Advanced Supply Chain Analytics Solutions

Our modern supply chain analytics solutions are tailor-made to make your supply chain organization agile, transparent, granular, and efficient.

Are you looking to optimize your end-to-end supply chain processes with analytics?

Bring data-driven intelligence to your end-to-end supply value chain—planning, sourcing, manufacturing, delivering— to eliminate inefficiencies, pre-empt bottlenecks, improve customer satisfaction, and drive innovation, growth, and market excellence..

See how we can help you bridge the gap between analytics insights and business logic to witness the impact of our solutions. 

Start your free 4-week pilot today.

Years of experience in the space of advanced analytics solutions

Clients from across the globe, including 55 Fortune 500 companies

Experienced data scientists, analytics experts, and consultants

Successful project completions with proven impact of solutions

How Does Quantzig Work?

Gain unparalleled insights with supply chain analytics and reinvent your supply chain to combat challenges like market volatility, dynamic demands, global regulations, supplier management, increasing spends.

Sales and Operations Planning

Align your supply planning with demand forecasts and create a data-driven, informed production plan. Improve inventory management, optimize budget forecasting, enhance product lifecycle management, and increase revenue share with S&OP analytics. Implement Quantzig’s supply chain data analytics solutions today.

Customer Lifetime Value
Customer Segmentation

Supply Chain Visibility 

 

Make use of Quantzig’s supply chain predictive analytics to enable end-to-end supply chain visibility with the help of artificial intelligence, big data, and IoT. Remove costly inefficiencies, obtain greater inventory control, improve customer experience, and make smarter business decisions.

Supply Chain Digitization 

 

Implement digital transformation across the supply value chain to create a unified, integrated sequence of processes that lead to reduced operational costs, accelerated lead times, better planning and production, and across-the-board visibility. Work with Quantzig’s SMEs to witness analytics applications in supply chain.

Action Analytics
Customer Satisfaction

Transportation and Logistics Management

 

Amalgamate supply chain transportation and logistics with automation and IoT to drive operational transparency, optimize routes and networks, rectify product/delivery errors, and enhance customer satisfaction. Create optimized transportation and logistics operations with Quantzig’s supply chain data analytics.

Network Optimization

 

Network optimization empowers companies to compare their current supply chain with multiple “What If” scenarios to see how these changes will impact the supply chain. It also enables companies to gain a singular view of the entire supply chain function as a whole, making it easier to take strategic decisions and optimize profitability and productivity. 

Using our advanced modeling techniques, we will create a realistic model of the supply chain network taking into account all the internal, external factors, and constraints. We will then analyze it and determine which of the several ‘What If’ scenarios drive optimum profitability, efficiently improves operations, and reduces maximum unnecessary spending.

Net Promotion
Customer Loyal

Inventory Optimization

 

With our supply chain data analytics solutions, effectively manage inventory levels to meet predetermined service levels while minimizing inventory holding cost to maximize manufacturing efficiency, increase profitability, and enhance customer satisfaction.

Procurement Analytics

 

Optimize your procurement spends, manage resources, improve compliance, and successfully manage supplier relationships with the help of Quantzig’s advanced supply chain data analytics solutions.

Net Promotion
Customer Loyal

Warehouse Optimization

 

Develop lean warehouses and an agile supply value chain with automation and data-driven planning. Optimize fleet management, eliminate bottlenecks, track shipments and orders in real time to create data-driven operational efficiency. Use supply chain predictive analytics solutions to reinvent warehousing.

FAQs – Supply Chain Analytics

What is supply chain analytics and Why is It Important?

Supply chains generate massive amounts of data. Supply chain analytics helps analyze and make sense of this data by uncovering patterns and generating insights.

Supply chain analytics represents the ability to bring data-driven intelligence to the end-to-end supply value chain—planning, sourcing, manufacturing, delivering—reducing inefficiencies and improving productivity across processes.

What is the role of supply chain analytics?

Supply chain analytics is used for supply chain optimization by solving complex problems, pre-empting bottlenecks, eliminating inefficiencies, improving customer satisfaction, and driving innovation, growth, and market excellence.

What is big supply chain analytics?

Big data refers to the vast amount of data any organization generates – both structured and unstructured.

Moving beyond traditional data stored in ERP and SCM systems and making use of big data for supply chain analytics enables it to generate insights and patterns that hold the key to complete supply chain transformation.

How can supply chain analytics enhance decision making in supply chains?

