Power BI 的供应商质量分析示例:教程Supplier Quality Analysis sample for Power BI: Take a tour

“供应商质量分析示例”简要概述A brief overview of the Supplier Quality Analysis sample

此行业示例仪表板与基础报表着重于传统供应链的其中一项挑战 — 供应商质量分析。This industry sample dashboard and underlying report focus on one of the typical supply chain challenges — supplier quality analysis. 有两个主要度量值在此分析中发挥作用:瑕疵品总数和瑕疵品所造成的停工时间总计。Two primary metrics are at play in this analysis: total number of defects and the total downtime that these defects caused. 此示例有两个主要目标:This sample has two main objectives:

  • 确定质量最好和最差的供应商Understand who the best and worst suppliers are, with respect to quality
  • 确定可以更好地发现和避免缺陷的工厂,以便最大限度地减少故障时间Identify which plants do a better job finding and rejecting defects, to minimize downtime

此示例是一系列教程的一部分,该系列教程阐明如何将 Power BI 与面向业务的数据、报表及仪表板搭配使用。This sample is part of a series that illustrates how you can use Power BI with business-oriented data, reports, and dashboards. 这些来自 obviEnce (www.obvience.com) 的真实数据都已进行匿名处理。This is real data from obviEnce (www.obvience.com) that has been anonymized.

先决条件Prerequisites

必须先将示例下载为内容包、.pbix 文件或 Excel 工作簿,然后才能使用它。Before you can use the sample, you must first download it as a content pack, .pbix file, or Excel workbook.

获取内容包形式的此示例Get the content pack for this sample

  1. 打开并登录 Power BI 服务 (app.powerbi.com)。Open the Power BI service (app.powerbi.com) and log in.
  2. 在左下角,选择“获取数据”。In the bottom left corner select Get data.

  3. 在显示的“获取数据”页上选择“示例”图标。On the Get Data page that appears, select the Samples icon.

  4. 依次选择“供应商质量分析示例”和“连接”。Select the Supplier Quality Analysis Sample, then choose Connect.

    供应商质量分析示例

  5. Power BI 导入内容包,并将新的仪表板、报表和数据集添加到当前工作区。Power BI imports the content pack and adds a new dashboard, report, and dataset to your current workspace. 新的内容会以黄色星号标记。The new content is marked with a yellow asterisk.

    星号

获取 .pbix 文件形式的此示例Get the .pbix file for this sample

也可以将此示例下载为 .pbix 文件,这是专为 Power BI Desktop 量身定制的文件格式。Alternatively, you can download the sample as a .pbix file, which is designed for use with Power BI Desktop.

获取 Excel 工作簿形式的此示例Get the Excel workbook for this sample

还可以针对此示例仅下载该数据集(Excel 工作簿)You can also download just the dataset (Excel workbook) for this sample. 该工作簿包含你可以查看和修改的 Power View 工作表。The workbook contains Power View sheets that you can view and modify. 若要查看原始数据,请选择“Power Pivot”>“管理”。To see the raw data select Power Pivot > Manage.

用料瑕疵所造成的停工时间Downtime caused by defective materials

让我们来分析用料瑕疵所造成的停工时间,并查看应由哪些供应商负责。Let’s analyze the downtime caused by defective materials and see which vendors are responsible.

  1. 在仪表板中,选择瑕疵品总数数字磁贴或停工时间总分钟数数字磁贴。On the dashboard, select the Total Defect Quantity number tile or the Total Downtime Minutes number tile.

    “供应商质量分析示例”报表会打开“停工时间分析”页面。The “Supplier Quality Analysis Sample” report opens to the “Downtime Analysis” page. 请注意,我们一共有 3300 万个瑕疵品,而这些瑕疵品所造成的停工时间总计为 77000 分钟。Notice we have 33M total defective pieces, and the total downtime caused by these defective pieces is 77K minutes. 虽然有些用料的瑕疵品较少,但它们会导致严重延误,从而导致停工时间更长。Some materials have fewer defective pieces but they can cause a huge delay resulting in larger downtime. 让我们在报表页面上浏览这些项目。Let’s explore them on the report page.

  2. 查看按用料类型划分的瑕疵品和停工时间(分钟) 组合图中的停工时间总分钟数一行,我们发现起皱的用料会导致最多的停工时间。Looking at the Total Downtime Minutes line in the Defects and Downtime (min) by Material Type combo chart, we see corrugate materials cause the most downtime.
  3. 选择相同组合图中的起皱列,查看哪些工厂受此瑕疵的影响最大,以及哪些供应商应负责。Select the Corrugate column in the same combo chart to see which plants are impacted most by this defect and which vendor is responsible.

