Purchase Order Automation
by Conner Cattaneo
The Common Struggle. With the negative effects of the COVID-19 pandemic on the global supply chain, it is now more important than ever to have reliable ways to track and order necessary goods. Far too often, businesses have rough estimates of their current component inventory levels, and order additional materials with a mixed level of confidence. Additionally, the process of ordering the goods themselves can be grueling — from the manual editing of a PDF to fit the current order, to the inconsistently structured emails to suppliers — one can quickly realize there is much room for automation.
The Solution. The powerful web based data analytics, visualization, wrangling, and automation tool of Ikigai is the cure to this headache. With the simple acquisition of our Supply Chain template, data operators can quickly familiarize themselves with our flexible inventory management dashboard. After seamlessly connecting to one of our 180+ supported data sources, data will be kept up to date and Ikigai becomes the central source of truth for inventory management. Rather than manually editing PDF purchase orders, and emailing these requests to suppliers, Ikigai automatically generates an easily-customizable PDF based on your desired input fields within clicks. As soon as this is generated, flexible data flows kick into action, calculating your current inventory levels based on the submitted order in combination with external source inventory levels. Additionally, the PDF will be automatically sent (email, download, downstream integration) to the desired supplier. From within the same dashboard, one can easily visualize their inventory levels at various levels (finished goods, raw materials, etc), view previously generated purchase orders, and even close orders when they are received. Coupled with our easy-to-integrate Demand Forecasting capabilities, Ikigai can even suggest which purchase orders should be generated next.
About the Author
Conner Cattaneo (Data Success Engineer)
Conner is a recent graduate of University of California, Berkeley, majoring in Computer Science and minoring in Data Science. He has experience in software development, as well as in data analytics, often combining the two to better solve the problems businesses face.