From "past experience" to "artificial intelligence–driven forecasting."
We support pricing strategies that incorporate cost inflation risks and maximize profits.
An AI-driven pricing service that visualizes costs and future risks to maximize profitability
Solution
The initial pricing of products and services is not simply a matter of setting a number; it is a critical management decision that directly affects a company’s future profitability and competitiveness. Ideally, prices should be determined through strategic decision-making that considers market trends, customer-perceived value, the competitive environment, profit plans, and future risks. In reality, however, prices are often set without a sufficient understanding of cost structures or without adequately reflecting future cost fluctuation risks. As a result, potential profits are missed, and repeated mispricing can weaken the long-term earnings base.
This challenge has intensified as cost structures become more unstable and complex due to volatile raw material prices, rising energy costs, exchange rate fluctuations, and an increasing number of product variations.
ABeam Consulting integrates data on costs, sales, and transaction terms and leverages artificial intelligence to visualize and quantify future cost fluctuation risks and their impact on profitability. We translate these insights into practical pricing strategies, decision rules, and operational processes, enabling executable pricing mechanisms. By combining advanced cost management with strategic pricing operations, we support sustainable profit generation and the strengthening of long-term earnings structures.
Background
In an era of uncertainty, what the manufacturing industry requires is a data-driven pricing strategy that does not rely on "past experience."
As geopolitical risks intensify and energy and raw material prices become increasingly volatile, the cost structures of manufacturing companies are growing more unstable. Traditional pricing approaches based on "past performance" and "experience" have long served as key decision-making foundations for maintaining competitiveness. However, continued reliance on these approaches makes it difficult to adequately reflect future cost inflation risks in pricing, which can lead to declining profit margins.
Against this backdrop, advances in digital transformation are enabling cost structures and market trends that were previously difficult to grasp to be captured accurately as data. What companies now require is an "offensive pricing strategy" that leverages artificial intelligence and data to incorporate future cost inflation risks into pricing and continuously optimize prices in response to changes in the external environment.
Challenges
A shift to strategic pricing decisions that take future risks and costs into account
Pitfalls
- [Limitations of forecasting] Insufficient visibility into future risks
Future fluctuation factors such as raw material prices and exchange rates are not being captured quantitatively, making it impossible to evaluate in advance the impact of prices on profits, which prolongs decision-making at the time of pricing. - [Limitations of accuracy] Personalization of cost calculation
Standard costs depend on the experience and tacit knowledge of individual personnel, and differences from actual costs and their causes are not sufficiently visualized, making it difficult to present justified decisions regarding the appropriateness of price revisions. - [Limitations of process] Absence of a profit-focused mechanism
Key performance indicators centered on sales and volume dominate, and the impact on profits is not incorporated into day-to-day business decisions, causing reviews of pricing aimed at improving profit margins to lag behind.
Approach
In order to make pricing decisions that take future risks, costs, and impacts on profits into account, it is essential first to visualize the current situation and quantitatively understand the effectiveness of pricing.
As a first step, we begin with the "visualization of impact" and connect it to steady profit improvement.
Step 01: Impact analysis phase
Through simulations using artificial intelligence, we quickly verify the effectiveness of pricing that incorporates future risks. We identify risks such as raw materials and exchange rates based on historical data and external factors, quantify the impact of future cost increases on profits, and clarify the impact on pricing strategies.
Step 02: Phased profit maximization phase
Based on the results of the impact analysis, we introduce measures in phases starting with high-impact areas. We assess the entire business process that supports pricing, and while evaluating return on investment and operational burden, we enhance cost accuracy and establish continuous price revision rules. By embedding these into actual operations, we establish a mechanism that enables continuous price review in response to environmental changes and strengthen the earnings structure.
Key features
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Predict future risks with AI and rapidly assess the appropriateness of pricing
Through AI‑driven model development, we significantly accelerate the entire cycle from objective and KPI definition to impact analysis. By quantitatively assessing how fluctuating factors such as raw material prices and exchange rates affect profitability, we can quickly assemble the insights required to support pricing decisions.
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Visualize costs and realize well-founded pricing decisions
By leveraging data accumulated in ERP and PLM systems, we deliver high‑accuracy cost visibility, from standard costs at the development stage through to actual costs after mass production. By clarifying the underlying profit structure, we enable pricing revisions to be evaluated and explained transparently, supporting well‑grounded decision‑making.
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A mechanism for continuously reviewing pricing, with profitability at its core
We establish rules for determining price revisions by continuously monitoring changes in the market environment and profit margins. By detecting anomalies and early signs of change at an early stage, we realize a mechanism that enables rapid decisions on price revisions and strategic reviews.
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