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Real-Time Inventory Decisions Intelligence With Planning in a Box Platform enabled with Generative AI and Google Cortex

August 28, 2023 | Manju Devadas

Blog / Real-Time Inventory Decisions Intelligence With Planning in a Box Platform enabled with Generative AI and Google Cortex

Your Answer to Inventory Overstock or Shortage Problems Impacting Customer Experiences

Key Takeaways: 

  • Redefining Inventory Management: Inventory management isn’t just a logistical challenge; it’s fundamentally a data optimization challenge.
  • The Core Data Problem: Stockouts and excess inventory are symptoms of deeper data-related issues such as scattered data sources, lagging data, and misinterpreted data.
  • The Power of Generative AI: Generative AI, as showcased by Planning in a Box, allows supply chain teams to transition from static historical data to dynamic, real-time insights.
  • Data Integration with Major Platforms: Planning in a Box integrates with platforms like SAP, Oracle EBS, Salesforce, and AdTech, ensuring a comprehensive data pool along with 250+ public datasets.
  • Real-time Inventory Management: The decision intelligence platform aids in accurately sensing the market, planning dynamically, positioning for success, and ensuring consumer delight.
  • Business Outcomes in 4 Weeks: Businesses can derive actionable insights in just 4 weeks, making data-driven decisions with confidence.
  • Workshop Invite: Businesses interested in harnessing this technology are invited to join a hands-on workshop to explore its potential in-depth. Estimate your potential inventory carrying cost and other savings when you get the solution right.

Inventory management, for decades, has been perceived primarily as a logistical challenge. The conventional understanding centers around warehouses, stock levels, and supply chains. To truly grapple with inventory challenges, businesses need to reframe their perspective: see inventory management not just as a logistical task but as a data optimization and decisions optimization challenge. 

The Invisible Data Problem in Inventory

When we talk about stockouts or excess inventory, we’re essentially discussing symptoms of a deeper ailment: a data problem and, more importantly, poor decision-making. Here’s why:

Scattered Data Sources: For a comprehensive grasp on inventory, businesses need a consolidated view of various data sources – from sales and returns to supplier lead times and market trends. However, these data points often reside in isolated silos, leading to partial insights and, consequently, misinformed decisions. Disconnected data results in less informed decisions, as incomplete information often requires human interpretation to bridge the gaps.

Lagging Data: The volatile nature of the market means that data from even a month ago can be outdated. Relying on such stale insights can lead to discrepancies between actual market demand and stocked inventory. Delayed decisions cost companies millions of dollars in lost revenue. 

Misinterpreted Data: Having access to heaps of data is futile if there’s no mechanism to interpret it correctly. Misreading market signals can lead to overestimations or underestimations of product demand. Wrong data could lead to wrong decisions.

Tackling Inventory with a Data-first Approach

Let’s map the intricacies of decision-making:

Sales Projections: While sales teams might have a pulse on market trends, their forecasts reside in a CRM system, isolated from procurement or logistics data.

Procurement Details: These crucial bits – from supplier lead times to shipment updates – are tucked away in another system, often inaccessible to sales or marketing teams.

Marketing Campaigns: Information about upcoming promotions or events? They’re in marketing tools, again separate from sales and procurement data.

Now, consider an inventory manager trying to answer: “Given our current sales projections, supplier timelines, and planned marketing activities, what’s our optimal stock level for next month?” Extracting an answer becomes complex. 

The manager would need to jump between systems, manually integrating data to get an overview. And it’s not just cumbersome; it’s error-prone.

Yet, businesses were largely sidestepping this intricacy. The cost of not doing so? Inventory carrying costs spiraling into millions every year, not to mention missed revenue opportunities from stock-outs.

Not knowing where your inventory is, how much inventory you actually have (as opposed to what the system indicates), or how much is in motion are basic insights you need to control.

Generative AI Makes The Complex Simple 

Generative AI is about leveraging your data to provide insights, offer advice, and facilitate quicker, better decision-making, or even autonomously make decisions on your behalf.

With Generative AI, supply chain teams have an open canvas. They are no longer limited to historical data and static forecasts. Instead, they’re pondering questions like, “What if I could ask my data, ‘Given the current sales trends and the upcoming marketing push, where will I likely experience stock-outs in the next two weeks?’ 

When planners contemplate, they frame their thoughts in “natural language,” posing simple questions internally and seeking straightforward answers.

This isn’t just theoretical. Pluto7, with its decisions intelligence platform Planning in a Box, has been a forerunner in addressing supply chain predicaments for businesses for over a decade. Traditionally, it’s enabled businesses to run analytics on consolidated datasets. With Generative AI, its capability has amplified manifold. 

Planning in a Box seamlessly integrates data from ERP and CRMs like SAP, Oracle EBS, Salesforce, and AdTech, using the Google Cloud Cortex Framework to offer a unified data pool. With the power of Generative AI, users can interactively query this consolidated data, obtaining instantaneous insights without the need for coding.

With Planning in a Box, businesses no longer hope—they know. They can ask complex queries like “How will a port strike in Baltimore affect my demand in real-time?” and receive precise answers, all without delving into intricate SQL queries.

