Supply Chain Tech
January 16, 2024

5 Ways Big Data Is Transforming Supply Chains

Discover how big data transforms supply chain management, enhancing decision-making, risk management, and more
Romain Fayolle

Big Data is altering how supply chain decision-makers make choices. Decision-making in today's time needs solid data-driven processes and methods. 

The power of big data — speed, diversity, and volume — is changing supply chain decision-making. These big data sets can provide businesses with unmatched visibility into market trends, customer behavior, and operations, enabling senior leadership to focus on more important strategic decisions.

This article covers five ways in which big data transforms supply chain management in modern-day companies such as Amazon as a tool for much better decision-making.

  1. Improved Demand Forecasting: Big data can help provide great insights into customers' and markets' behavior. For the supply chain, it enables in-depth and granular demand forecasting. Companies could better anticipate potential demand and its changes through information like sales figures, social media, market trends, and weather forecasts. Big data sets the base for the predictive models to decipher the trends through such inputs. And when the demand forecasting improves, it has ripple effects of improvement across the chain. It helps optimize inventory levels, minimizes overstocking/stockouts, and enhances consumer satisfaction.
  1. Optimum Facility Location: Setting up a new facility is a massive capex expense for any company. Leveraging big data helps reduce the chances of error for such critical decisions. It helps businesses determine where to put brand-new warehouses and distribution centers. They can identify the ideal places for the lowest delivery times and expenses, combining information on customer locations, demand nodes, shipping times, and costs.
Such a data-driven approach lowers the supply chain cost and improves customer care by reducing delivery times.
  1. Proactive Supply Chain Risk Management: Big data analytics helps anticipate and determine supply chain risks in advance and help control them in time. Recognizing trends and patterns could help businesses prepare for disruption and take proactive actions.
This risk management can be applied to any part of the supply chain, like diversifying supplier bases, changing inventory management methods or inventory distribution to prevent stockouts, or shifting routes because of expected risks like political instability or natural disasters. Big data can also help keep plan Bs ready and activate them immediately in case of an adverse supply chain event.
  1. Better Supplier Performance and Relationships: Big data analytics helps businesses better measure and evaluate supplier performance. Continuous improvement metrics may be monitored and analyzed with big data, including delivery times, quality standards, and adherence.
This provides improved insights into supplier performance, resulting in closer, much more cooperative relationships. Such step-by-step improvements unlock the power of a collaborative supply chain, leading to a more dependable and robust end-to-end supply chain.
  1. Sustainable & Ethical Supply Chain Management: Big data analytics helps monitor and manage ethics and sustainability in supply chains. It can enable effective product trailing from procurement to delivery. Companies can track products' sourcing, production, and transport to meet environmental and social requirements. This lessens the carbon footprint, enhances the company's reputation due to ethical sourcing, and meets customer needs for environmentally and socially responsible business practices.

Challenges to Be Big Data Ready in Supply Chains

Regardless of the immense advantages of big data for supply chain management, many challenges prevent efficient use and implementation. Combating these problems is crucial for businesses looking to leverage big data.

  1. Data Quality and Accuracy:

A significant challenge is keeping good data quality and accuracy. False positives and incorrect conclusions may come from incorrect or incomplete data.

  1. Data Privacy & Security Issues:

The greater information volume increases the risk of data breaches and privacy infringements.

  1. Building Analytical Skills and Capabilities:

The application of big data analytics calls for specialized skills. Data science and analytics are a potential talent gap for a lot of businesses.

  1. Cultural and Organizational Changes:

It can be tough to embrace a data-driven culture of organizations where decision-making is usually intuitional. It entails a cultural shift toward data, evidence-based decision-making, and organizational change to accommodate new collaborations and workflows.

Big data & analytics is an evolving field. Keeping in front of the curve with new trends and technologies can be challenging but crucial to remain competitive. Holocene can be of help here.

Role of Holocene in Big Data-Driven Supply Chain Management

Holocene offers innovative solutions that help businesses leverage big data across supply chains. From advanced analytics to data integration and real-time insights, Holocene allows businesses to make much better, data-informed supply chain choices. Holocene's deep expertise in this space enables companies to leverage big data to transform supply chains into dynamic, predictive, and resilient ones. Contact us today to learn more.