that fits their needs - after all, the only constant now is change.

At PREDIK Data-Driven we help our clients with delivery route planning, optimizing their movement, improving their estimated time of arrivals, both to reach distributor outlets, reach the consumer on time, improve omnichannel strategies, and better serve their entire customer base.

Do you need to implement location intelligence to optimize your distribution routes and last-mile processes? Contact us!









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More on this topic

Footfall analytics for wholesalers: Sysco Vs. US foods

November 2021

Location intelligence and footfall analytics can be valuable tools for wholesale distributors to maximize their revenue, optimize their processes and choose the best distribution routes for their pickup and last-mile delivery processes.

The correlation between foot traffic patterns, visitation, sales, and the success of wholesale food distribution companies has been studied and proven, so the development of this type of analysis has become a priority in the process of site selection, supply chain process optimization, and expansion modeling.

How do wholesalers use location analytics to understand the performance of their distributors?

November 2021

Foot traffic and location analytics help wholesale distributors maximize profits, allowing them to reveal where operational inefficiencies are and then implement solutions in problem areas.

With advanced location intelligence analytics, point-of-interest characterization and supply chain-centric foot traffic analytics, distributors can now truly understand their channels on a line-by-line basis, which can lead to better decisions and ultimately higher profits.

Global Supply Chain Crisis? 

October 2021

COVID-19 and climate change have directly impacted the supply chains of the sectors and industries that generate the most economic output.  

Unfortunately, fiction has become reality, and a global pandemic coupled with sudden climate changes have increased these problems worldwide, also due to unforeseen events in logistics routes and the exponential increase in online shopping, forcing industries to increase the load of transportation, vehicles, staff and resources in general. 

Supply Chains Predictive Analytic benefits

September 2021

Unlike historical analytics, predictive supply chain analytics allows you to anticipate and prepare for the future, taking out the conjectures planning processes and improving decision making.

Predictive supply chain analytics use advanced technological tools such as machine learning, geomarketing, data mining that enables organizations to identify hidden patterns, understand market trends, identify demand, establish pricing strategies, achieve a high return on investment, optimize and reduce inventory costs.

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