TecnoCampus Foundation
eCampus
University Business
eCampus

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General information

Subject type: Mandatory

Coordinator: Maria Dolors Celma Benaiges

Trimester: Second term

Credits: 2

Teaching staff: 

Ivette Fuentes Molina

Academic year: 2025

Teaching course: 1

Languages ​​of instruction

  • Spanish

Competencies / Learning Outcomes

Basic skills

  • CB6-Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context

  • CB7. That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ​​study. 

Specific skills

  • CE1. Show critical distance autonomy in issues or issues related to the maritime business, logistics and supply chain and in the application of innovative ideas in these areas.

  • CE2. Apply tools and methodologies that facilitate creative and innovative thinking in everyday situations related to the supply chain environment and logistics and maritime businesses.

  • CE4. Strategically manage business innovation processes in the supply chain and the maritime business, from diagnosis to application, being able to align resources, capabilities and skills to implement them

  • CE6. Evaluate the performance of the entire logistics system, taking into account the fulfillment / non-fulfillment of the objectives of quality, cost and service planned to detect and prioritize areas for improvement.

  • CE7. Manage (plan, schedule and control) the flow of materials and information (supply chain flow) through the coordinated direction and management of the areas of purchasing, production and physical distribution of the company. 

Transversal competences

  • CT1. Show willingness to learn about new cultures, experiment with new methodologies and encourage international exchange in the context of logistics, supply chain and maritime business.

  • CT2. Demonstrate entrepreneurial leadership and leadership skills that build personal confidence and reduce risk aversion. 

  • CT3. Develop tasks applying the acquired knowledge with flexibility and creativity and adapting them to new contexts and situations. 

Presentation of the subject

This subject aims to analyze the types of demand and generate an estimate of the company's demand through various tools. This involves calculating and forecasting the demand for goods or services to align it with production. Demand planning allows the demand planner to efficiently project activities and processes and propose short and long-term strategies.

Contents

Introduction: Demand planning and forecasting

Topic 1. Demand

1.1 Definition of demand according to types of company and production processes

1.2 Types of demand and production needs

Topic 2. Demand forecasting models

2.1 Demand forecasting models: Primary data collection techniques

2.1.1 Sample selection

2.1.2 The focus group

2.1.3 The survey

2.1.4 Others

2.2 Demand forecasting models: Quantitative techniques

2.2.1 Short-term demand forecasting models based on time series

- Moving averages

- Simple exponential smoothing method

2.2.2 Long-term demand forecasting models

- Time series regression

- Seasonality

- Trend

2.2.3 Cause and effect models

- Simple and multiple regression models

Activities and evaluation system

The final assessment will consist of two parts: a) practical cases carried out in teams and b) a final exam.

a) 50% Practical cases

  • Final report 20%
  • Presentation 30%

b) 50% Final exam

Students must pass each part of the assessment separately to pass the course (minimum grade of 5 out of 10 in each assessment element).

Bibliography

Basic

Armstrong, JS (Ed.). (2001). Principles of forecasting: a handbook for researchers and practitioners (Vol. 30). Springer Science & Business Media.

Chopra, S. (2019). Supply Chain Management: Strategy, Planning, and Operation. 7th ed. Pearson.

Jain, CL, & Malehorn, J. (2012). Fundamentals of demand planning & forecasting. Graceway Publisher.

Complementary

BV, BP, & Dakshayini, M. (2020). An Effective Multiple Linear Regression-Based Forecasting Model for Demand-Based Constructive Farming. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 15(2), 1-18.

Cohen, MJ (2021). New conceptions of sufficient home size in high-income countries: Are we approaching a sustainable consumption transition?. Housing, Theory and Society, 38(2), 173-203

Crum, C., & Palmatier, GE (2003). Demand management best practices: process, principles, and collaboration. J. Ross Publishing.

Feng, Y., & Wang, S. (2017, May). A forecast for bicycle rental demand based on random forests and multiple linear regression. 16th International Conference on Computer and Information Science (ICIS) (pp. 101-105). IEEE.

Juárez, AC, Zuñiga, CA, Flores, JLM, & Partida, DS (2016). Analysis of time series in the forecast of demand for storage of perishable products. Management Studies, 32(141), 387-396.

Peña, D. (2010). Time series analysis. Publishing alliance.

Perez, C. (2011). Time series, techniques and tools. Madrid, Garceta Editorial Group.

de Los Mozos, EA, Badurdeen, F., & Dossou, PE (2020). Sustainable consumption by reducing food waste: A review of the current state and directions for future research. Procedia Manufacturing, 51, 1791-1798.