GenNext Supply Chain Management with Data Science - CirrusLabs

Cirruslabs-Blog-Image-GenNext Supply Chain Management with data science

We have come a long way from the past when data were recorded manually, often to the extent of making mistakes that had a negative impact on business. While human error is an unavoidable factor, no scope for error exists in the supply chain management domain. A simple mistake can lead to a huge blunder costing millions of dollars.

Supply chain management had been a slow process. But advances in data science ensured recovery of the time once lost in the traditional way of handling the supply chain. With powerful and free tools like Python and R, organizations can solve complex business problems in a jiffy. A business thrives on customer experience. Making a positive impression on the customer is possible only with a clear and precise supply chain management. Data analytics governs most of these solutions, a major part of which is Big Data. Remember that information gathered is information stored (for later use) in the entire timeline of supply chain management system.

What can data-driven supply chain management solutions do?

Some of the core capabilities of these solutions are demand and capacity planning, procurement cost optimization, working capital management, spend analytics, inventory optimization, purchase order flow management, warehouse optimization, OTIF Optimization, distribution and freight cost optimization, logistics management and more.

In this increasingly data-driven world, improving supply chain management is the only solution for a profitable business.

Data science and supply chain management

Data science combines tools, processing systems, and algorithms to interpret insight from data. With supply chain analytics, data-driven solutions reach a different level altogether, shifting focus from mere automation to forward-thinking data integration and better decision making. The main aim to integrate analytics simplifies end-to-end integration with collaboration from the suppliers’ network. Only using real-time data makes this possible, using a mix of both structured and unstructured formats.

The solutions are structured around the 3 Vs – Volume, Velocity, and Variety. Organizations achieve this by adept planning, sourcing, and development, execution, delivery and return. Data science plays a crucial role in all the stages in the following ways:

Planning: Helps in forecasting demands more accurately. Integrated data across the entire supply chain network along with the use of statistical models help in forecasting the demand more accurately. Big data analytics communicates with inventories and replenishment systems to prevent out-of-stock scenarios. These models also consider past, real-time and macroeconomic factors.

Sourcing and development: Evaluating contractor performance in real-time and identifying hidden costs. Procurement costs comprise 43% of the total costs. A lot of scope for saving exists at this stage. Firms are leveraging supply chain analytics to evaluate contractor performance and compliance in real-time rather than in quarterly or annual cycles when it may be too late to intervene.

Execution: Big Data helps in maximizing resources and production output. It helps in optimizing the available resources for maximum output. For instance, IoT sensors in the manufacturing industry can provide information about the equipment in real-time, which can be optimized on the fly through data science.

Data science has equal weight in the delivery and return processes, too. It helps improve performance significantly in terms of efficiency, accuracy, and speed. Gauging consumer behavior at the end of the cycle is another important factor that needs to be determined to reduce return costs. Data science helps in that too. Currently, product returns are estimated to be 30% for certain product categories, which is a major deterrent for companies serious about their profitability. Return costs include reverse logistics, restocking expenses, transportation costs in returning the product to the retailer or the warehouse, shipping overhead in sending the product to another consumer and decision costs on assessing the returned products. Analytics can reduce these costs by providing the visibility needed by combining data from inventory and sales systems and inbound and outbound flows.

For every industry, a data-driven supply chain has a productive, competitive and efficient impact on decisions. Industries quickly gain an interest in integrating data science into the supply chain management of their manufacturing sector. Carly Fiorina, former CEO of HP had once rightly pointed out, “The goal is to turn data into information, and information into insight”. But how it is used is completely up to the industries playing the numbers game.