IOM440 Data Science for Supply Chain Management
面向供应链管理的 IOM440 数据科学
Semester 1, 2024/25 Mini-project Report
2024/25 学年第一学期 项目报告
Submission Deadline: 23:00 on Dec 17th, 2024
提交截止日期：2024 年 12 月 17 日 23：00
A dataset and the associated background information will be provided. The mini-project must be conducted individually. Each student should perform descriptive analytics and predictive analytics on the dataset to gain informative insights.
将提供数据集和关联的背景信息。小型项目必须单独进行。每个学生都应对数据集进行描述性分析和预测分析，以获得信息丰富的见解。
In particular, the analysis should be based on a supply chain dataset used by DataCo Global. The dataset contains both structured data and unstructured data. The dataset presents various features of important activities such as provisioning, production, sales, and commercial distribution for three different types of products: Clothing, Sports, and Electronic Supplies. It allows for the use of machine learning algorithms and R software. For the mini-project, you focus on two types of products: Clothing and Electronic Supplies.
特别是，分析应基于 DataCo Global 使用的供应链数据集。数据集包含结构化数据和非结构化数据。该数据集显示了三种不同类型的产品（服装、运动和电子用品）的重要活动的各种特征，例如供应、生产、销售和商业分销。它允许使用机器学习算法和 R 软件。对于这个小型项目，您专注于两种类型的产品：服装和电子用品。
Overall, in this mini-project, you are required to perform analytics to identify patterns, trends, and insights to inform decision-making and improve supply chain performance. In particular, the analysis contains three major parts: sales, delivery, and customer segment. For the sales part, you need to identify the top-selling products, the selling trends over time, the potential opportunities to increase sales and profits, etc. For the delivery part, you need to identify the areas of the supply chain where the delays and/or other issues are happening and propose potential solutions to improve the delivery performance. For the customer segment part, you need to analyze customer data to classify customers and then provide suggestions for marketing and sales promotions based on customer segments to improve customer loyalty. The last part of the analysis is to conduct predictive models to forecast sales.
总体而言，在这个小型项目中，您需要执行分析以识别模式、趋势和见解，从而为决策提供信息并提高供应链绩效。特别是，该分析包含三个主要部分：销售、交付和客户细分。对于销售部分，您需要确定最畅销的产品、随时间推移的销售趋势、增加销售额和利润的潜在机会等。对于交付部分，您需要确定供应链中发生延误和/或其他问题的区域，并提出潜在的解决方案来提高交付绩效。对于客户细分部分，您需要分析客户数据对客户进行分类，然后根据客户细分提供营销和促销活动建议，以提高客户忠诚度。分析的最后一部分是进行预测模型来预测销售。
Report guidelines
Report 指南
The final report consists of two major parts: an .Rmd file that contains all source code and an associated HTML file generated by R notebook.
最终报告由两个主要部分组成： an .Rmd 文件，其中包含所有源代码和由 R 笔记本生成的关联 HTML 文件。
GENERAL INSTRUCTIONS TO CANDIDATES:
对考生的一般说明：
This assignment comprises 100 marks and weighs 70% in the final score of this module.
该作业包括 100 分，占本模块最终分数的 70%。
The report should be written in English.
报告应以英文撰写。
Please use the provided report template to produce your report.
请使用提供的报告模板生成您的报告。
An electronic version of the report in HTML and a source code file should be submitted. The electronic file and source code file names should be Module Number + “Mini-project” + Your Name + Student ID.
应提交 HTML 格式的电子版报告和源代码文件。电子文件和源代码文件名应为模块编号 + “迷你项目” + 您的姓名 + 学生证。
For example: IOM440 Mini-project Qian Luo xxxxxx.
例如：IOM440 Mini-project Qian Luo xxxxxx。
Standard XJTLU penalties apply for lateness and plagiarism. Please note that weekends are treated as normal working days and count towards the lateness.
标准的 XJTLU 处罚适用于迟到和抄袭。请注意，周末被视为正常工作日，并计入迟到。
HAND-IN REQUIREMENTS:
提交要求：
You should aim to produce a report within 2,000-word counts (excluding table, figures, and code). Marks of up to 5% points will be deducted if you overshoot the word limit.
您应该以生成 2,000 字以内的报告（不包括表格、图表和代码）。如果您超过字数限制，最高 5% 的分数将被扣除。
Please use the following structure to write your report:
请使用以下结构编写您的报告：
Head information (1%): The title of the project, your name, and dates
标题信息 （1%）：项目名称、您的姓名和日期
General introduction of your work (4%): Briefly summarize the problems of the project, how you perform the descriptive analytics and predictive analytics, findings, suggestions, etc.
一般介绍您的工作 （4%）：简要总结项目的问题、您如何进行描述性分析和预测分析、结果、建议等。
Sales Analysis (20%): present your analysis regarding sales data and your findings and insights, and make suggestions to improve sales volume and profits.
销售分析 （20%）：展示您对销售数据的分析以及您的发现和见解，并提出提高销量和利润的建议。
Delivery Analysis (20%): present your analysis of delivery operation, identify the potential issues, and make relevant suggestions to solve the problems.
投放分析 （20%）：展示您对投放运营的分析，识别潜在问题，并提出相关建议来解决问题。
Customer Segment Analysis (20%): Classify customers into different groups based on some rules or features. Discuss how to improve the sales, deliveries, and/or profits based on your segmentation.
客户群分析 （20%）： 根据一些规则或功能将客户分为不同的组。讨论如何根据您的细分提高销售额、交付量和/或利润。
Sales forecasting (30%): build predictive models (you must use at least one predictive technique introduced in this module) to forecast product sales and select the best model.
销售预测 （30%）：构建预测模型（您必须至少使用本模块中介绍的一种预测技术）来预测产品销售并选择最佳模型。
Writing style (5%)
写作风格 （5%）
You are required to do this mini-project individually. Your assignment markings are graded based on the marking scheme shown on the next page.
您需要单独完成此小项目。您的作业评分将根据下一页上显示的评分方案进行评分。
Writing tips:
写作技巧：
To effectively convey your understanding and illustrate your points:
为了有效地传达您的理解并说明您的观点：
Use simple words, short sentences, and short paragraphs.
使用简单的单词、短句和短段落。
Use graphics (such as figures and tables) if applicable, number and name them.
如果适用，使用图形（例如图形和表格），对它们进行编号和命名。
Use data, examples, and/or cases to support your statements and arguments when it is necessary.
必要时使用数据、示例和/或案例来支持您的陈述和论点。
Use Harvard style for your citation (when applicable).
使用哈佛风格进行引用（如适用）。