Using Big data and analytics in Operations#300924

• Analyse the impact of future trends on data generation and the evolving landscape of big data in operations.
• Identify key challenges and opportunities within current operational data management practices, considering future advancements in data technologies and analytics capabilities.
• Develop and implement future-proof strategies for big data collection, management, and analysis to gain actionable insights for operational improvement.
• Leverage advanced data analytics techniques like machine learning and artificial intelligence to optimize resource allocation, predict maintenance needs, and improve forecasting accuracy.
• Utilize big data and analytics to identify potential disruptions and develop proactive risk mitigation strategies within operations.
• Design and implement data-driven performance dashboards and metrics to track operational effectiveness in a future context.
• Communicate the value proposition of big data analytics to different stakeholders within the organization, highlighting the impact on cost savings, efficiency improvement, and competitive advantage.
• Develop a strategic action plan for implementing future-oriented big data and analytics practices within their operations.

• Operations managers, directors, and executives seeking to leverage big data and analytics for improved decision-making and operational efficiency.
• Data analysts and business intelligence professionals specializing in operational data analysis.
• Supply chain and logistics professionals interested in using data to optimize their networks.
• Anyone interested in gaining a comprehensive understanding of big data, analytics, and their application in future-proofing operations.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The Future of Big Data in Operations
• Welcome and program overview.
• The Big Data Revolution in Operations: Exploring the impact of big data on operational management practices, including data-driven decision making, predictive maintenance, and real-time optimization.
• Future Studies for Operations Professionals: Learning how to incorporate scenario planning, horizon scanning, and other future studies methodologies to identify trends that will impact data generation and analytics within operations in the future.
• Case Study: analysing a real-world example of a company that successfully leveraged big data and analytics to gain a competitive advantage in its industry.
Day 2: Identifying Future Challenges & Opportunities for Big Data in Operations
• Mapping Your Operational Data Landscape: Participants collaboratively map the current state of data collection, storage, and analysis within their operations, identifying potential bottlenecks and opportunities for improvement in a future context.
• Future Trends & Big Data Impact: Delving into specific future trends with high potential to impact big data and analytics in operations, such as:
o The rise of the Internet of Things (IoT): The proliferation of connected devices will generate massive amounts of data that can be leveraged for operational insights.
o Advancements in data security and privacy regulations: Operational data management practices will need to comply with stricter regulations regarding data protection in the future.
o Evolving data analytics capabilities: Participants explore emerging techniques like artificial intelligence (AI) and machine learning (ML) with the potential to unlock deeper insights from operational data.
o The growing importance of data governance: Developing robust data governance frameworks will be crucial for ensuring data quality, reliability, and accessibility for future analytics initiatives.
• Scenario Planning for Data-Driven Operations: Participants work in teams to develop future scenarios that may impact data generation within their operations and identify challenges and opportunities associated with each scenario, considering how their data management and analytics strategy would need to adapt.
Day 3: Building Future-Proof Big Data Strategies
• Developing a Future-Oriented Data Strategy: Learning how to develop and implement future-proof data strategies that consider not only current needs but also potential future requirements of big data and analytics within operations. This may include:
o Identifying key performance indicators (KPIs) relevant for future success.
o Implementing data governance frameworks that ensure data quality and compliance with future regulations.
o Selecting and integrating scalable data storage and analytics solutions to accommodate future data growth and evolving needs.
• Advanced Data Analytics Techniques for Operations: Exploring how to leverage advanced data analytics techniques like:
o Machine learning: Predictive maintenance, demand forecasting, and optimizing resource allocation based on historical data and real-time patterns.
o Artificial intelligence: Automating data analysis tasks, identifying anomalies and potential disruptions, and generating data-driven recommendations for improvement.
o Big data visualization: Creating clear and compelling data visualizations to communicate operational insights to different stakeholders.
• Hands-on Data Analysis Workshops: Participants gain practical experience using industry-standard data analysis tools and platforms to analyze real-world operational datasets, applying techniques learned throughout the program.
Day 4: Ensuring Data-Driven Decision Making & Risk Management
• Leveraging Big Data for Proactive Risk Management: Learning how to utilize big data analytics to identify potential disruptions and vulnerabilities within operations, allowing for proactive risk mitigation strategies. This may include:
o Identifying operational bottlenecks and predicting potential equipment failures.
o analysing historical data to assess the impact of potential disruptions like weather events or supply chain disruptions.
o Developing data-driven contingency plans to ensure operational continuity in the face of unforeseen challenges.
• Designing Data-Driven Performance Dashboards: Participants learn how to design and implement data-driven performance dashboards that track key operational metrics and KPIs relevant for future success. These dashboards should be adaptable to evolving needs and provide insights for informed decision-making.
• The Human Factor in Big Data Analytics: Exploring the importance of human expertise alongside data analysis tools. Participants discuss strategies for fostering collaboration between data analysts and operations professionals to ensure insights are translated into actionable improvements.

Day 5: Action Planning & The Future of Big Data in Operations
• Communicating the Value of Big Data & Analytics: Developing effective communication strategies to showcase the value proposition of big data analytics to different stakeholders within the organization, including senior management, employees impacted by data-driven changes, and potential investors. Highlighting the impact on cost savings, efficiency improvement, competitive advantage, and risk mitigation.
• Developing a Strategic Action Plan: Participants develop personalized action plans outlining steps to implement future-oriented big data and analytics practices within their operations. This may include:
o Conducting future studies analyses to identify trends impacting data generation in their industry.
o Investing in infrastructure and talent to support big data management and analysis.
o Implementing data governance frameworks to ensure data quality and compliance.
o Integrating advanced data analytics techniques into operational decision-making processes.
• The Future of Big Data in Operations: Discussing future trends and potential advancements in big data technologies and analytics capabilities. Participants explore how the role of data might evolve within operations and how to stay ahead of the curve.
• Course Wrap-Up & Ongoing Support: Reviewing key takeaways from the program, addressing any remaining questions, and discussing