3 ways to optimise your supply chain with “Big Data”

In the last decade, the advancement of big data, IoT and now AI, has led to more optimised systems across supply chains. So much so that it is hard to imagine what logistics would look like without these tech advances. According to The Times of India, 41% of supply chain professionals give data analysis the highest priority in supply chain technologies. With AI and the potential of rapid analysis, data specific growth and the significant uses of that data, the opportunities for supply chains are endless.

Big data and supply chain optimisation

Big data’s role in the supply chain has steadily grown, with evermore opportunities for integration and efficiency being discovered. At the core of this continuous growth within the field is the objective of bringing optimisation to processes and procedures.

Big data can make processes simpler, faster, and attuned to the needs of consumers. With the rapid development of AI, advanced analytic systems are predicted to play a vital role for logistics.

According to Gartner, they expect 50% of global eCommerce companies will invest in AI, a real-time supply chain, and advanced analytical solutions, while Appiven predicts that by 2026, 75% of supply chain vendors will provide AI and data science.

Furthermore, big data can give insights on consumer wants and needs, as well as utilising it to stay on top of consumer trends.

Supply chain data origins

Today, big data is considered essential to supply chains, guiding, analysing, and forecasting, but it was not always like this.

The term “Big Data” refers to “extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time” and was coined in 1990.

It reflects an understanding that data could be gathered swiftly and analysed to make facets of work easier. Like a domino effect, this technology has advanced at a rapid pace due to the growth of the internet, increased usage of mobile devices and the development of the Internet of Things. What started as simple tracking has now moved to customer behaviour predictions, real time tracking, robotics merged with delivery and AI processing tools for maximum efficiency.

The past 5 years have highlighted a need for resilience, agility, and flexibility in supply chains. While there are definite things that cannot be designed or planned for, there is much that big data can do to help companies streamline and fortify their operations.

1. Forecasting

   The ability to foretell what lies on the horizon has long been something companies strive to have. With the leaps and bounds that have occurred within big data, such as predictive modelling, AI, and trend forecasting, companies are now better versed in predicting customer spending behaviour, customer preferences and market changes.

   Demand forecasting: big data is used to create predictable patterns by analysing historical data and market trends to predict customer behaviour, highs, and lows of seasons, managing inventory and even how to provide a competitive edge using customised customer experiences. According to a report done by McKinsey, “Companies using customer analytics report 115% higher ROI and 93% higher profits than the ones who do not.” Using big data for forecasting moves beyond just retailers, it has provided the opportunity for manufacturers and shipping companies to predict production demand and delivery analytics using this knowledge for planning purposes, such as in the case of cold chain logistics.

   Inventory Optimisation: big data analytics can help forecast demand fluctuations, lead times, and seasonality patterns. Companies can use historical sales data, current inventory levels, and future demand forecasts, to analyse and determine the optimal inventory levels for each product’s SKU (stock keeping unit) or location and reduce excess inventory and lower costs. Amazon is one of the biggest companies that have embraced big data to streamline their decision making, specifically harnessing it to personalise their customer experience, manage their inventory and even for their targeted marketing campaigns.

   Production Planning: production schedules can be streamlined by examining factors like machine utilisation, labour availability, and material availability. By integrating data from production systems, ERP (Enterprise Resource Planning) systems, and other sources, companies can optimise their production processes, reduce idle time, and improve overall efficiency.

   To further strengthen their supply chain movements, companies should consider partnering with logistics companies that can offer supply chain resilience models as well as alternative solutions. Partnering with a logistics company that can offer multimodality, such as air, ocean, and rail, can offer the agility and reflexivity needed to accommodate unexpected disruptions and changes. This can help soften some of the impacts, geopolitical events, extreme weather, and other disruptive elements can have on supply chains.

2. Tracking

   Big data tracking can significantly benefit supply chains by providing real-time visibility, predictive analytics, optimization capabilities, and improved decision-making.

   Real-time visibility: big data tracking allows supply chain managers to monitor the movement of goods, inventory levels, and production processes in real-time. This supply chain visibility plays a vital role in identifying blockages and inefficiencies in the chains, allowing companies to input corrective action swiftly.

   Optimisation: Big data tracking allows for overview, evaluation, and improvement of various supply chain processes, such as route planning, inventory management, and production scheduling. By analysing multiple parts of the supply chains, organisations can identify opportunities to streamline operations, reduce costs, and improve overall efficiency.

   Customer satisfaction: tracking also allows companies to track changes in their customers’ behaviour, such as shifts in buying habits, interests, and satisfaction levels. Through analysis of this data, companies can better tailor their output, including services and products, and strengthen customer satisfaction and loyalty.

   Additionally, companies should use big data tracking to monitor the performance of their suppliers, assessing things like delivery times, product and material quality and overall compliance with regulations and so forth. In these matters, it may make more sense for companies to align with logistics partners who have a varied portfolio, offering more speed, reactivity, and flexibility when it comes to supply chain moves.

3. Risk Management

   Never has the ability to identify potential risks and implement migrating action against them been more important. With climate change and geopolitical events causing delays and disruptions, companies are always looking for ways to manage risks and mitigate the impacts of them.

   By analysing data from various sources, including social media, news feeds, and sensor networks, big data tracking systems can identify potential risks to the supply chain, such as natural disasters, geopolitical instability, or supplier disruptions. This early warning allows organizations to implement contingency plans and minimize the impact of unforeseen events.

   Walmart has harnessed big data to predict their customer behaviour as well as predict how things like weather can affect their business and how to navigate it.

Supply chain data and the future

Big data has never been more valuable to companies when it comes to their supply chains. With “uncertainty” being the word that best describes the 2020’s so far, companies can utilise Big Data to enhance visibility and resilience to their supply chains. Utilising Big Data, companies can make data-drive decisions, utilising analytics to optimise their production, inventory, improve customer experience and proactively manage risks, and reduce overall costs.

Furthermore, companies who partner with integrated logistics providers who are utilising Big Data to drive innovation, from things like a supply chain resilience model, to enhancing refrigerated cargo option, can further enhance companies supply chains. Additionally, an integrated logistics partner can also help mitigate risks, optimise routes, and assist with forecasting changes to upstream and downstream moves.