Gartner’s recently published Hype Cycle for Supply Chain Planning Technologies, 2024, offers a fascinating glimpse into the evolving landscape of supply chain management. Notably, some key technological innovations such as probabilistic supply chain planning and Demand-Driven MRP (DDMRP) have transitioned to the trough of disillusionment. However, related concepts like short-term demand sensing are now in the slope of enlightenment, generating tangible benefits for organizations.
A closer examination of the technologies currently in the slope of enlightenment and plateau of productivity reveals a common theme: the application of advanced analytics. This theme is evident across various technologies, including:
- Advanced Analytics: Advanced analytics use techniques like regression analysis, multivariate statistics, simulation, business rules, and optimization modeling to predict future events and prescribe actions to achieve objectives. They include predictive analytics, which forecast demand, predict supplier lead times, and monitor risks, and prescriptive analytics, which optimize inventory, plan routes, and utilize assets. By anticipating future conditions and planning accordingly, advanced analytics help organizations maximize opportunities, meet customer expectations, and avoid disruptions. Adopting these techniques at scale can significantly improve financial and supply chain metrics, such as reducing working capital and enhancing asset utilization and customer satisfaction.
- Supply Chain Control Tower: A supply chain control tower is an operational framework that integrates people, processes, data, and technology to enhance visibility and decision-making. It serves as a central hub for capturing and using both structured and unstructured data, providing insights and predictions for short-term decisions. Key features include exception alerts and interactive dashboards for managing capacity and inventory shortages. While these capabilities are now standard in modern supply chain management, advanced features like broader impact analysis and scenario simulation are still developing to meet user expectations.
- Supply Chain Segmentation: Supply chain segmentation involves designing, implementing, and managing strategies across the end-to-end supply chain to cater to distinct customer experiences. Each segment has specific operational outcomes, procedures, and targets for relevant metrics. Segmentation helps leaders balance cost, growth, sustainability, efficiency, and complexity, improving alignment of supply chain designs and performance to meet varying customer needs. Successful implementations should have a standardized playbook and ongoing governance. Segmentation supports product, market, and channel expansions, planning change roadmaps, and shifting focus toward customer value and profitability.
- Short-Term Demand Sensing: Short-term demand sensing provides real-time visibility and predictive insights into expected demand using advanced technologies. It incorporates channel data into planning, modeling, and analysis practices, supporting short-term and midterm demand management. This capability helps organizations differentiate and extend their demand management approach, providing a more accurate signal for supply planning.
- Multi-Echelon Inventory Optimization (MEIO): MEIO is a supply chain planning system that supports Stage 4 inventory planning. It uses optimization-based technologies with business intelligence and analytics to determine optimal buffer stocks and policies. MEIO improves response and tactical management across a multilevel supply chain by identifying appropriate inventory buffers and policies, considering all echelons. This approach supports segmented response strategies for different customers, categories, and channels.
- Data Lake: A data lake is a flexible data storage repository combined with processing capabilities. It stores data from diverse sources in their raw formats, enabling advanced analytics and complementing traditional data warehouses. Data lakes support broad data exploration, essential for data mining, statistics, and machine learning. They provide scalable data acquisition, preparation, and processing, serving as a foundation for various business analytics, including predictive analytics and self-service data access. However, data lakes require skilled users to avoid data overload, leading to the concept of data lakehouses.
- Optimized Planning: Optimized planning creates optimal demand and supply plans considering current resources and supply chain configuration. It focuses on tactical planning associated with sales and operations planning (S&OP). Most companies have some form of optimized planning in their technology landscape, covering demand, inventory, and supply planning. Expanding to include resources like workforce, finance, and ESG enables multiconstraint optimization. Optimized planning produces feasible plans for optimal resource use, supporting supply chain goals such as service, cost, and profitability. It supports S&OP maturity levels, improving internal measures like inventory turns and customer service, and external measures like profitability and market share.
Advanced analytics is not just a buzzword; it is the driving force behind the transformation of supply chain planning. By leveraging sophisticated algorithms and data models, organizations can gain deeper insights into their supply chain operations, enabling more informed decision-making and strategic planning.
Supply Chain Control Tower
The Supply Chain Control Tower is a prime example of how advanced analytics can be applied to enhance visibility and control. By providing real-time data and predictive insights, it allows organizations to proactively manage disruptions and optimize performance.
Supply Chain Segmentation
Supply chain segmentation, powered by advanced analytics, enables companies to tailor their strategies to different segments of their supply chain. This targeted approach ensures that resources are allocated efficiently, leading to improved service levels and cost savings.
Short-Term Demand Sensing
Short-term demand sensing is another technology benefiting from advanced analytics. By analyzing real-time data, organizations can accurately forecast demand and adjust their supply chain operations accordingly. This agility is crucial in today’s fast-paced market environment.
Multi-Echelon Inventory Optimization (MEIO)
MEIO leverages advanced analytics to optimize inventory levels across multiple echelons of the supply chain. This holistic approach minimizes costs while ensuring that inventory is available where and when it is needed.
A data lake serves as a centralized repository for all supply chain data, enabling advanced analytics to be applied across the entire dataset. This comprehensive view allows for more accurate and actionable insights.
Optimized planning, driven by advanced analytics, ensures that supply chain plans are both efficient and effective. By considering a wide range of variables and constraints, organizations can develop plans that maximize performance and minimize risk.
The application of advanced analytics is set to govern the future of supply chain planning. As technologies continue to evolve and mature, organizations that embrace advanced analytics will be better positioned to navigate the complexities of the modern supply chain landscape. By leveraging these powerful tools, they can achieve greater efficiency, resilience, and competitive advantage.
Great insights on supply chain planning! At ZoTok, we’ve seen how automation and data-driven insights can transform the order-to-cash cycle, reducing errors and improving efficiency across the board. We are trying to bring in data analytics to the Small Business owner so that he/she can take data driven decisions just like their larger counterparts.
数字供应链战略与运营 @ Lenovo | Ex-Motorola | Ex- Flipkart | MIT Sloan
2 个月Nicely articulated Aditya Gupta - The biggest challenge that most of the top global supply chain companies face is the missing mathematical correlation of forecast, inventory and shipment to regulate these applications practically especially at product/ region level. This leads to an uncertain answer to what level of inventory at a given demand for a particular product / market should be kept to get XX level of serviceability and accuracy.