Resilient planning substantially impacts your company’s key CFO-level metrics including BoM cost, time-to-market, inventory holding costs, expedited freight costs, OTIF penalties, energy costs, labor costs, stockout events and missed revenue, emissions and sustainability.
Forecasting models alone have been failing to deliver true resilience, as they get thrown with every new shock that breaks historical patterns!
DeepVu's Solution: DeepVu's multi-agent AI decision models are trained on top of digital twins that simulate normal scenario, shock scenario-A, shock scenario-B etc that recommend actions to optimize for your business KPIs. The human planner selects the action most applicable to today’s scenario in that specific market/geo.
With traditional planning, humans make all recurring decisions, which is error prone, has long lead times, and the impact on KPIs is delayed or never evaluated.
DeepVu's Solution: Leverage multi-agent Generative AI decision models to make intelligent planning decisions that learn from historical human decisions, and improve upon them as they get smarter over time with usage and human feedback. The impact of each generated action on the business KPIs is imediately available.
Annual marketing oriented sustainability reports are neither granular nor an accurate account of a company’s supply chain emissions.
DeepVu's Solution: DeepVu takes sustainability as one of the business KPIs for which the AI decision models are optimizing. The weight of the KPI is per each customer’s requirement. Hence the impact of each decision on sustainability metrics is readily available in the logs and aggregated quarterly for full transparency and accuracy in compliance reports.
Traditional shallow forecasting, such as ARIMA and basic ML forecasting, fall short in leveraging hundreds of important external signals.\ p>
DeepVu's Solution: Enrich your internal forecasting models with a rich industry and global context by leverag\ ing ever-growing supply chain knowledge graphs that incorporate hundreds of external signals that capture shocks as they emerge.
Integrating a B2B enterprise platform into your existing work flow can be a time-consuming and challenging process.
DeepVu's Solution: Our platform is sold as SaaS subscription "à la carte" per use case, and seamlessly integrates with any legacy or ERP system using cloud APIs, or used via a beautiful dashboard to boost planner’s productivity and effectiveness.
Get insights and actionable shock resilient decisions in real-time. DeepVu analyzes data streams of the external signals as they arrive which enriches the decision models with current global context.
Deep Reinforcement Learning (DRL) is the most advanced, scalable, self-tuning type of Generative AI decision models. Handles complexities and scale that traditional machine-learning cannot.
A versatile resilient planning solution capable of handling any supply chain data-set and use-case including demand planning, order fulfillment, auto-replenishment, production planning, and freight.
No feature-engineering work or thousands of rule updates needed. DeepVu's models will scale up as your company scales to new products, SKUs, fulfillment centers, and markets.
Unlock invaluable insights by utilizing DeepVu's supply chain knowledge graphs (VuGraph),
which include hundreds of external signals with their predictive weights. These signals range
from macro and microeconomics to commodity prices, and global trade metrics.
By identifying the most impactful signals and integrating them into your model,
you can substantially improve the accuracy of your forecasting.
DeepVu's model forecasts demand per (retailer-id, SKU, month) in 5 countries with a forecast horizon of 1-3 months out, taking numerous external signalsRequest Case Study
DeepVu's model forecasts the probability of a stockout per (store/DC, SKU, week) with a forecast horizon of 2--6 weeks out.
DeepVu's model forecasts the weekly price of hot-rolled-coil steel in Shanghai market with forecast horizon of 2--12 weeks out with an accuracy of 91%--95% (with MAPE as the metric)Request Case Study
Eight supply chains are responsible for more than 51% of global emissions (source: World Economic Forum 2023)
Moreover, supply chain emissions are 11.4 times higher than operational emissions (source: CDP Report 2020).
Climate disruptions have become the new norm. All manufacturers are experiencing adverse effects as a result of
supply chain challenges exacerbated by ongoing shocks. Over the past decade, big companies started talking
about connected planning and integrated planning, but none of these approaches could deliver true resilience.
Now is the time for a revolutionary shift towards resilient autonomous sustainable supply chain planning, ensuring
long-term sustainability for both businesses and the planet.
Decision models to map customer orders to one or more DCs to optimize delivery date (OTIF), minimize shipping cost, splitting orders, and stockout events
Sales and operations planning for optimizing production and inventory levels, including forecasting shelf availability and preventing stock-outs per SKU, per DC/store per day
Decision models to allocate purchase orders to suppliers to optimize BoM cost, delivery date, and sustainability while de-risking production disruptions
Decision models to recommend scheduling actions to optimize for multiple KPIs including factory production capacity, energy usage, labor cost, and sustainability
AI assistant for logistics and freight optimization, including demand forecasting per lane, decision models to optimize OTIF, fuel consumption, labor allocation etc.