Supply Chain Risk Management and Security
Expert-defined terms from the Professional Certificate in AI-Driven Pharmaceutical Supply Chain Management course at London School of International Business. Free to read, free to share, paired with a globally recognised certification pathway.
Artificial Intelligence (AI) #
A branch of computer science that focuses on creating intelligent machines that can learn from data and make decisions like humans. In the context of pharmaceutical supply chain management, AI can be used for predicting demand, identifying potential risks, and optimizing operations.
Blockchain #
A decentralized and distributed digital ledger that records transactions across a network of computers. It provides a secure and transparent way to track and trace the movement of goods in the supply chain.
Cybersecurity #
The practice of protecting computer systems, networks, and data from unauthorized access, use, disclosure, disruption, modification, or destruction. In the context of supply chain risk management, cybersecurity is essential to protect against threats such as hacking, data breaches, and cyber attacks.
Demand Forecasting #
The process of predicting future demand for a product based on historical data and other factors such as market trends, seasonality, and promotions. Accurate demand forecasting is essential for effective supply chain management, as it helps to ensure that the right products are available in the right quantities at the right time.
Internet of Things (IoT) #
A network of interconnected physical devices, vehicles, buildings, and other objects that are embedded with sensors, software, and other technologies to collect and exchange data. In the context of supply chain management, IoT can be used to monitor and control the movement of goods in real-time, improving efficiency and reducing costs.
Machine Learning (ML) #
A subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. In the context of supply chain risk management, ML can be used to identify patterns and anomalies in data, detect potential risks, and predict future outcomes.
Predictive Analytics #
The use of statistical models and machine learning algorithms to analyze data and make predictions about future events or outcomes. In the context of pharmaceutical supply chain management, predictive analytics can be used to forecast demand, identify potential risks, and optimize operations.
Risk Assessment #
The process of identifying, analyzing, and prioritizing potential risks in the supply chain. This involves evaluating the likelihood and impact of each risk, and developing strategies to mitigate or manage them.
Supply Chain Visibility #
The ability to monitor and track the movement of goods and information throughout the supply chain in real-time. This is essential for effective supply chain management, as it enables organizations to identify potential issues and take action to prevent or mitigate them.
Vendor Management #
The process of selecting, evaluating, and managing third-party vendors and suppliers to ensure that they meet the organization's requirements for quality, cost, and delivery. In the context of supply chain risk management, vendor management is essential to mitigate risks related to supplier performance, compliance, and security.
Artificial Neural Networks (ANNs) #
A type of machine learning algorithm inspired by the structure and function of the human brain. ANNs are composed of interconnected nodes or "neurons" that can learn and adapt to new data. In the context of supply chain risk management, ANNs can be used to identify patterns and anomalies in data, detect potential risks, and predict future outcomes.
Big Data #
Large and complex sets of data that cannot be processed or analyzed using traditional methods. Big data is characterized by its volume, velocity, and variety, and requires specialized tools and techniques to extract insights and value. In the context of supply chain risk management, big data can be used to analyze patterns and trends in the supply chain, identify potential risks, and optimize operations.
Business Continuity Planning (BCP) #
The process of developing a plan to ensure that an organization can continue to operate in the event of a disruption or disaster. BCP involves identifying critical functions and processes, assessing potential risks, and developing strategies to mitigate or manage them.
Cloud Computing #
The delivery of computing services over the internet, including servers, storage, databases, networking, software, and analytics. Cloud computing enables organizations to access and use computing resources on demand, without the need to invest in expensive hardware or infrastructure.
Data Analytics #
The process of examining data to extract insights and value. Data analytics can be descriptive, diagnostic, predictive, or prescriptive, and involves the use of statistical methods, machine learning algorithms, and other techniques to analyze data and identify patterns, trends, and anomalies.
Data Governance #
The process of managing the availability, usability, integrity, and security of data. Data governance involves establishing policies, procedures, and standards for data management, as well as monitoring and enforcing compliance.
Data Mining #
The process of discovering patterns and insights in large datasets using machine learning, statistical, and other techniques. Data mining can be used to identify trends, correlations, and anomalies in data, and to develop predictive models.
Data Privacy #
The protection of personal information and data from unauthorized access, use, or disclosure. Data privacy is essential to maintain trust and compliance with legal and regulatory requirements.
Data Security #
The protection of data from unauthorized access, use, disclosure, disruption, modification, or destruction. Data security is essential to maintain the confidentiality, integrity, and availability of data.
Data Warehouse #
A large, centralized repository of data that is used for reporting, analysis, and decision-making. Data warehouses are designed to handle large volumes of data from multiple sources and to provide fast access to data for analysis and reporting.
Decision Tree #
A type of machine learning algorithm that uses a tree-like model to represent decisions and their possible consequences. Decision trees can be used to classify or predict outcomes based on input data, and are often used in supply chain risk management to identify potential risks and develop mitigation strategies.
Deep Learning #
A subset of machine learning that involves training artificial neural networks with multiple layers to learn and represent complex patterns in data. Deep learning can be used for image and speech recognition, natural language processing, and other applications.
Digital Twin #
A virtual replica of a physical object or system, such as a manufacturing plant or a supply chain. Digital twins can be used to simulate and optimize the behavior of the physical system, and to monitor and diagnose issues in real-time.
Disaster Recovery #
The process of restoring an organization's operations and systems after a disruption or disaster. Disaster recovery involves developing and testing plans to ensure that critical functions and processes can be resumed quickly and effectively.
Edge Computing #
The processing of data and the execution of applications at the edge of the network, near the source of data. Edge computing enables faster response times, lower latency, and reduced bandwidth requirements.
Enterprise Resource Planning (ERP) #
A suite of integrated applications that manage core business processes, such as finance, human resources, and supply chain management. ERP systems provide a single source of truth for data and enable organizations to streamline their operations and improve their efficiency.
Fraud Detection #
The process of identifying and preventing fraudulent activities in the supply chain. Fraud detection involves analyzing data and identifying patterns, anomalies, and other indicators of fraud.
Internet of Medical Things (IoMT) #
The network of interconnected medical devices, sensors, and systems that collect and exchange data to improve patient outcomes and healthcare delivery. IoMT can be used to monitor patients, track inventory, and optimize supply chain operations.
Machine Vision #
The use of cameras, sensors, and other devices to capture and analyze visual data. Machine vision can be used for quality control, object recognition, and other applications in the supply chain.
Natural Language Processing (NLP) #
The ability of computers to understand, interpret, and generate human language. NLP can be used for text analysis, sentiment analysis, and other applications in the supply chain.
Operational Intelligence (OI) #
The real-time analysis of data to support decision-making in operations. OI involves the use of machine learning, predictive analytics, and other techniques to identify patterns, trends, and anomalies in data, and to provide actionable insights to operators.
Predictive Maintenance #
The use of data analytics and machine learning to predict and prevent equipment failures in the supply chain. Predictive maintenance involves analyzing data from sensors and other sources to identify potential issues before they become critical.
Real #
time Analytics: The analysis of data in real-time to support decision-making in operations. Real-time analytics involves the use of machine learning