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Rethinking S&OP with Generative AI: A Deep Dive with Planning in a Box

August 10, 2023 | Premangsu Bhattacharya

Blog / Rethinking S&OP with Generative AI: A Deep Dive with Planning in a Box

Sales and Operations Planning (S&OP), in an ideal world, is a beautiful orchestra of sales forecasts, supply planning, financial goals, and inventory management. However, in the real world, especially for startups and SMBs, this orchestra can quickly turn into a chaotic cacophony. That’s where Planning in a Box steps in, using the power of Generative AI to harmonize your S&OP process.

So, how exactly does ‘Planning in a Box’ make this happen?

By integrating with your existing systems, ‘Planning in a Box’ consolidates diverse data streams and employs the advanced analytics of Google Cloud’s AI. It then funnels these insights into SAP’s operations planning modules, creating a seamless, intuitive, and powerful S&OP process. This integration is the conduit through which the strength of Generative AI is brought to bear on your planning challenges.

In essence, ‘Planning in a Box’ serves as the conductor of your S&OP orchestra, ensuring that every component is perfectly in sync and performing optimally.

Let’s look at some real-world scenarios to understand how ‘Planning in a Box’ can harmonize your S&OP processes:

Learn why Gartner recommends Planning in a Box 

Supply Planning


A global company that manages multiple product lines across various regions has a supply chain with many warehouses supporting different sales channels. The company follows a hub-and-spoke model, and product demand is highly dependent on the region, season, and local market trends.

Main Concerns:

  • They need to plan their supply according to a hub-and-spoke model.
  • Accurately predicting what to store, where, and when is their main challenge.


  • Struggles to manage inventory across warehouses due to varying demand patterns and seasonality.
  • Lacks a system for hub/spoke or dependent demand scenario planning.
  • Difficulty in optimizing and classifying inventory according to demand, leading to overstocking or understocking situations.
  • Unable to generate accurate purchase order recommendations based on current inventory levels, supplier lead times, and predicted sales.

‘Planning in a Box’ Solution:

  • Ingests and learns from past sales data, warehouse data, and market trends using Generative AI.
  • It supports the hub-and-spoke model and handles dependent demand scenario planning, ensuring stock availability according to predicted demand.
  • The platform classifies inventory, prioritizing products with higher demand and maintaining optimal stock levels.
  • It generates precise purchase order recommendations based on real-time data, avoiding excess inventory and stockouts.

Learn more about this capability

Demand Planning


A fast-growing fashion company operating in a seasonal market with online and physical store sales channels often launches new product lines. The customer base varies across channels, each with different purchase habits. 

Main Concern:

  • Current demand planning is a mix of guesswork and simple quantitative models, unable to predict the effects of market trends, new product launches, or sales promotions.


  • Difficulty predicting high growth demand, especially during product launches and peak seasons.
  • Struggles to generate demand forecasts considering different sales channels and customer bases.
  • The current KPIs fall short in providing an accurate measure of the forecast’s performance.
  • Lacks a system for scenario planning to understand the implications of different business decisions on demand.

‘Planning in a Box’ Solution:

  • The Generative AI in ‘Planning in a Box’ ingests historical sales data, new product launch schedules, and current market trends, providing accurate forecasts.
  • It can generate demand forecasts at granular levels, considering the unique customer base and purchase habits of each sales channel.
  • It supports KPIs like FVA, WMAPE, and BIAS to provide an accurate assessment of the forecast’s performance.
  • The platform allows the creation of multiple ‘what-if’ scenarios, enabling the company to visualize the potential outcomes of different business decisions.

Learn more about this capability

Sales Analytics


A multinational retailer with sales channels in Retail, Sports, Military, Ecommerce, Business, and Industry segments wants to better understand its diverse customer base. The customer buying patterns, churn rates, and lifetime value (LTV) vary across channels, making sales analytics complex.

Main Concern:

  • The business seeks to understand its customer base across various channels better to cater to their needs.


  • Difficulty in calculating LTV due to varying customer purchase habits and churn rates.
  • Unable to predict churn accurately, particularly in the E-commerce channel where customer behavior is highly dynamic.

‘Planning in a Box’ Solution:

  • Centralizes and analyzes data from various sources, providing deep insights into customer segmentation, LTV, and churn.
  • The platform uses predictive modeling techniques to calculate LTV, considering factors like purchase frequency, average purchase value, and customer lifespan.
  • It uses machine learning algorithms to analyze patterns in customer behavior and predict churn, helping the retailer design effective retention strategies.

Learn more about this capability 

Marketing Analytics


An e-commerce platform sells a broad range of products from electronics to fashion to home goods. They serve a diverse customer base with varying tastes and preferences. Their marketing efforts span various channels including email, social media, influencer partnerships, and search engine advertising.

Main Concern:

  • The company struggles to measure the success of marketing strategies and allocate the budget efficiently due to a lack of data-driven insights.

Current Challenges:

  • Struggling to understand the effectiveness of marketing campaigns across different channels.
  • Difficulty in segmenting customers for targeted marketing campaigns.
  • Unable to predict customer lifetime value (LTV) and churn rate accurately.
  • Struggles with attributing sales to specific marketing initiatives.

How ‘Planning in a Box’ helps:

  • ‘Planning in a Box’ centralizes marketing data across channels and uses AI to analyze the impact of each marketing campaign, providing actionable insights to optimize future campaigns.
  • The platform segments customers based on their browsing behavior, purchase history, and feedback, enabling the e-commerce platform to target their marketing efforts effectively.
  • Using predictive modeling, ‘Planning in a Box’ calculates LTV, taking into account factors like purchase frequency, average purchase value, and expected customer lifespan.
  • The platform also analyzes patterns in customer behavior to predict churn, helping the company devise effective customer retention strategies.
  • By analyzing sales data and marketing initiatives together, ‘Planning in a Box’ assists in attributing sales to specific marketing campaigns, providing a clearer picture of marketing ROI.

Learn more about this capability 

No Data Foundation, No Generative AI 

While Generative AI is revolutionary, it is crucial to remember that its power hinges heavily on a strong data foundation. Generative AI models learn and generate insights from data. Hence, without high-quality, relevant data feeding into these models, the outputs won’t be reliable or valuable. 

‘Planning in a Box’ ensures a solid, reliable data foundation. It uses Google Cloud Cortex to auto-clean and standardize data, continually checks data quality and relevance, and provides fast time-to-value. How fast? With the data foundation already taken care of, you can expect to see tangible results in eight weeks or less.

Workshop: Your Quick Start to Value

If you’re looking for a rapid realization of value, we recommend a workshop. It’s a tactical approach, aligned with immediate strategic priorities. This workshop method allows for an interactive, hands-on experience where your team can gain practical knowledge of implementing and using ‘Planning in a Box.’ It’s a perfect opportunity to explore the potential of Generative AI in revolutionizing your S&OP processes, aligning with both your immediate needs and long-term strategic goals. Reach out to us to schedule a workshop today!


Premangsu B, is a digital marketer with a knack for crafting engaging B2B content. His writings are focused on data analytics, marketing, emerging tech, and cloud computing. Driven by his passion for storytelling, he consistently simplifies complex topics for his readers, creating narratives that resonate with diverse audiences.

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