  4. 选择地图中的个别工厂,查看哪家供应商或哪种用料该为这家工厂的停工时间负责。Select individual plants in the map to see which vendor or material is responsible for the downtime at that plant.

哪些供应商最差?Which are the worst suppliers?

我们想要找出最差的八家供应商,并决定他们的停工时间百分比责任归属。We want to find the worst eight suppliers and determine what percentage of the downtime they are responsible for creating. 为此,我们可以将按供应商划分的停工时间(分钟) 分区图更改为树状图。We can do this by changing the Downtime (min) by Vendor area chart to a treemap.

  1. 在报表的第 3 页“停工时间分析”中,选择左上角的编辑报表On page 3 of the report, “Downtime Analysis,” select Edit Report in the upper-left corner.
  2. 选择按供应商划分的停工时间(分钟) 分区图,并在“可视化效果”窗格中选择“树状图”。Select the Downtime (min) by Vendor area chart, and in the Visualizations pane select Treemap.

    树状图会自动将供应商字段作为The treemap automatically puts the Vendor field as the Group.

    从此树状图中,我们可以看到前八个供应商是树状图左侧的八个区块。From this tree map, we can see the top eight vendors are the eight blocks on the left of the tree map. 我们也可以看到它们应为约 50% 的停工时间总分钟数承担责任。We can also see they account for about 50% of all downtime minutes.

  3. 在顶部导航栏中选择“供应商质量分析示例”,返回到仪表板。Select Supplier Quality Analysis Sample in the top navigation bar to go back to the dashboard.

工厂比较Comparing plants

现在让我们来探索哪些工厂在管理瑕疵用料方面表现较佳,从而缩短了停工时间。Now let’s explore which plant does a better job managing defective material, resulting in less downtime.

  1. 选择按工厂和瑕疵类型划分的总瑕疵报表地图图块。Select the Total Defect Reports by Plant, Defect Type map tile.

    报表会打开“供应商质量”页面。The report opens to the “Supplier Quality” page.

  2. 在地图图例中,选择影响圆圈。In the map legend, select the Impact circle.

    请注意,气泡图中的物流是最糟糕的类别 — 它在瑕疵品总量、总瑕疵报表和停工时间总分钟数上都居最高位置。Notice in the bubble chart that Logistics is the most troubled category – it’s the largest in terms of total defect quantity, total defect reports, and total downtime minutes. 让我们进一步研究此类别。Let’s explore this category more.

  3. 在气泡图中选择“物流”气泡,并观察伊利诺斯州斯普林菲尔德市和内伯威尔市的工厂。Select the Logistics bubble in the bubble chart and observe the plants in Springfield, IL and Naperville, IL. 内伯威尔市似乎在管理瑕疵供货方面做得更好,因为它的退货量较高,影响量也较小,而斯普林菲尔德市的影响量就较大。Naperville seems to be doing a much better job of managing defective supplies as it has a high number of rejects and few impacts, compared to Springfield’s large number for impacts.

  4. 在顶部导航栏中选择“供应商质量分析示例”,返回到活动工作区。Select Supplier Quality Analysis Sample in the top navigation bar to return to your active workspace.

哪种类型的用料管理最佳?Which material type is best managed?

管理最佳的用料类型是指无论瑕疵品数量如何,都具有最低的停工时间或不造成任何影响的类型。The best managed material type is the one with lowest downtime or no impact, regardless of defect quantity.

  • 在仪表板中,查看按用料类型和瑕疵类型划分的瑕疵品总数磁贴。In the dashboard, look at the Total Defect Quantity by Material Type, Defect Type tile.

请注意原料的瑕疵品总数很多,但大多数瑕疵品都会被退货或不造成任何影响。Notice that Raw Materials have a lot of total defects, but most of the defects are either rejected or have no impact.

由此确认,尽管瑕疵品数量高,原料仍不会造成大量的停工时间。Let’s verify that raw materials don’t cause a lot of downtime, despite high defect quantity.

  • 在仪表板中,查看按用料类型划分的瑕疵品总数和停工时间总分钟数磁贴。In the dashboard, look at the Total Defect Qty, Total Downtime Minutes by Material Type tile.

很显然,原料受到妥善管理:它们的瑕疵品虽然更多,但停工时间总分钟数较低。Apparently raw materials are well managed: they have more defects, but lower total downtime minutes.

