For E2open's Demand Sensing solution, should we focus on enhancing forecast accuracy or reducing the computational complexity to improve processing speed?

Introduction

The trade-off we're examining for E2open's Demand Sensing solution is whether to focus on enhancing forecast accuracy or reducing computational complexity to improve processing speed. This decision is crucial for the product's future direction and its ability to meet customer needs effectively. I'll analyze this trade-off by considering various factors including business context, user impact, technical feasibility, and strategic alignment.

Step 1

Clarifying Questions (3 minutes)

• Based on the current market trends, I'm thinking forecast accuracy might be a key differentiator. Could you share how our accuracy compares to competitors and what our customers are demanding?

Why it matters: Helps prioritize between accuracy and speed based on market positioning Expected answer: We're slightly behind in accuracy but competitive in speed Impact on approach: Would lean towards improving accuracy if it's a significant gap

• Considering our revenue model, I assume we charge based on the value delivered. Is this correct, and how does improved accuracy or speed translate to customer value?

Why it matters: Aligns solution with our business model and customer value proposition Expected answer: Accuracy directly impacts customer ROI, speed is a secondary benefit Impact on approach: Would prioritize accuracy if it has a stronger link to revenue

• Looking at user behavior, I'm curious about the typical use case. Do most customers run demand sensing daily, weekly, or in real-time?

Why it matters: Determines the importance of processing speed in actual usage Expected answer: Most run weekly, with some moving towards daily Impact on approach: If daily is becoming more common, speed improvements become more critical

• From a technical perspective, I'm wondering about the current bottlenecks. Is the computational complexity primarily in data processing or in the algorithmic calculations?

Why it matters: Identifies where improvements in speed would have the most impact Expected answer: Algorithmic calculations are the main bottleneck Impact on approach: Would focus on algorithmic optimizations rather than data processing improvements

• Regarding our development resources, how are we positioned to tackle either of these challenges? Do we have the right expertise for both accuracy and speed improvements?

Why it matters: Ensures we can execute on the chosen direction effectively Expected answer: Strong data science team for accuracy, need to bolster engineering for speed Impact on approach: Might lean towards accuracy if that's where our current strength lies