CPG

What do you get?

Obtain the key to drive efficiency, minimizing waste, and maximizing profits.

What are we going to do for you?

Unlock enhanced revenue management, adaptable supply chains, and foster positive interactions with customers and consumers with advanced AI systems.

Challenge

CPG companies have struggled with accurately predicting consumer demand for products, leading to issues like overstocking or stockouts. Overstocking ties up capital, increases storage costs, and leads to wastage due to expired products. Stockouts result in lost sales, dissatisfied customers, and damage to brand reputation..

Solution

OmniData's frameworks analyze various data sources to generate accurate demand forecasts. Machine learning algorithms identifies patterns and correlations in the data enabling companies to better anticipate fluctuations in demand and adjust their production and inventory levels accordingly. AI strategies optimizes supply chains, reduce costs, improve customer satisfaction, and ultimately enhances competitive advantage in the market.

Optimal Ordering Levels

Prescribed ordering levels and timing given demand forecasts, lead times and minimum ordering quantities.

Demand Forecasting

Forecasting to shape product life cycle strategies for demand or scope of sales over time.

Scenario Planner

Perform What-if analyses on safety stock levels and demand change inventory.

Business Problem

Despite advances in technology, CPG and Retailers are still faced with stock-outs, and the subsequent customer dissatisfaction. Often a result of understocking is the kneejerk reaction to order surplus quantities, which in turn leads to overstocking and constraints on liquidity.

A large contributor to this challenge is the inadequate forecasting of demand over time. The impact of factors like market forces, pricing and promotions, and seasonality and cyclicality needs to be visible.

Solution Approach

In response to the persistent challenges of stock-outs and overstocking plaguing the CPG and Retail sectors, AI-powered solutions offer a transformative approach. Leveraging advanced machine learning algorithms and predictive analytics, AI enables accurate demand forecasting by assimilating and analyzing vast datasets encompassing market dynamics, historical sales patterns, pricing and promotions, as well as seasonality and cyclicality trends.

AI facilitates optimal inventory management strategies, mitigating the risks associated with both understocking and overstocking. By dynamically adjusting inventory levels based on projected demand, AI-driven solutions not only ensure product availability to meet customer demand but also alleviate the strain on liquidity caused by excessive inventory. Furthermore, AI's adaptive capabilities enable proactive risk mitigation by identifying and addressing supply chain disruptions before they escalate into critical issues.

Value Impact

Studies have shown that between 20-43% of customers will go to another store to buy an item they are looking for when faced with a stock-out. Overall stores face a reduction of up to 50% of intended purchases in these scenarios.

OmniData's experience shows that between 10-20% reduction in working capital is possible with better inventory management strategies based on accurate demand forecasting and optimized inventory planning.

Technical Implementation: Data Sources

In building solutions for our clients our data scientists work with data sources across thousands of SKUs. These values along with pricing, promotion, market forces such as macroeconomic data, and seasonality factors such as public holidays, are fed into the modelling framework.

Technical Implementation: Solution Features

• Forecasts per product for the coming year on a daily level: These values were rolled up into weekly, monthly and quarterly values catering for both operational and financial reporting levels.
• Optimal inventory ordering points and levels: Using inputs for lead time scenarios, and safety stock levels OmniData provided optimal levels and ordering points for different products across brand ranges.
• What-if Scenaro planner: Having the availability to cycle through scenarios and plan for base cases but also best- and worst cases is good practice. These scenarios can be analyzed for all products in the range.

Conclusion

The results of the efforts meant our client was able to unlock value adding opportunities for reductions in working capital and reducing liquidity constraints. Overall the solution allowed the company to anticipate the demand levels in the near future, optimally plan for inventory ordering and drive operational and financial decison making.