按年份比较瑕疵品与停工时间的关系Compare defects to downtime by year

  1. 选择“按工厂和瑕疵类型划分的总瑕疵报表”地图图块,将报表打开至第一个报表页面“供应商质量”。Select the Total Defect Reports by Plant, Defect Type map tile to open the report to the first report page, Supplier Quality.
  2. 请注意,2014 年的瑕疵品数量比 2013 年高。Notice that Defect Qty is higher in 2014 than in 2013.

  3. 瑕疵品多代表停工时间一定也更多吗?Do more defects translate into more downtime? 我们可以在“问答”框中提问以找出答案。We can ask questions in the Q&A box to find out.
  4. 在顶部导航栏中选择“供应商质量分析示例”,返回到仪表板。Select Supplier Quality Analysis Sample in the top navigation bar to go back to the dashboard.
  5. 既然我们知道“原料”具有最高数量的瑕疵品,可在问题框中键入“显示用料类型、年份和瑕疵品总数”。Since we know Raw Materials have the highest number of defects, in the question box, type “show material types, year and total defect qty”.

    2014 年的原料瑕疵品数量比 2013 年高很多。There were many more raw materials defects in 2014 than in 2013.

  6. 现在,将问题改为“显示用料类型、年份和停工时间总分钟数”。Now change the question to “show material types, year and total downtime minutes”.

虽然 2014 年的原料瑕疵品更多,但 2013 年和 2014 年的原料停工时间差不多。Raw materials downtime was about the same in 2013 and 2014, even though there were many more raw materials defects in 2014.

因此,即使 2014 年原料瑕疵品较多,也不会导致 2014 年的原料停工时间更长。It turns out more raw materials defects in 2014 didn’t lead to much more raw materials downtime in 2014.

按月份比较瑕疵品与停工时间的关系Compare defects to downtime month to month

让我们看看另一个与瑕疵品总数相关的仪表板磁贴。Let’s look at another dashboard tile related to total defective quantity.

  1. 选择问题框上方左上角的返回箭头 ,返回仪表板。Select the back arrow in the upper-left corner above the question box to get back to the dashboard.

    进一步查看按月份和年份划分的瑕疵品总数磁贴,可发现 2014 年上半年的瑕疵品数与 2013 年非常接近,但 2014 年下半年的瑕疵品数大幅激增。Looking more closely at the Total Defect Quantity by Month, Year tile shows that the first half of 2014 had a similar number of defects as 2013, but in the second half of 2014, the number of defects jumped significantly.

    我们来看看瑕疵品数的增加是否会导致停工时间分钟数也跟着增加。Let’s see if this increase in defect quantity led to an equal increase in downtime minutes.

  2. 在问题框中键入“按月份和年份划分的停工时间总分钟数折线图”。In the question box, type “total downtime minutes by month and year as a line chart”.

    在 6 月和 10 月间,我们发现停工时间分钟数大增,但除此之外,瑕疵品数并没有明显导致更长的停工时间。We do see a jump in downtime minutes during June and Oct, but other than that, the jump in the number of defects didn’t result in significantly more downtime. 这意味着我们管理瑕疵品的成效很好。This shows we’re managing defects well.

  3. 若要将此图表固定到仪表板,请选择问题框右侧的固定图标 To pin this chart to your dashboard, select the pin icon to the right of the question box.
  4. 若要浏览离群值月份,可提出问题(例如“工厂 10 月份的停工时间总分钟数”),按用料类型、工厂位置、类别等查看 10 月的停工时间分钟数。To explore the outlier months, check out the downtime minutes during Oct by material type, plant location, category, etc. by asking questions such as "total downtime minutes in October by plant".
  5. 选择问题框上方左上角的返回箭头 ,返回仪表板。Select the back arrow in the upper-left corner above the question box to get back to the dashboard.

这是一个安全的试验环境。This is a safe environment to play in. 你可以始终选择不保存所做的更改。You can always choose not to save your changes. 但是,如果保存更改,则可以始终转到获取数据来获取本示例的新副本。But if you do save them, you can always go to Get Data for a new copy of this sample.

后续步骤:连接到你的数据Next steps: Connect to your data

我们希望本教程已经演示 Power BI 仪表板、问答和报表如何帮助深入了解供应商质量数据。We hope this tour has shown how Power BI dashboards, Q&A, and reports can provide insights into supplier quality data. 现在轮到你了 — 连接到你自己的数据。Now it’s your turn — connect to your own data. 借助 Power BI,你可以连接到各种数据源。With Power BI you can connect to a wide variety of data sources. 了解 Power BI 入门的详细信息。Learn more about getting started with Power BI.