Supply chain execution segment focuses on the following areas of supply chain:
This blog is the second one in a two-part series. In the first blog we had explored the role of data analytics technology in driving supply chain visibility in the ‘supply chain planning’ segment. In this blog, we’ll focus on how data analytics technology drives supply chain visibility across the ‘supply chain execution’ segment.
Supply chain execution segment focuses on the following areas of supply chain:
In the subsequent part of this blog, we will describe the role that data analytics can play in driving visibility in each of these areas.
Order management/fulfillment involves receiving, processing, and fulfilling customer orders. This includes order entry, order confirmation, picking and packing, shipping, and delivery. The goal is to provide customers with accurate and timely order fulfillment while maintaining customer satisfaction.
Order management in pharmaceutical and life sciences companies suffers from siloed order systems across regions and channels, lack of real-time inventory visibility, manual tracking of order fulfilment, and limited integration between ERP, customer relationship management, and logistics platforms. These issues lead to delays, errors, and inefficiencies in meeting customer demand.
Data analytics can bridge these gaps by:
Procurement is the process of acquiring goods and services from external suppliers. This includes sourcing, negotiating contracts, issuing purchase orders, and managing supplier relationships. The goal is to obtain the best value for money while meeting quality, delivery, and regulatory requirements.
Procurement in pharmaceutical and life sciences companies is hindered by fragmented supplier data, lack of real-time spend visibility, limited risk assessment capabilities, and manual vendor performance tracking. These can lead to suboptimal sourcing decisions, supplier disruptions, and inflated costs, especially in a globally distributed and compliance-heavy environment.
Data analytics can remove this hindrance by:
Warehouse management involves the efficient storage and handling of goods within a warehouse or distribution center. This includes receiving, put-away, storage, picking, packing, and shipping. The goal is to optimize warehouse space, minimize handling costs, and ensure the accurate and timely fulfillment of orders.
Warehouse management in pharmaceutical and life sciences companies faces limited real-time visibility into inventory levels and locations, inadequate environmental monitoring (e.g., temperature, humidity), poor integration with upstream and downstream systems, and manual, error-prone tracking of controlled substances and serialized items. These issues can result in compliance risks, product spoilage, and inefficient space utilization.
Data analytics can plug these gaps by:
Transportation management involves the planning, execution, and control of the movement of goods from one location to another. This includes selecting transportation modes (e.g., truck, air, sea), optimizing routes, negotiating rates, and managing transportation providers. The goal is to deliver goods safely, efficiently, and cost-effectively.
Transportation management in pharmaceutical and life sciences companies suffers from shortcomings such as lack of real-time shipment tracking, limited visibility into cold chain conditions, poor coordination across carriers and regions, and manual incident reporting. These shortcomings increase the risk of delays, product spoilage, and non-compliance with regulatory requirements.
Data analytics can address these shortcomings by:
Manufacturing is the process of transforming raw materials and components into finished pharmaceutical products. This involves a series of operations, including mixing, blending, granulation, tableting, encapsulation, filling, and packaging. Manufacturing must adhere to strict GMP regulations to ensure product quality, safety, and efficacy.
Manufacturing in pharmaceutical and life sciences companies faces disconnected production systems, limited real-time visibility into equipment performance and batch quality, poor integration of shop-floor data with enterprise systems, and reliance on manual, paper-based processes. These challenges lead to production delays, quality deviations, and inefficient resource utilization.
Data analytics can address these gaps by:
Quality management encompasses all activities related to ensuring that pharmaceutical products meet the required quality standards. This includes quality control, quality assurance, and continuous improvement. Quality management systems are implemented to ensure that all processes and activities are performed in accordance with Good Manufacturing Practice regulations.
Quality management in pharmaceutical and life sciences companies suffers from siloed quality data across functions, reliance on manual documentation, delayed detection of deviations, and limited ability to perform root cause analysis. These issues hinder quality decision making, increase compliance risks, and slow down batch release processes.
Data analytics can plug these issues by:
Returns management involves the process of handling returned products. This includes receiving, inspecting, and processing returned goods. The goal is to minimize losses, comply with regulations, and protect the company’s reputation.
Returns management in pharmaceutical and life sciences companies faces challenges such as lack of real-time tracking of returned products, poor visibility into return reasons and conditions (e.g., expired, damaged, temperature-excursion), manual and inconsistent returns processing, and limited integration with inventory and quality systems. These gaps lead to revenue leakage, compliance risks, and inefficient product disposition.
Data analytics can address these challenges by:
DiLytics has built a supply chain execution solution, branded as DiLytics Supply Chain Execution Insight Solution, that provides the above-mentioned functionalities/capabilities for the pharmaceutical and life sciences industry. DiLytics Supply Chain Execution Analytics Solution comes prebuilt with:
A high-level architecture of DiLytics Supply Chain Planning Insight Solution is provided below:
In the world of pharmaceutical and life sciences supply chains, execution excellence is paramount. From procurement and manufacturing to warehousing, transportation, and returns management, data analytics turns supply chain execution from a black box to a glass box – transparent, intelligent and responsive. By leveraging descriptive to prescriptive analytics, companies can ensure product quality, regulatory compliance, and on-time delivery while reducing operational risks and inefficiencies. Data analytics, therefore, has the power to transform supply chain execution from just a function to a true competitive differentiator for pharmaceutical and life sciences companies.
DiLytics helps organizations modernize analytics, improve decision-making, and turn complex data into a competitive advantage.