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Redefining Subscription-Based Retail: A Global Beauty Brand’s Data-Driven Inventory Transformation

Our client, a leading player in the beauty industry, operates an impressive portfolio, including two of the largest beauty subscription services globally, an innovative brand incubator, and a popular personal care brand. Their business model involves both subscription-based services and the sales of individual items, catering to a diverse clientele with varied needs.

Unraveling the Challenges: Disintegrated Inventory and Demand Data

With a multitude of product offerings, maintaining a detailed and accurate overview of their inventory was absolutely essential.

However, they encountered a substantial hurdle. Their current system did not adequately link their inventory data with the specific quantities of each product required for each unique subscription program. For instance, consider one of their sales initiatives – the monthly beauty subscription boxes. Each box requires a particular set of products in specific quantities to fulfill the customers’ needs. Without a system in place that could align this product requirement data with the existing inventory data, it became increasingly difficult to manage and plan for future boxes.

In essence, if the current inventory data indicated that they had 1000 units of a particular beauty product, but there was no clear linkage with the data showing that 600 units of the same product were required for the upcoming subscription boxes, they were in a blind spot. They wouldn’t know whether the remaining 400 units would be sufficient for other business needs until the subscription boxes were assembled.

Limitations of the Existing Inventory Management Tool

Moreover, the inventory management tool they were using proved to be inadequate. This tool, however, was not designed to integrate or communicate with another crucial piece of data – the forecasted product requirements for upcoming periods.

To illustrate, imagine the client is preparing for their monthly beauty subscription box service for the month of July. They need to know how many units of each product they need to include in these boxes. 

Now, let’s say their current inventory shows that they have 500 units of a certain product in stock. But their forecast shows that they will need 300 units of the same product for the July subscription boxes, and they anticipate a demand of another 300 units for their online store sales.

Ideally, their inventory management tool should be able to integrate this forecasted demand data and alert them that they will fall short by 100 units. However, because the tool was not designed to integrate with the forecasted product requirements, this critical piece of information would be missed.

The inventory management tool was also not able to segregate the BOM (Bill Of Materials) that make up the subscription boxes from individual product sales. Thereby making it difficult to know the current stock levels at each location. In addition, the incoming Purchase Orders were not integrated with the Inventory Management system, thereby making it a manual process to reconcile the data. 

Consequently, the client would face difficulties predicting their future inventory needs accurately, potentially resulting in stockouts. Likewise, without an understanding of upcoming demand, managing their current stock levels would also prove to be challenging, leading to inefficiencies and missed sales opportunities.

This disjointed data scenario created a substantial gap in the client’s inventory visibility, leading to several issues:

  • Inaccurate Demand Forecasting: Because of the lack of consolidated data, accurately predicting product demand became a challenge. This often resulted in stock shortages for popular items and overstocks for less popular ones, impacting their operational efficiency and profitability.
  • Inefficient Resource Utilization: Without an integrated overview of their inventory, resource allocation became a guessing game, often leading to excessive storage costs for overstocked items and missed sales opportunities for understocked ones.
  • Delayed Decision Making: Decisions about product restocking, discontinuation, or introduction relied heavily on accurate inventory data. The absence of real-time, integrated data often led to delays and inefficiencies in these critical business decisions.

Compounding these problems, the client’s heavy dependence on spreadsheets for planning brought its own set of challenges:

  • Outdated Data: As the data on spreadsheets wasn’t updated in real-time, it frequently led to inaccuracies in inventory management and flawed decision-making.
  • Difficulty in Tracking Changes: As the usage of spreadsheets continued over time, tracking changes and identifying trends across different planning cycles became a daunting task. This resulted in missed insights and optimization opportunities.
  • Increased Risk of Data Breaches: Housing critical data across various spreadsheets posed a significant security risk, threatening the company’s reputation and customer trust.

Pluto7 addressed these gaps by connecting the data from various processes and unifying them to give the complete picture of current stock levels.

The Client’s Aim: Streamlining Inventory Management for Peak Efficiency

Our client had a clear set of objectives they wished to accomplish to overcome their inventory management challenges. These included:

  • Integrated Inventory Data Platform: They wanted to create a flexible data platform that could unify all relevant data – from item information, location details, program requirements, to future demand forecasts. This platform would provide a holistic view of their inventory, facilitating more accurate planning and decision-making.
  • Transparent Data Access: It was important for the client to establish a system where data access was transparent and streamlined. It would ensure that all stakeholders, from supply chain managers to executives, had the visibility they needed to make informed decisions.
  • Advanced Planning Tool: The client envisioned an advanced planning tool that could cater to the needs of their supply chain planners. This tool would use integrated data to provide actionable insights for both current and future demands, enhancing the overall efficiency of their inventory management process.

The Pluto7 Solution: An Integrated Approach to Inventory

In response to these objectives, Pluto7 employed a two-pronged approach. Firstly, they worked to integrate data from various sources into a common database. Secondly, they developed a sophisticated inventory planning tool to make full use of the integrated data.

