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Automation of Data Operations

Ikigai can generate daily purchase orders and send them automatically or after human approval to the desired downstream system. Read more here.

Sep 30, 2022
15 minutes to read

The Common Struggle. We live in an era where data is at the core of operations for any organization. As the organization scales, so does the data itself, along with the corresponding data pipelines as well as the data stores (now also called lakes), and managing them becomes increasingly challenging even for a sizable team. To much surprise, many organizations have been accustomed to using outdated data applications such as spreadsheets and plain-text files, or even doing things by hand. While these have surely been functioning, it is definitely inefficient in the best case, and error-prone and loss-making in the worst case. The common dilemmas companies face when working with data applications include, but are not limited to: poor quality data output, messy data, unstable data pipelines, and much more. This also leads to the inability of organizations to utilize AI/ML tools that are becoming available at large. While many recent, and very expensive as well as disruptive solutions are coming up to provide a “common” data view, they lack easy to use decision making tools including AI / ML and automation functionalities.

This raises a natural question: why is there not a single data decision and automation platform that enables connections to various data sources at scale, utilizes BI, AI and ML tools on such a unified data view, performs optimized decision making with human in the loop interaction and automates them while keeping humans involved.

The Solution, Ikigai to Rescue. Ikigai has built a cloud-native scalable platform (hosted as well as on-prem versions — want to try it out?) to precisely address this challenge. A “data operator” can build such end-to-end data to decisions to automation with the human in the loop powered by AI/ML with Ikigai with ease. As an example, consider an ‘e-commerce supply chain’ data operator whose singular goal is to determine the day-to-day production planning. Without Ikigai, this requires them to connect to a multitude of datasets daily, somehow create a “seeming ground truth” in a spreadsheet to understand the current inventory positions in the different stages of the supply chain, with the help of data scientists or past trends or simply with ingenious instincts “forecast” the demand and use these to somehow decide the next set of purchase orders.

And this is without fully grasping the primary goal — how good a financial instrument is the e-commerce supply chain? That is, given that on average, it is holding $X/day across the supply chain, what are the daily, weekly, monthly or yearly returns on it? At its core, this is the primary evaluation criteria for every for-profit organization.

At Ikigai, such a data operator can simply obtain a “template” and build an end-to-end decision making solution with humans in the loop within days. And from that point onwards, they need to daily monitor the various interactive, automation dashboard and intervene with simple “clicks” to provide their input.

In short, Ikigai can generate daily purchase orders and send them automatically or after human approval to the desired downstream system (via email, download, downstream integration). 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 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 with a hot of one button. And most importantly, it provides ways to understand how many opportunities you are losing with your existing strategy and how to optimize it in an interpretable manner.

Want to change your e-commerce supply chain operations? Give it a try here.

About the Author

Ryan Kang (Data Analyst)

Ryan Kang is a graduate of the University of California, Berkeley with a bachelor’s degree in data science. He has a background in software programming, mathematics, and economics that has helped build a strong foundation for implementing various data analytic solutions within the e-commerce industry by using the Ikigai product.

Originally published on medium.com

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Ryan Kang

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