{"id":35311,"date":"2022-12-18T23:39:29","date_gmt":"2022-12-19T04:39:29","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=35311"},"modified":"2022-12-19T11:20:05","modified_gmt":"2022-12-19T16:20:05","slug":"process-optimization-using-kpi-dashboard","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/process-optimization-using-kpi-dashboard\/","title":{"rendered":"Process Optimization using KPI Dashboard"},"content":{"rendered":"\n

Background<\/strong><\/p>\n\n\n\n

When I say process, What do I mean? Everything that happens in between you ordering a book in Amazon and it being delivered to your doorstep, or You calling customer care and your problem being fixed by an agent. This series of actions is called a Business Process. A Business Process is also defined as \u2018A collection of linked tasks that find their end in the delivery of a service or product to a client\u2019 by Appian.com<\/em>. These tasks that compose each business process are called events. For example, Amazon handles 1.6 million orders daily and not all orders follow the same ideal path. They tend to have variations and exceptions in the path followed, departments and resources involved.  In modern enterprise systems these are maintained as detailed records of events that occur during the execution of the business processes they support.  This collection of records is called an Event Log.<\/p>\n\n\n

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Incident Response Log aka Event Log<\/figcaption><\/figure>\n<\/div>\n\n\n

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With this much data in hand, patterns start arising. Finding these patterns from an event log aka analyzing an event log is valuable as it helps the company to identify issues and opportunities for improvement. An approach to analyzing event logs with the help of different types of visualizations<\/strong> is called Process mining. These visualizations should allow process analysts and process owners to identify issues and opportunities for improvement such as bottlenecks, sources of waste, and root causes of service-level violations. And that is what Process Optimization does! A service that helps users find and fix inefficiencies and discover potential opportunities within a company\u2019s processes to save resources (time, talent, and money).<\/p>\n\n\n\n

For this report, we will be analyzing the processes of a business unit called Incident Management. Incident management is responsible for restoring services and resolving issues quickly. Keeping employees productive and happy is its main goal by ensuring that they can easily contact support to track and fix issues. Now, Let’s optimize their processes.<\/p>\n\n\n\n

Materials <\/strong><\/p>\n\n\n\n

In order to analyze an Incident Management process using visualization, 1 Dataset: Incident Response Log<\/strong>, an event log of an incident management process extracted from data gathered from the audit system of an instance of the ServiceNowTM platform used by an IT company downloaded from<\/p>\n\n\n\n

Incident Response Log<\/em>. (n.d.). Www.kaggle.com. Retrieved October 23, 2022, from https:\/\/www.kaggle.com\/datasets\/vipulshinde\/incident-response-log?select=Incident_response.txt<\/a><\/p>\n\n\n\n

and two main software will be used, one visualization software: Tableau, and one spreadsheet software: MS Excel. The data from Incident Response Log is exported as .csv directly to Tableau, which will then be modeled into custom visualizations that benefits our use case.<\/p>\n\n\n\n

Method<\/strong><\/p>\n\n\n\n

The focus of this report is to create appropriate visualizations that best benefit process analysts and process owners to identify, track, and resolve high\u2011impact incidents to optimize an incident management process, and to do that we need one main dataset, here we are using \u2018Incident Response Log\u2019.<\/p>\n\n\n\n

As a process owner of Incident Management (IM), the main responsibility is Defining metrics & KPIs <\/strong>for their business processes to<\/p>\n\n\n\n

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  1. Create and maintain consistent processes.<\/li>\n\n\n\n
  2. Identifying opportunities for automation.<\/li>\n\n\n\n
  3. Perform Analysis, identify inefficiencies, and delegate analysis.<\/li>\n<\/ol>\n\n\n\n

    KPIs (Key performance indicators) are metrics used by businesses to help track their goals and tracking KPIs can help identify anomalies quickly which will provide a jumping-off point for larger questions.<\/p>\n\n\n\n

    Thus decided to create KPI Dashboard to help optimize a process where data visualizations can be used to present and interpret KPI data allowing users to quickly and easily understand trends, patterns, and relationships in the data.<\/p>\n\n\n\n

    The initial exploration started with 3 main questions,<\/p>\n\n\n\n

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    1. Can we create data visualization to monitor and track KPIs from the event log dataset?<\/li>\n\n\n\n
    2.  Can we map \u201cwhich factor is causing the most impact\u201d?<\/li>\n\n\n\n
    3.  Will it help process analysts to find inefficiencies & opportunities quickly?<\/li>\n<\/ol>\n\n\n\n

      To get a deeper understanding of how Data Visualization can be used to analyze and optimize Business Processes, I studied journal articles, conference and research papers, related to Process Optimization and found one of the main issues still encountered in the area of business process mining outlined by van der Aalst (2004a) is Visualizing Results<\/strong>. He states \u201cThe results of process mining may be presented in graphical form in terms of a management panel.\u201d <\/em><\/p>\n\n\n\n

      Visualization is a central part of exploratory data analysis. Data analysts use visualization to examine, scrutinize, and validate their analysis before they report their findings. Decision-makers use visualization to explore and question the findings before they develop action plans. Each group of people using the data needs different graphics and visualization tools to do its work (Myatt, Glenn J., 1969)<\/p>\n\n\n\n

      Things to remember while using visualization to analyze Business Process mining from Making sense of data II: a practical guide to data visualization, advanced data mining methods, and applications\/Glenn J. Myatt, Wayne P. Johnson.<\/em><\/p>\n\n\n\n