Transparency, tracking, interconnections – supply chains are increasingly transforming into dynamic, data-driven systems. Thanks to the ongoing digitalization of the logistics industry, companies now have access to a wealth of data that is available in real time. In order to make this information usable for operational processes, logistics experts rely on highly developed analytical technologies that enable them to act with foresight and make data-based decisions. In our latest article on the blog, we discuss how Advanced Analytics as an integral planning tool in supply chain management contributes to reformulating and solving supply chain problems and why it is worth combining it with Advanced Reporting.
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Advanced Analytics in Supply Chain Management: Opportunities and benefits
Within Supply Chain Management (SCM), Advanced Analytics is an indispensable tool for logistics companies to take their supply chain processes to a new level. The technology uses innovative statistical and mathematical models, machine learning and data analysis methods to gain valuable insights from large volumes of raw data. Advanced Analytics covers the following aspects:
- Predictive Analysis: This technology allows companies to create future developments and demand forecasts with unprecedented accuracy. With the help of historical data and advanced algorithms, stock levels can be optimized, supply bottlenecks avoided and excess stock reduced. Various data processing techniques are used for this purpose, such as data cleansing, integration and scaling as well as feature engineering to prepare data for processing in algorithms. Important forecasting methods include time series analyses, regression and classification. Through these precise predictions, predictive analysis helps those responsible to identify trends and patterns, use resources more efficiently and make customer supply more reliable.
- Prescriptive Analytics: In contrast to Predictive Analytics, which is used to forecast trends, prescriptive analytics goes one step further and creates specific recommendations for action for companies based on the analyses. End-to-end planning scenarios and complex simulation models for exact and heuristic solutions are used to predict the possible effects of various decisions. The aim is to determine the best possible steps to avoid bottlenecks or waste and to enable greater profitability.
- Real-time Data Analysis: Whether identifying delivery delays or coping with fluctuations in demand – by having access to relevant information in real time, companies can react more quickly to potential disruptions. Analyzing real-time data increases agility and reaction times in the supply chain.
Recognizing and avoiding Top 5 supply chain risks with the help of Advanced Analytics
With the help of Advanced Analytics, companies are able to switch from reactive problem solving to proactive problem detection and prevention mode: delays, bottlenecks and other disruptions can be identified and resolved before they have a real impact on the supply chain. The following five examples of challenges in SCM demonstrate the decisive contribution that Advanced Analytics can make to overcoming them:
- Bottlenecks in resource allocation: It is not uncommon for companies to have to deal with limited resources such as storage space, transport or production capacities. By reformulating these bottlenecks as mathematical optimization problems, Advanced Analytics can precisely model the resources and optimize their use. Real-time monitoring, forecasting models and capacity management also provide support in this context.
- Inefficient inventory management: Advanced Analytics uses data analysis, predictive analytics and algorithms to ensure product availability and proactively manage inventory management. The switch to just-in-time warehousing also reduces stocks to a minimum, while the use of ABC analysis helps to categorize and prioritize products. Companies can thus develop optimal strategies and order quantities to avoid excess stock.
- Route planning: Challenges in route planning include variable transportation costs, different delivery times or numerous stopovers. By integrating data for these areas, the problems can be modeled mathematically. Optimization algorithms such as the Traveling Salesman Problem (TSP) or Vehicle Route Problem (VRP) take into account factors such as minimizing the total mileage or meeting delivery deadlines, and powerful analysis techniques adapt the routes in real time. The resulting efficient planning is crucial to reducing transportation costs and ensuring on-time deliveries.
- Fluctuations in demand: A major challenge for smooth, resilient supply chains is accurate demand forecasting in order to avoid under or overstocking. The integration of data from various sources such as points of sale, supply chain partners or social media is essential for this process. Advanced Analytics uses predictive models and machine learning for better production and inventory planning in order to create the most accurate forecasts possible based on the collected data and proactively counteract fluctuations.
- Risk management: Natural disasters, geopolitical tensions, problems in the supply chain or market fluctuations – assessing risks along the supply chain is a critical aspect. Data analysis and machine learning are used to identify anomalies within the data. Possible effects are simulated and evaluated by applying scenario modeling techniques. This helps in the development of robust contingency plans and strategies to manage risk and strengthen resilience.
Advanced Analytics and Advanced Reporting – a powerful alliance
In supply chain management, Advanced Analytics creates valuable synergies, especially in combination with Advanced Reporting. The two approaches complement each other. Together, they can further improve the efficiency and agility of supply chain processes. Advanced reporting focuses primarily on the presentation and visualization of data and information and creates meaningful reports and dashboards. In contrast to Advanced Analytics, the focus is less on solving complex problems and more on presenting the available and analyzed data and KPIs.
In practice, both approaches are therefore often combined in a two-stage process to provide even more comprehensive insights and solutions relevant for the supply chain: While Advanced Analytics enables the evaluation and modeling of complex data, Advanced Reporting presents the results of these analyses in a clear and easy-to-understand form. Managers can communicate the information gained to the entire organization and ensure that the relevant data can be understood and used at all levels.
Conclusion: Advanced Analytics in SCM – key element in a data-driven business environment
Advanced Analytics offers a transformative opportunity to reformulate challenges in the supply chain. Managers can redefine complex problems, develop innovative solutions and make data-based decisions. However, in order to not only analyze the collected data, but also to communicate it and generate added business value from it, it is advisable to combine the technology with Advanced Reporting. Thus, companies create a seamless transition from data to information, from which logistics experts can derive concrete measures for an improved business performance.