In today's fast-paced business environment, the efficiency and effectiveness of a company's supply chain can be the difference between success and failure. Supply chain analytics (SCA) is a transformative approach that uses data analysis, simulations, and predictive modeling to optimize supply chain performance. By leveraging the power of SCA, businesses can achieve higher levels of transparency, efficiency, and resilience. But what concrete potential use cases are there? Let’s have a closer look.
Supply chain analytics involves the use of data-driven tools and methodologies to gain insights into every aspect of the supply chain. From raw material procurement to product delivery, SCA enables organizations to make informed decisions, anticipate disruptions, and improve overall performance. The core functions of SCA can be categorized into three main areas: analytics, simulations, and predictions.
Analytics: Creating transparency and identifying opportunities
The first pillar of SCA is analytics, which focuses on understanding the current state of the supply chain through data examination. Analytics provides a comprehensive view of supply chain operations by collecting and analyzing data from various sources. This transparency helps identify inefficiencies, bottlenecks, and areas for improvement.
Example: Consider a manufacturing company experiencing frequent stockouts and overstock situations. By analyzing historical sales data, inventory levels, and supply chain processes, the company can pinpoint the root causes of these issues. Suppose the analysis reveals that the forecasted quantity deviates significantly from the actual quantity sold. The potential reason for this could be poor communication and no exchange of information between the sales and logistics teams. Based on this insight, the company can implement measures to improve coordination and reduce stockouts and overstocks.
Simulations: Estimating the impact of potential changes
Simulations, the second pillar of SCA, involve creating digital models of the supply chain to test the effects of various scenarios and changes. By simulating different strategies and interventions, companies can predict outcomes and make data-driven decisions that optimize performance.
Example: A producer wants to determine the best approach to increase its EBIT (Earnings Before Interest and Taxes). The company considers two options: investing in new machinery to reduce processing times or purchasing tools to streamline maintenance operations. By simulating both scenarios, the producer finds that investing in maintenance tools would lead to a 3% increase in machine utilization and a higher EBIT compared to the machinery investment. This simulation allows the company to choose the most cost-effective and beneficial option.
Predictions: Anticipating future challenges
The third pillar, predictive analysis, uses advanced algorithms and machine learning to forecast future supply chain trends and potential disruptions. Predictive analytics helps companies prepare for changes in demand, market conditions, and external factors, ensuring flexibility and resilience.
Example: A construction materials supplier anticipates a new law requiring homeowners to enhance their insulation. Using predictive analytics, the supplier forecasts a 10% increase in demand for insulation materials over the next two years. This prediction allows the supplier to adjust its production schedules, inventory levels, and supplier contracts to meet the forecasted demand, ensuring they can capitalize on the market opportunity.
Unlock the Full Potential of Your Supply Chain with Advanced Analytics
Supply chain analytics is a powerful method that enables companies to enhance transparency, make informed decisions, and anticipate future challenges. By leveraging analytics, simulations, and predictive models, businesses can optimize their supply chains, improve efficiency, and increase resilience.
Horváth has extensive experience in implementing supply chain analytics across various industries. Our team is ready to share insights and engage in discussions to help your organization leveraging the full potential of SCA. Do you have any questions or comments? Let us know about it.
Kröber, J. / Malbrant, S. / Monath, B. / Zeigert, J.
