Supply & Demand Forecasting

About

Manual supply and demand forecasting are still in vogue despite so many advancements in the information economy. Accurate forecasting is not achievable with manually handcrafted spreadsheets because of the intense diversity of data, variables, and inconsistencies. AI planning renders forecasting free of oversights. Machine learning-driven models anticipate supply and demand with harmonization, thereby positively influencing the decision-making process.

Challenges Faced by the Customers

Inconsistency/Seasonality

Retailers are always under the knife of inconsistency and diversified seasonality throughout the year. Dynamically, situations are so unstable that forecasting must be flexibly pinpointed and sensitively accurate each day. These are the preliminary requirements for survival in the retail market. AI-focused forecasting considers factors like product mix, demographics, competitor analysis, lead time, etc., to chalk out a foolproof prediction structure.

Pricing

Pricing strategies influence the operational model of any industry. With value-driven pricing models, the demand chain can be impacted over time-time leading to lower demand at a given time. Higher forecasts in such an instance can be misleading, leading to exorbitant inventory management costs. AI incorporates pricing strategies with forecasting to better illustrate actual scenarios.

Inventory excess

A manually managed supply and demand forecasting model always encounters astronomical variations in inventory. The shelf lives of in-stock items are always at stake. Once consumables/disposables pass the prescribed shelf life, the operational budget is severely distorted. AI-driven applications manifest the customer’s need to predict the possible supply/demand provisions and give a solid base for informed decision-making.

False pattern recognition

Humans always look for patterns in scenarios where no patterns exist. This falsely identified pattern can lead to dead ends. Manual forecasting is always prone to falsely identifying ways that don’t even live. AI involvement in forecasting is, therefore, the only solution.

In a nutshell:

An interactive, self-expanding, self-scalable AI-enabled supply and demand forecasting provide enhanced visibility, diminished inventory expenses, higher revenue, and streamlined workflow.