Why Planning in a Box is the Answer to Your Inventory Challenges

Planning in a Box is designed from the ground up to tackle the nuances of inventory management. Let’s explore how it addresses and elevates each core area:

1.  Sensing the Market Accurately:

  • Capturing Real-time Market Signals: Consider a sudden surge in demand for a particular shoe design on social media. Planning in a Box immediately spots this trend, alerting businesses to prepare for a potential increase in sales. The key is to align external signals into your datasets and organize them to be ready to answer the questions you might ask with Gen AI.
  • Understanding Consumer Preferences: Imagine a shift where consumers are now leaning towards sustainable packaging. The platform identifies these preferences early on, enabling businesses to adapt and meet these expectations.  Your customer experience is scattered across many data sets and data models in your enterprise and, more importantly, spread outside your boundaries.
  • Predicting Future Trends: For example, it can foresee a rising interest in winter apparel as colder months approach, based on historical data and current market patterns.

2.  Planning Dynamically:

  • Integrated Data Analysis: Instead of separately examining logistics and sales data, Planning in a Box combines them. If there’s a delay in shipment and a simultaneous spike in sales, it offers immediate insights for businesses to take preventive measures.
  • Adjusting on the Fly: A sudden weather event might affect crop yields. The platform dynamically revises forecasts, ensuring businesses stock up or slow down inventory accumulation based on these real-world changes.

Think real-time. If your world does not move in batch mode, then why should your system be doing it?

Generative AI in Supply Chain Use Cases

3. Positioning for Success:

  • Optimizing Inventory Levels: If a product is selling fast in New York but slow in Texas, the platform suggests redistributing stock, ensuring no location faces a stockout or excess.
  • Efficiency in Movement: It analyzes routes, lead times, and storage to suggest the most cost-effective way to move goods. For instance, if a warehouse in Ohio is overstocked, but one in Nevada is understocked, it recommends the most efficient transfer method.
  • Anticipating Needs: By analyzing past purchase patterns, it can predict when a regular customer might run out of a product and send timely reminders or offers.
  • Enhancing Availability: A product launch can be better managed with insights on where demand might be highest, ensuring eager consumers aren’t met with “Out of Stock” signs.

The Result is Improved Customer Experience (CX):
Ensuring Timely Deliveries and Maximized Marketing Efficiency for Consumer Delight

Explore the Inventory Positioning Solution

Beyond providing insights, Planning in a Box ensures they’re actionable. Business leaders can seamlessly move from data interpretation to informed decisions, all within a single platform. Thanks to its self-learning capabilities, as business processes and market dynamics evolve, so does Planning in a Box, ensuring it remains perpetually relevant. 

Levi’s, AB InBev, Ulta Beauty: Trusting ‘Planning in a Box‘ for Solution-Based Ideation, Data Platforms, and Gen AI Journeys

Endorsements are a testament to a solution’s efficacy, and when global stalwarts vouch for a platform, it’s worth taking note. 

💡 Planning in a Box has gained the trust of not just industry leaders such as Levi’s, AB InBev, and Ulta Beauty but also won accolades from Gartner, a name synonymous with in-depth industry analysis, championing Planning in a Box as a leading decision intelligence platform.

Here’s a snapshot of what sets Planning in a Box apart:

  • Endorsement & Partnerships: Planning in a Box is backed by tech behemoths like Google Cloud and SAP, hosting solutions on both GCP and SAP BTP
  • Flexible Data Platform: It’s not just about having data; it’s about wielding it effectively. Planning in a Box offers unrivaled flexibility, allowing supply chain teams to customize data models, slice and dice data, and more. We integrate with Oracle EBS as well.
  • Glassbox Methodology: With its Glassbox approach, users get a clear view of how results are derived, ensuring transparency and trustworthiness in every insight.
  • Deep-Rooted Association with Google: A robust engineering relationship with Google ensures that users are always at the cutting edge of tech innovations.
  • Time to Value: It’s fast. Really fast. Deployment takes a mere 2 hours, and actionable insights can be generated in as little as 4 weeks. For instance:
    • AB InBev took a solution from Newark and scaled it across 126 countries.
    • Levi’s redefined their forecasting model for packing jeans, optimizing carton usage.
    • Ulta Beauty revolutionized their approach to predicting supplier performance, ensuring consistent product availability.
    • Lixil managed to substantially curtail manufacturing waste, saving millions in the process.

A Modern Solution for Age-Old Inventory Problems

Every inventory challenge, at its core, is a data puzzle waiting to be solved. Planning in a Box addresses this head-on. By creating a solid data foundation with the Google Cloud Cortex Framework, it seamlessly transforms vast data into actionable insights. 

Within just 4 weeks, businesses move from grappling with uncertainties to confidently making data-driven decisions.

This isn’t about merely using technology; it’s about harnessing the right technology to drive tangible change in supply chain management. The outcomes speak for themselves.

Want to experience this transformation firsthand? Join our upcoming workshop and dive deep into the capabilities of Planning in a Box. Discover how it’s rewriting the rules of inventory management.

Join our workshop


Manju Devadas is the Founder and CEO of Pluto7, bringing 20+ years of experience in predictive analytics for Supply Chain, Retail and Manufacturing. With expertise in AI, Deep Learning, and Machine Learning, he has been instrumental in improving efficiency and strategic growth across industries.

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