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Big Data for supply chain management

Knowing in-depth what happens in our supply chain is the basis for creating optimal and efficient strategies. Data analytics is one of the greatest technological advances that have the power to positively impact supply chain innovation.

Leading companies are already working on obtaining and analysing their data to drive operational efficiency and reduce costs; at the same time, setting the logistics’ trends that we will be experiencing its development for the next decade. 

Big Data essentially refers to a large amount of data, structured and non-structured, which support companies to establish behaviour and human interactions patterns. This makes it possible to take advantage of the information collected to improve decision-making, foresee problems, generate opportunities, thanks to knowledge. The assertive application of Big Data in the hands of Artificial Intelligence (AI), Machine Learning (ML) and the automation of systems and operations are the technological keys to not lag our competitors.

 

The Big Data analysis effect on different supply chain stages.

Big Data analysis requires a combination of tools, processing systems and algorithms that can interpret information from data.

When conducting coordinated supply chain analysis, application shifts from a focus on simple automation to forward-thinking data integration and better decision making. This creates new insights that help improve supply chain management, from improving front-line operations to strategic choices.

Using real-time data, which is a combination of structured and non-structured formats, and the power of 3Vs (volume, speed, and variety), supply chain analysis enables supplier network collaboration and integration. This from one extreme to the other in a systemic sense, affecting each of the chain stages.

Big data for planning

Integrated data from the entire supply chain combined with the use of statistical models can help forecast demand with greater precision. This influences inventory and replenishment planning. Having the opportunity to coordinate them upfront ensures that there are no out-of-stock situations, for example. At this stage, a suitable Big Data model analysis not only considers real-time and historical data but also considers macroeconomic factors, market trends and even competitive data. 

Big data for provisioning

Managing purchasing coordination is always a challenge where, in general, there is enormous potential for savings and optimization. Leverage supply chain analysis to assess supplier performance and compliance in real-time on-site on a quarterly or annual basis can help us spot opportunities and intervene on issues early.

Monitoring and gathering data from suppliers can allow us to create a transparent relationship, without hidden costs, focused on real information and in the moment.

Big data for performance

During implementation, Big Data helps reconfiguring the many flexible parts of the chain, optimizing available resources, (space, tools, materials, human resources, etc.) and maximizing production.

In the manufacturing industry, the IoT sensors application can provide the machines status data in real-time. This allows not only to improve assets performance and production capacity but also to perform predictive scenarios to estimate failures or scheduled maintenance.

 

Big data for delivery

At the delivery stage, everything is focused on: speed (getting the product out on time), precision (ensuring packages reach the correct destination) and efficiency (finding the optimal route / combining deliveries). Real-time delivery data overlaid with external data, such as traffic and weather patterns, can result in significant improvements in logistics management performance.

 

Big data for inverse logistics

Return efficiency is a key factor for companies that want to maintain their profitability. Optimizing restocking and transportation costs to return the product to the retailer / warehouse, the general shipping costs to send another product to the customer, and the decision costs on the evaluation of the returned product are the goal given the complexity of the reverse logistics. Analytics can help reduce these costs and provide the visibility necessary for trouble-free returns, by combining data from inventory and sales systems, and inbound and outbound flows.

Statistics: The impact of Big Data on the supply chain

  • 40.7% of modern companies believe that data analytics will be one of the key technologies for supply chain management in the next two years. (Gestión Logística)
  • 28% of supply chain leaders say analysing data from multiple SCM systems is a key benefit of advanced analytics. (Logility)
  • 81% of supply chain managers report that data analytics will be crucial when it comes to reducing costs. (El Grupo Hackett)
  • 75% of large manufacturers seek to update supply chain operations using IoT and situational awareness based on data analytics. (Inbound Logistics)

Big Data is already being applied in leading organizations that seek to transform and generate efficient, effective, optimized supply chains and with the possibility of adapting to change. However, within the logistics sector, there are still few who are reacting to a change that has no turnback.

These technologies are transforming the logistics world. Therefore, it is important to address the application learning and adaptation gap that this type of technology entails. Also, refocusing logistics towards systemic, coordinated, and transparent processes. It is critical to start this process if we want our company to be competitive and generate greater benefits in an increasingly changing and unstable environment. 

 

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