Supply Chain
Impact of big data analytics on supply chain performance
Supply chain management (SCM) involves management of all the activities that are a part of producing and delivering a product—from raw material procurement to last-mile delivery of finished products. It includes activities such as demand planning, finances, processes, inventory management and transportation, to name a few. SCM is an intricate system and needs to be managed carefully for industries to operate smoothly.
Supply chains that involve multiple countries are highly prone to disruptions due to any disturbance anywhere on the globe. Any adverse incident anywhere can result in an impact that spans numerous products and many nations. The blockage of the Suez Canal in early 2021 is an illustrative example. It held up USD 9.2 billion worth of goods each day causing serious worldwide supply chain shocks. This is where data analytics has stepped in to help better manage the system.
Supply chains generate huge amounts of data and analytics can be effectively used to understand and optimise all that data. Today, big data analytics has become an integral part of SCM. It enables organisations to obtain valuable and proactive insights into their operations so that they can improve productivity, boost delivery efficiency, predict demand, respond to changing customer demands, reduce operational costs, optimise inventory management, identify risks and enable accurate planning. Fraud can be detected and controlled too. Advanced analytics tools that can work with large amounts of data make all these functions possible.
The benefits of implementing big data analytics on SCM include:
- Enhanced forecasting and demand management
- Real-time visibility and tracking
- Improved supplier management
- Optimised route planning and logistics
- Risk mitigation
- Cost reduction and efficiency boost
- Customer satisfaction and loyalty
One of the primary advantages of big data analytics in the supply chain is its ability to improve demand forecasting. By analysing vast sets of historical and real-time data, organisations can gain deep insights into customer behaviour, market trends and even seasonal requirements. This enables more accurate predictions of demand and reduction of excess inventory costs and stockouts. Organisations can optimise inventory levels and allocate resources efficiently to meet customer demands.
Big data analytics provides real-time visibility into the supply chain, enabling organisations to minutely monitor the movement of goods. Through the use of IoT sensors and data analytics, organisations can track shipments, monitor inventory levels, and identify potential bottlenecks or delays. This visibility helps in proactive issue resolution and reduces the risk of disruptions. The availability of real-time data offers flexibility and resilience since supply chain managers can quickly simulate disturbances, if any, and make critical data-driven decisions to protect the smooth flow of the supply chain.
Data analytics enables organisations to evaluate supplier performance more comprehensively. By analysing supplier data, including quality metrics, lead times, and delivery accuracy, organisations can make informed decisions about supplier relationships. This can lead to better negotiation terms, reduced costs, and a more robust supplier network that enhances supply chain resilience. In case of any disruptions to their primary supply chain, organisations can locate alternative suppliers to avoid supply interruptions.
Big data analytics can optimise route planning and logistics operations. Algorithms can analyse traffic patterns, weather conditions, and historical data to recommend the most efficient routes for transportation. This reduces fuel costs, lowers emissions and ensures timely delivery, ultimately improving customer satisfaction.
Supply chain disruptions can have significant financial implications. Big data analytics helps identify potential risks and develop strategies to reduce them. Organisations can assess the impact of geopolitical events, natural disasters, or supplier issues and implement contingency plans to ensure continuity of operations. Potential quality issues and equipment failures can also be identified early from sensor data and corrective steps can be implemented in time.
By optimising various aspects of the supply chain, such as inventory management, transportation, and labour, big data analytics contributes to cost reduction. It eliminates wasteful processes, reduces overheads, and boosts energy efficiency and overall system efficiency. By possessing competitive intelligence, organisations can meet their social and environmental goals too.
Ultimately, the impact of big data analytics on supply chain performance translates into enhanced customer satisfaction. Timely deliveries, accurate order fulfilment, and responsive customer service all contribute to higher customer loyalty and retention. Meeting or exceeding customer expectations leads to increased brand value and market share.
High data quality is a necessity
For an organisation to enjoy all the benefits of big data analytics, the quality of data must be high, accurate, and available in the right format at the right time. Data accuracy and consistency must be checked by collaborating with all the involved stakeholders such as suppliers and distributors, manufacturers and retailers. Further, the organisation must have enough technical expertise to handle big data.
In fact, two primary challenges to implementing data analytics are the lack of technical expertise and the absence of efficient data-gathering methods. Organisations must apply a structured approach to effectively implement analytics and prevent supply chain shocks.
Positive impact of big data analytics
However, with the right data and the right technical expertise to manage the data, big data analytics is already creating quite an impact on the global supply chain by providing actionable insights, enhancing efficiency, and improving overall performance. Organisations that harness the power of data-driven decision-making in their supply chain operations are better positioned to adapt to changing market dynamics and achieve sustainable growth, especially required in today's highly competitive and constantly shifting business landscape. As technology continues to evolve, the role of big data analytics in optimising supply chain performance will only become more pivotal.
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