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Understanding Supply Chain Analytics: A Simple Overview

By: Manpreet Kaur

In today’s fast-paced and uncertain business landscape, companies are facing unprecedented challenges, from record-high inflation to geopolitical unrest and supply chain disruptions. In such volatile times, the need for real-time data access and comprehensive supply chain visibility has never been more crucial. Yet, while the potential of big data analytics to revolutionize supply chain management is widely recognized, many companies struggle to harness its full power. Hindered by a lack of capabilities and structured processes, they often fail to capitalize on the transformative opportunities that big data presents. In the first part of this article series, we delve into the barriers preventing companies from fully embracing big data in their supply chains and explore how a systematic approach can unlock its immense potential. Join us as we unravel the complexities of data-driven methodologies and showcase how forward-thinking organizations are embedding them into their DNA to navigate today’s unpredictable business landscape with confidence and agility

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Supply chain analytics: An overview

In today’s complex business landscape, supply chain analytics emerges as a game-changer, leveraging data to optimize performance and drive strategic decisions. By harnessing the potential of analytics, organizations can enhance efficiency, reduce costs, and gain a competitive edge. But what exactly does supply chain analytics entail, and how does it revolutionize traditional supply chain management?

Supply chain analytics is more than just crunching numbers; it’s about unraveling patterns and trends hidden within vast datasets to improve operations. From descriptive analytics that provide a snapshot of current performance to predictive analytics that forecast future outcomes, and prescriptive analytics that recommend actionable strategies, this multifaceted approach enables organizations to proactively address challenges and capitalize on opportunities.

Enter big supply chain analytics, a paradigm shift that expands the scope of analysis beyond conventional internal data. By incorporating cognitive analytics and advanced statistical methods, organizations can gain deeper insights into their supply chains, from front-line operations to strategic decisions. This evolution in analytics is not merely about gathering more data—it’s about extracting meaningful insights to drive continuous improvement and innovation.

In the ever-evolving landscape of supply chain management, the need for effective supply chain analytics has never been greater. As distributed global supply chains become increasingly complex, organizations must leverage analytics to navigate disruptions and optimize supply shaping. From diagnosing bottlenecks to mitigating risks, analytics platforms equipped with augmented analytics capabilities empower organizations to stay ahead of the curve.

But the journey towards world-class supply chain analytics isn’t without its challenges. Organizations must overcome obstacles such as data silos, integration issues, and legacy systems to unlock the full potential of analytics. By embracing a systematic approach and investing in predictive analytic software, organizations can pave the way for transformative change.

In the next installment of this series, we’ll delve deeper into the evolution of supply chain analytics and explore real-world examples of organizations harnessing its power to drive innovation and achieve operational excellence. Join us as we unravel the complexities of supply chain analysis and uncover the secrets to building a resilient and agile supply chain in today’s dynamic business environment.

6 Areas for Supply Chain Analytics

Areas for supply chain analytics

Supply chain analytics involves analyzing various aspects of the supply chain process to optimize efficiency, reduce costs, and enhance overall performance. Here’s an explanation of each area:

Sales, Inventory, and Operations Planning (SIOP):

  • This involves the integration of sales forecasts, inventory levels, and operational capabilities to develop a comprehensive plan for meeting customer demand while optimizing inventory levels and operational resources.
  • It helps in aligning sales forecasts with production plans and inventory levels to ensure smooth operations and minimize stockouts or excess inventory.

Sourcing:

  • Sourcing involves the procurement of raw materials, components, or finished goods from suppliers.
  • Supply chain analytics in sourcing focuses on supplier selection, evaluation, and relationship management to ensure the availability of quality inputs at competitive prices and within specified timelines.
  • It may involve analyzing supplier performance, negotiating contracts, and identifying opportunities for cost savings or process improvements.

Manufacturing:

  • This area focuses on optimizing production processes to maximize efficiency, reduce costs, and improve product quality.
  • Supply chain analytics in manufacturing may involve monitoring equipment performance, analyzing production data to identify bottlenecks or inefficiencies, and optimizing production schedules to meet demand while minimizing lead times and costs.

Warehousing:

  • Warehousing involves the storage and management of inventory within a distribution network.
  • Supply chain analytics in warehousing includes optimizing warehouse layout and design, inventory management strategies, and order fulfillment processes to improve efficiency and reduce operating costs.
  • It may also involve analyzing data on inventory levels, order volumes, and picking/packing processes to streamline operations and enhance customer service.

Transportation:

  • Transportation involves the movement of goods between different points in the supply chain, such as from suppliers to manufacturers, between manufacturing facilities, or from warehouses to customers.
  • Supply chain analytics in transportation focuses on route optimization, carrier selection, freight cost analysis, and transportation mode selection to minimize transportation costs, reduce delivery times, and improve service levels.

Point of Sale (POS):

  • Point of Sale refers to the location where a retail transaction is completed, typically involving the exchange of goods for payment.
  • Supply chain analytics in POS involves analyzing sales data, customer purchasing patterns, and inventory levels at retail locations to forecast demand, optimize inventory replenishment, and improve sales forecasting accuracy.
  • It helps retailers ensure that the right products are available at the right locations and times to meet customer demand and maximize sales.

Overall, supply chain analytics across these areas enables organizations to make data-driven decisions, optimize processes, and enhance overall supply chain performance.


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Conclusion

In conclusion, supply chain analytics stands as a transformative force in today’s dynamic business landscape, offering organizations the tools to navigate challenges and seize opportunities with unprecedented precision and agility. By harnessing the power of big data and advanced analytics techniques, companies can unlock hidden insights, optimize operations, and drive strategic decision-making across every facet of the supply chain.

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Author’s Details

Manpreet Kaur

Assistant Manager Presales – Sourcing and Procurement Intelligence

Manpreet is a presales specialist at Infiniti Research and has expertise in sales, business strategy execution, and innovative solution design. She is actively involved in supporting clients from F&B, CPG, Healthcare, Pharma, Chemicals, BFSI, Oil & Gas and Automotive sectors.

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Frequently asked questions

Supply chain analytics involves the use of data analysis techniques to gain insights and optimize various aspects of the supply chain, including inventory management, demand forecasting, logistics optimization, and supplier performance evaluation.

Five common types of supply chain analytics include demand forecasting, inventory optimization, transportation analytics, supplier performance analysis, and risk management.

A supply chain analyst is responsible for analyzing data related to various aspects of the supply chain, such as inventory levels, transportation costs, supplier performance, and demand patterns. They use this analysis to identify opportunities for improvement and help make informed decisions to optimize the supply chain.

Real-life example of supply chain analytics: A retail company uses demand forecasting analytics to predict future demand for its products accurately. By analyzing historical sales data, market trends, and other relevant factors, the company can adjust inventory levels and production schedules to meet customer demand efficiently while minimizing excess inventory costs.

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