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[The 11th Teddy Cup Data Mining Challenge in 2023] Topic B: Data Analysis and Demand Forecasting of Product Orders 23-page paper and implementation code

1 topic

one. problem background

In recent years, the external environment of enterprises has become more and more uncertain, and the complex and changeable external environment has made the supply chain of enterprises face more difficulties.

Demand forecasting is the first line of defense in an enterprise's supply chain, and its importance is self-evident. However, demand forecasting is affected by various factors, resulting in generally low forecasting accuracy. Therefore, more excellent algorithms are needed to solve this problem. Demand forecasting is a theoretically based conclusion based on historical data and future predictions, which is helpful for the company's management to make decisions about future sales and operation plans, goals, and capital budgets; secondly, demand forecasting is helpful for procurement planning And arrange the formulation of production plans to reduce the impact of business fluctuations. If there is no demand forecast or the forecast is inaccurate, many internal decisions about sales, procurement, and financial budgets in the company can only be based on experience, which will lead to insufficient market forecasts, resulting in backlogs or shortages of inventory and funds, etc. Inventory costs.

two. the data shows

The training data (order_train1.csv) in the attachment provides the shipment data of a large domestic manufacturing company to dealers from September 1, 2015 to December 20, 2018 (see Table 1 for the format), reflecting the company's products Price and demand information in different sales regions, including: order_date (order date), sales_region_code (sales region code), item_code (product code), first_cate_code (product category code), second_cate_code (product category code), sales_chan_name ( sales channel name), item_price (product price), and ord_qty (order demand quantity).

Table 1: Data format of training quantity (historical data)
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Among them, the "order date" is the date of a certain demand; one "major product category code" corresponds to multiple "product category codes"; "sales channel name" is divided into online (online) and offline (offline), "Online" refers to e-commerce platforms such as Taobao and JD.com, and "offline" refers to offline physical dealers.

The forecast data (predict_sku1.csv) in the attachment provides the sales area code, product code, product category and product category of the product to be forecasted (see Table 2 for the format).

Table 2: Sample data for products that require forecasting
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three. issues that need resolving

Please conduct an in-depth analysis on the training data (order_train1.csv) in the attachment, you can refer to but not limited to the following main

question.

(1) The impact of different prices of products on the quantity demanded;

(2) The influence of the region where the product is located on the demand, and the characteristics of the product demand in different regions;

(3) Characteristics of product demand for different sales methods (online and offline);

(4) What are the differences and commonalities of product demand among different categories;

(5) What are the characteristics of product demand in different time periods (such as the beginning of the month, the middle of the month, the end of the month, etc.);

(6) The impact of holidays on product demand;

(7) The impact of promotions (such as 618, Double Eleven, etc.) on product demand;

(8) The impact of seasonal factors on product demand.

Based on the above analysis, a mathematical model is established to predict the monthly demand for the products given in the attached forecast data (predict_sku1.csv) in the next three months (that is, January, February, and March 2019), and the forecast results are calculated according to the table 3 and save it as the file result1.xlsx, and submit it together with the thesis. Please make predictions according to the time granularity of day, week, and month respectively, and try to analyze the impact of different prediction granularities on the prediction accuracy.

2 Introduction to the paper

Data analysis of product orders and demand forecast based on Arimax and Var models

Summary

This paper mainly analyzes the data of product orders, discusses the influence of product price, location, sales method, category, time period, holidays, promotional activities and seasonal factors on the demand for product orders, and provides scientific decision-making basis for enterprises. Through group statistics and scatter plots to display the experimental results, it is found that the price has a certain influence on the demand, and the demand changes with the price. Using visual analysis and variance analysis, it is found that the region where the product is located has a significant impact on the demand. The product demand for online and offline sales has different characteristics, and the product demand for different categories varies greatly. Order demand varies seasonally. In addition, this paper also uses ARIMAX and VAR models for the demand forecasting problem, integrates various indicators and methods to improve the robustness and scientificity of the model, and conducts sensitivity analysis according to the time granularity. The results show that when the time granularity is days, The prediction results of the ARIMAX model are relatively more accurate. To sum up, the research results of this paper have certain guiding significance for enterprises to formulate production and sales strategies.

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3 Obtaining methods

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