Bloom’s Taxonomy: III, IV, V, VI
Definition
- LI. Remembering: Exhibit memory of previously learned material by recalling facts, terms, basic concepts, and answers.
- LII. Understanding: Demonstrate understanding of facts and ideas by organizing, comparing, translating, interpreting, giving descriptions and stating main ideas.
- LIII. Applying: Solve problems in new situations by applying acquired knowledge, facts, techniques, and rules in a different way.
- LIV. Analyzing: Examine and break information into parts by identifying motives or causes. Make inferences and find evidence to support generalizations.
- LV. Evaluating: Present and defend opinions by making judgments about information, validity of ideas, or quality of work based on a set of criteria.
- LVI. Creating: Compile information in a different way by combining elements in a new pattern or proposing alternative solutions.
Topic Description
The success of food and beverage processing businesses in the future will hinge on integrated data systems and a data-driven culture. This will be especially critical as more and more businesses transition to the use of Big Data and Artificial Intelligence (AI) as routine strategies in business planning and management.
Learning Objectives
LO1. Develop essential data literacy and analytical mindset.
This topic allows manager to identify major data sources, existing databases, and data strategies and evaluate how data analysis is used to support business decisions and create the data culture and mindset.
Detailed Competencies = Performance indicators include but are not limited to:
- P1. Create the culture to address data literacy and analytical mindset.
- P2. Identify key users of data and tailor information and data accordingly (e.g., detailed technical report vs high level summary for senior management).
- P3. Communicate with data analysist regarding basic concepts of data science (structured and unstructured data, data organization, data strategies).
- P4. Extract knowledge from databases.
- P5. Compile available information from multiple sources to see patterns.
- P6. Describe the data analytics cycle in various business contexts and industries.
LO2. Evaluate potential of Big Data and Artificial Intelligence (AI) tools tin the food and beverage processing business.
This topic gives the food and beverage processing manager the knowledge and skills to use Big Data and Artificial Intelligence tools to drive business performance.
Detailed Competencies = Performance indicators include but are not limited to:
- P1. Identify opportunities for collecting new data or collecting data with different tools.
- P2. Consider Findability, Accessibility, Interoperability and Reusability (FAIR) principles while setting the data systems.
- P3. Create a data-driven business by using Big Data and AI tools:
- a. Determine the questions that drive effective and efficient decision-making with data.
- b. Evaluate the tools that will best answer business-relevant questions and drive business performance.
- c. Communicate with data analysts and consultants on data collection tools and methodologies.
- d. Use data collection tools to assemble and present data in useful ways.
- P4. Evaluate Big Data and Artificial Intelligence tools for their potential use in:
- a. monitoring consumer demand.
- b. automated reporting.
- c. product integrity.
- d. customer relations.
- e. sales & marketing.
- f. human resources management.
- g. competitive analysis.
- h. logistics & supply chain management.
- i. inventory control & product flow.
- j. Big Data
- k. Artificial Intelligence
- l. Big Data & AI in business research/decisions
- m. SQL
- n. Google, Microsoft Excel,
- o. Machine Learning Toolkit (Matlab)
LO3. Design data analytics and statistics systems for making business decisions.
This topic gives the food and beverage processing manager the knowledge and skills to design data and statistics to make sound business decisions.
Detailed Competencies = Performance indicators include but are not limited to:
- P1. Use spreadsheets and other data handling software to manage and manipulate data.
- P2. Use data analytics and statistics for trends analysis, market research, forecasting and projections, setting targets, and evaluating risk.
- P3. Evaluate data sets for entry errors, calculation errors, incorrect assumptions, and methodologies.
- P4. Evaluate the integrity of data used in trends analysis, market research, forecasting, projections, and setting targets (source, reliability, long/short-term limitations).
- P5. Use data tools and spreadsheets to assemble and present data in useful ways.
- P6. Evaluate the impact of consumer credit on purchasing power and product sales.
- P7. Evaluate the impact of discounting on product sales.
- P8. Calculate the value of future interest on a capital purchase.
- P9. Calculate the depreciation on a capital purchase.
- P10. Evaluate the impact of the following on capital purchase decisions:
- a. Business credit (borrowing power).
- b. Simple and compound interest rates.
- c. Depreciation.
- P11. Use financial instruments to track assets and liabilities and manage risk.
- P12. Spreadsheets/data software
- P13. Data types, sources, integrity for food and beverage processors
- P14. Consumer & business credit
- P15. Discounting
- P16. Interest/interest rates
- P17. Depreciation
- P18. Financial instruments
LO4. Think proactively of the end game when generating info for data analytics and making business decisions.
Many data needs and challenges arise due to reporting and decision needs, along with their data requirements, often being considered after projects and systems are designed. This results in end users not having the high-quality data they need or reporting capabilities to make decisions. At the outset of projects involving data collection, data management and reporting, the entire data-to-decision spectrum needs to be considered.
Detailed Competencies = Performance indicators include but are not limited to:
- P1. Verify Data Quality e.g., garbage in garbage out, need to ensure the integrity and quality of data if it will be used for business decisions.
- P2. Verify data quality integration are considered at every step of the Information Life Cycle and datasets can horizontally speak to each other.
- P3. Ensure that data is available and accessible to everyone who needs it.
- P4. Enhance the organization’s “data culture”, support with relevant training and solidify partnerships.
- P5. Ensure data accountability where all levels of the organization understand their role in data management and set up governance to support, make data decisions and rules.
Links to existing courses
Approved Accredited Training Programs (Academic, Industries, Private Trainer)
NA
Recognition of worker skills = Certification
NA
Evaluation technics / assessment
- Quizzes
- Written tests
- Multiple choice questions
