塑胶跑道铺装设备故障检测报修系统需求量预测数据

本数据聚焦于预测塑胶跑道铺装设备故障检测报修系统的需求量，为公司（系统供应商）及外部相关方提供了重要的决策依据，具有显著的应用价值。具体体现在以下方面：
1.优化库存与销售策略​​：
对公司而言，通过预测各区域市场需求量，可以精准制定采购计划，合理配置不同版本的库存比例，避免库存积压或供应短缺。同时可根据预测数据调整区域销售策略，在需求旺盛地区加大市场推广力度，提升销售业绩。
2.辅助场馆维护与采购决策​：对体育场馆运营方、学校体育设施管理部门和工程承建商而言，本预测数据可作为系统采购和维保升级计划的重要参考，可根据需求趋势提前制定智能化改造预算，优化维护周期并降低运营成本，从而与系统供应商形成更高效的长期合作关系。此外，数据还能帮助维保服务商合理规划智能检测设备的配置方案，提升服务质量和效率。1.数据采集：
采集公司塑胶跑道铺装设备故障检测报修系统的销售数据，包括订单编号、客户编号、客户所在地区、订单日期、系统型号、订单数量（个）、订单金额（元）。
2.数据预处理：
对采集的数据进行清洗，去除重复记录，处理缺失值。
3.数据加工与分析：
（1）计算历史需求量：对于每种塑胶跑道铺装设备故障检测报修系统型号，使用SUMIFS函数对订单数量进行累加，分别计算出其过去365天、90天和30天的总需求量。（2）建立需求量预测模型：每种塑胶跑道铺装设备故障检测报修系统型号的未来30天需求量预测值=[(过去365天总需求量÷365*a)+(过去90天的总需求量÷90*b)+(过去30天的总需求量÷30*c)]*30*k；其中，系数a=0.5，b=0.3，c=0.2，调整因子k=1.1。系数a、b、c反映数值对未来30天需求量预测的影响程度，由于算法更注重长期需求趋势的影响，因此a被赋予了最高的权重。k是基于公司在浙江地区的市场增长预期给出的修正值。

This dataset focuses on forecasting the demand for the fault detection and repair reporting system of plastic runway paving equipment, providing important decision-making basis for the company (system supplier) and external stakeholders, and has significant application value. Specifically, its value is reflected in the following aspects:
1. Optimizing inventory and sales strategies:
For the company, by forecasting the market demand in each region, precise procurement plans can be formulated, and the inventory proportion of different versions can be reasonably allocated to avoid inventory overstock or supply shortage. Meanwhile, regional sales strategies can be adjusted based on the forecast data, with increased marketing efforts in regions with high demand to improve sales performance.
2. Assisting venue maintenance and procurement decisions: For sports venue operators, school sports facility management departments and general contractors, this forecast data can serve as an important reference for system procurement and maintenance and upgrade plans. They can formulate intelligent renovation budgets in advance based on demand trends, optimize maintenance cycles and reduce operating costs, thereby forming a more efficient long-term cooperative relationship with system suppliers. In addition, the data can also help maintenance service providers reasonably plan the configuration plan of intelligent detection equipment, improving service quality and efficiency.
1. Data collection:
Collect the sales data of the fault detection and repair reporting system of plastic runway paving equipment of the company, including order number, customer number, customer's region, order date, system model, order quantity (unit), order amount (yuan).
2. Data preprocessing:
Clean the collected data, remove duplicate records, and handle missing values.
3. Data processing and analysis:
(1) Calculate historical demand: For each model of the fault detection and repair reporting system of plastic runway paving equipment, use the SUMIFS function to accumulate the order quantity, and calculate the total demand over the past 365 days, 90 days and 30 days respectively.
(2) Establish a demand forecasting model: The 30-day future demand forecast value of each model of the fault detection and repair reporting system of plastic runway paving equipment = [(Total demand over the past 365 days ÷ 365 * a) + (Total demand over the past 90 days ÷ 90 * b) + (Total demand over the past 30 days ÷ 30 * c)] * 30 * k; where the coefficients a=0.5, b=0.3, c=0.2, and the adjustment factor k=1.1. The coefficients a, b, and c reflect the impact of values on the 30-day future demand forecast. Since the algorithm pays more attention to the impact of long-term demand trends, a is given the highest weight. k is a correction value based on the company's market growth expectations in the Zhejiang region.