Advanced Supply Chain Analytics Solutions

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.

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.

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.

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.

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.

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.

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.