How is analytics capable of making the supply chain more reliable

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Supply chains are the backbone of successful organisations. These complex webs of interconnected networks help in delivering superior customer experiences, reducing operational costs, and integrating stakeholders across the value chain. No wonder, every organisation desire to have vibrant, dynamic, and reliable supply chain networks capable of reducing operational costs and improving profitability. The application of analytics can prove crucial to achieving the desired levels of reliability and efficiency in supply chain networks. It brings a layer of intelligence on top of the existing information, enhancing understanding. By collecting, analysing, and simplifying supply chain data, businesses can significantly improve forecasting accuracy, route scheduling, and the decision-making capabilities of organisations. Further, the insights from the analysis can also prove helpful in identifying bottlenecks and potential disruptions so that proactive measures can be taken well in advance. How else the application of analytics on the existing information can make supply chains more reliable, here are the complete details: 1) Inventory Optimisation: By analysing historical data and current trends, businesses can identify inventory patterns and predict future demand. This enables them to optimize inventory levels and avoid overstocking or understocking. Additionally, data experts use analytics to identify slow-moving or excess inventory, enabling businesses to take action to reduce waste and free up valuable storage space. 2) Improved Supply Planning and Demand Forecasting: Analytics capabilities help businesses in optimizing their supply planning and improving their demand forecasting. By utilizing AI-enabled analytics, businesses can gain better visibility into stock levels, freshness, and overall supply plans, which enables them to make informed decisions and take necessary actions. n addition, businesses can assess product availability and suitability based on factors such as geography, seasonality, brand, technology, market, etc. ensuring visibility on service level and stock coverage to meet their customers’ needs. Furthermore, data driven demand visibility enables the smooth running of production schedules, optimal inventory management, and timely delivery of finished products to customers. 3) Proactive Risk Management: With analytics capabilities, businesses can empower their teams to identify potential risks in the supply chain caused by various factors. By analysing historical data and current market conditions, firms can develop predictive models to anticipate potential disruptions and take pre-emptive measures to mitigate their impact. Additionally, by analysing factors such as their financial stability, reputation, and performance history, businesses can assess the supplier risks. By identifying high-risk suppliers, business stakeholders can take steps to diversify their supplier base and reduce their overall exposure to risk. Furthermore, businesses are also deploying analytics to monitor and improve supply chain performance, such as reducing lead times, improving on-time delivery rates etc… By continuously monitoring and analyzing supply chain data, firms can make data-driven decisions to improve their risk management strategies and ensure the resilience and continuity of their supply chain operations. 4) Improving Working Capital: By analyzing data from different sources, businesses can identify clusters of late payments from customers, early payments to vendors, overstocking situations that create cash flow blockades. By following an analytics approach in different KPIs: a) improving DSO (Days Sales Outstanding), b) improving inventory levels, c) optimizing vendor payment, d) managing short-term cash flow deficits, etc., businesses can track, manage, and improve their working capital efficiency. 5) Improving Transparency and Agility: Firms can monitor their supply chain operations effectively, gaining complete visibility into the processes, workflows, and key performance indicators. This transparency helps firms identify bottlenecks and inefficiencies in the supply chain, which can be addressed promptly to minimize the impact on the overall operations. Furthermore, by following an analytics-driven approach, firms can build agile supply chain systems that can respond quickly to changes in the market, customer demands, or disruptions in the supply chain. By leveraging advanced analytics techniques such as predictive modelling and machine learning, firms can develop contingency plans to mitigate the impact of any disruption. Conclusion By developing a data driven approach in supply chain management can have a major impact on businesses. Companies can optimize inventory levels, improve supply planning, and forecast demand in a scientific and methodical way. This allows businesses to reduce operational costs, improve efficiency, and deliver better customer experiences. The article was originally published in ETCIO
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