Data Integration

Pluto7 started by gathering data from the various spreadsheets used by the client and moving them into a single, central location in Google Cloud. This first step marked a shift from the client’s previous disjointed system, setting the stage for more advanced, integrated analytics.

As part of this process, several key components were used:

  • Google Cloud Storage (GCS) Bucket: This was used to store the raw data collected from the client’s various spreadsheets.
  • Cloud Function: Pluto7 used Google Cloud’s serverless compute platform to automatically trigger actions such as moving the uploaded files into BigQuery.
  • BigQuery: This was the staging area where files were loaded in their raw format, providing a scalable and cost-effective data warehouse.

Inventory Planning Tool: Visibility across all SKUs 

Once the data was successfully integrated in BigQuery, Pluto7 then turned their attention to developing the advanced planning tool that the client needed. This tool was designed to provide visibility across all SKUs and helped them to plan and manage both current and future orders.

The tool was hosted on Google’s App Engine, a platform for developing and hosting web applications at scale. Data updates were managed using Google Sheets, providing an easy way for the client to interact with the tool and maintain the accuracy of their data.

To display the information in an easily consumable format, a custom application was built on Looker, Google Cloud’s business intelligence platform. This allowed the client to visualize their inventory levels in real-time, a significant upgrade from the snapshot-based planning they had been using previously.

The Transformation: Harnessing Data for Predictive Intelligence

The integration of Pluto7’s data platform solution drastically reshaped the client’s inventory management approach, leading to an evolution in their decision-making process.

Key results of this transformation included:

  • Unlocking Real-Time Inventory Visibility: With Pluto7’s solution, the client moved from a limited, fragmented view of their stock levels to a holistic, real-time overview. 
  • Leveraging Data for Demand Forecasting: The client could now accurately predict demand trends, using data to drive their forecasting. This resulted in more efficient resource allocation and quicker decision-making processes, reducing waste and increasing profitability.
  • Maximizing Cost Efficiency and Consumer Satisfaction: With real-time, accurate data on stock levels and demand, the client could avoid excessive stock holding and associated costs. 
  • Securing and Centralizing Data: The introduction of a centralized data platform improved the security of sensitive information. It also provided a structured and organized way to manage and access inventory data, moving away from disparate and siloed information sources.
  • Creating a Scalable, Future-Ready Operation: With a robust, scalable foundation in place, the client is well-positioned to expand operations in a rapidly changing industry. Pluto7’s solution provided them with a competitive edge and a readiness to manage growing inventory complexities.

The newly implemented platform brings together world-class innovation and decision intelligence, transforming the client’s enterprise data into a powerful strategic asset. They now have access to real-time inventory data, accurate demand forecasting, secure data storage, and advanced analytics, that are equipping them to navigate the current market intricacies and also to foresee future challenges.

From Disconnected Decisions to Intelligent Planning

In the retail world, being proactive and swift is essential. Understanding consumer trends and behaviors is paramount, as these insights drive the strategic decisions that propel a company forward. Agility is crucial, as the landscape can shift abruptly, and companies must be equipped to pivot efficiently. Reducing and optimizing inventory costs is a key component of this agility, as it’s essential to maintain a dynamic inventory that can accommodate sudden spikes or falls in demand. 

The journey with Pluto7 has equipped the client to meet these needs head-on, driving them from a reactive to a proactive state,  swapping decision bottlenecks with data-empowered decision intelligence, and trading excess inventory for an optimized, dynamic stock system. This strategic shift paves the way for a future where data not only fuels decision-making but also infuses efficiency and intelligence into every facet of their business. 

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  • Scattered data across multiple spreadsheets leading to inconsistencies and accessibility issues.
  • Lack of consolidated data caused inaccurate demand prediction resulting in stock shortages for popular items and overstocks for less popular ones, impacting their profitability
  • Inefficient resource allocation because of lack of inventory visibility leading to overstocking and understocking problems.
  • The absence of real-time, integrated data resulting in delays and inefficiencies in critical business decisions.
  • Loss of information across planning cycles due to spreadsheet usage resulted in missed optimization opportunities.
  • Huge risk of data breach and customer privacy because of spreadsheets housing critical data.


  • Transition to a unified data platform, boosting operational efficiency.
  • Optimized inventory management by achieving full stock visibility, minimizing excess holding and related expenses.
  • Data-driven demand forecasting leading to efficient resource allocation.
  • Ability to make accurate and informed decisions reducing waste and increasing profitability.
  • Secure and robust platform to store sensitive information.
  • Scalable platform that accommodates future operational requirements, market demands, and growing inventory complexities, setting the stage for growth.

Products Used

  • Google Cloud Storage Bucket
  • Cloud Function
  • Big Query
  • Vertex AI
  • Google Sheets
  • App Engine
  • Looker Studio