Unlike the traditional siloed and slow-moving supply chain, supply chain analytics enables all processes to seamlessly exchange information and create a flexible, optimally integrated network that works in tandem.

Supply chain analytics enhances decision making by:

  1. Optimize Demand Forecasting – By analyzing datasets like POS, order history, competitor strategies, and external impact creators, organizations can improve their demand forecast accuracy. This enables them to optimize inventory levels, reduce lead times, and improve supply chain flexibility.
  2. Optimize Production Planning – An agile supply chain makes it easy for the organization to implement changes basis ongoing sales. Bringing about changes in production planning basis the latest sales data saves on unnecessary transportation and inventory holding costs, and improve overall efficiency.
  3. Improving Reaction Time – Supply chains are complex, interwoven processes that require time and effort to implement any changes. By creating a flexible supply chain, it becomes easier for organizations to react to bottlenecks and unforeseen issues.

All these steps in turn lead to optimization of productivity, budgets, and profitability across the supply value chain.

Which industries can use supply chain analytics

Supply chain analytics can be used by any organization, from any industry, that wants to improve its operational efficiency, make informed decisions about procurement and spending, optimize inventory, supplier performance, and demand forecasting.

What is the future of supply chain analytics?

The future of supply chain analytics will be characterized by three key factors—customer-centricity, inter-operability, and agility.

  • AI and ML – Artificial intelligence- and machine learning-based optimizations will become more and more prevalent in modern supply chains, especially in demand forecasting, production planning, warehousing, transportation, and predictive maintenance.
  • IIoT – The more systems and processes interact with each other, the more data flows between them. Internet of things enables various this exchange of data and information between processes, devices, and systems. The use of IoT in supply chains will enable organizations to monitor their assets and plan repairs, check inventory levels in real time and prevent stock-outs, improve supply chain performance, and increase visibility and reliability of supply networks.
  • Blockchain – A global supply chain is complex, vast, and intervowen. Keeping track of thousands of records generated via every interaction and transaction is a tedious process. The use of blockchain technology will enable organizations to keep track of the source of each interaction and transaction, thus increasing transparency while ensuring security.
  • Supply Chain Digitization – Supply chain digitization is radically transforming supply chains with a huge impact on product manufacturing and delivery. More and more organizations will opt for supply chain digitization to enhance end-to-end supply chain visibility, increase flexibility, and reduce costly inefficiencies.
What types of analytics are applicable to supply chain management?

The different types of analytics applicable to SCM are:

Descriptive Analytics – What happened?

Descriptive analytics analyses the data coming in real-time and historical data for insights on how to approach the future. The main objective of descriptive analytics is to find out the reasons behind success or failure in the past.

Diagnostic Analytics – Why it happened?

Diagnostic analytics gives in-depth insights into a particular problem and finds out the why behind it.

Predictive Analytics – What is likely to happen?

Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions, and predict future trends, which makes it a valuable tool for forecasting.

Prescriptive Analytics – What should be done?

Prescriptive Analytics is a method of analytics that analyzes data to answer the question – what should be done? This type of analytics is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

Cognitive Analytics – Analytics with Human-Like Intelligence

Cognitive analytics is a blend of artificial intelligence (AI), machine learning (ML), deep learning (DL), and semantics. It enables analytics tools to think like humans. The aim of cognitive analytics is to continuously learn from data and human-machine interactions and become smarter. Cognitive analytics has the potential to create truly self-thinking, intelligent technologies that can reveal context and find answers hidden in large volumes of information.

Is data analytics important for supply chain?

Traditional supply chains, that do not make use of data analytics are siloed and slow-moving, with no data being exchanged between processes. This setup calls for a host of issues like poor logistics efficiency, limited transparency, high inventory holding costs, poor forecasting leading to stock-outs.

The use of data analytics enables organizations to rid themselves of all these issues and create an agile, transparent, robust supply chain organization of the future to combat uncertainties.

Quantzig offers a comprehensive portfolio of customizable supply chain analytics solutions to help business leaders improve supply chain planning, reduce excess spend, reduce wastage and excess inventory costs, improve the demand forecast, demand planning process, and the capacity planning process.

We use best-in-class processes, cutting-edge tools, and our deep domain supply chain analytics expertise to help businesses transform their supply chain into a world-class organization that’s tailored for success.

Request a free proposal to learn how you can leverage our advanced supply chain analytics solutions, such as demand and capacity planning, procurement cost optimization, working capital management, inventory optimization, spend analysis, supply chain visibility, to optimize your supply value chain.

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