Issue

Syensqo's current pricing architecture, designed to provide a sustainable pricing management system, is a commercial initiative from the CEM team that resulted in a disparate tooling landscape. Although the tooling combines a backend data lake, a tailored optimization engine, and a heavily customized front-end, this is still architecture is in project mode and not all GBUs are fully onboarded. Additionally, several customizations were implemented contrary to the CRM platform team's recommendations, which conflict with Syensqo's strategic objectives of simplification and standardization.

This architecture presents several challenges, including the following:

• Lack of Pricing Visibility: There is a lack of transparency beyond the net price on a transactional level, with key price components missing, such as transportation costs. Additionally, discounts and rebates are not readily available, making it difficult to understand the true breakdown of the final price. This opacity prevents a clear understanding of the pricing strategy and can lead to errors, compliance issues, and missed opportunities for optimization across GBU’s.
• Integration Challenges: The current architecture uses multiple connections to diverse systems, including numerous non-standard XLS files (i.e., production rates, freight costs etc.,), to meet data requirements. Furthermore, it utilizes outdated technologies, such as OLEDB, which hinder efficient data management. The pricing optimization output is manually uploaded to custom applications(Pricing Campaign/WebApp) for price review and approvals, this process lacks seamless integration between the backend, frontend pricing tools and operational systems, significantly delaying the price update process and hindering operational excellence.
• Customization Complexity: Modules designed for both backend and frontend tools are bespoke applications, including Pricing Campaigns, Regional Market Policies, Freight & Duty Guidance, and OneQuote. These customized applications lead to significant technical debt, making maintenance and future enhancements increasingly challenging and costly.
• Limitations of homegrown optimization: The machine learning approach, which manages multiple independent models for each product family, requires significant resources and maintenance. Complex machine learning models are difficult to interpret, making it challenging to understand the relationships between price levers and pricing decisions. Additionally, relying on median price for recommendations does not capture nuanced market dynamics. The approach is also vulnerable to data quality issues, model performance variability, and potential biases in SHAP value calculations.
• Data Quality Issues: Ensuring data accuracy, completeness, and consistency is a challenge because the architecture relies on multiple sources. Furthermore, its reliance on XLS for specific data sets ie., Product Taxonomy, Variable Costs, Freight, Duties, Production rates etc., require manual validation which is resource intensive and prone to human error.
• Misalignment with Strategic Objectives: The current pricing process architecture obstructs Syensqo's ability to achieve its strategic objectives of adopting a simple and standardized approach. The reliance on a homegrown solution, combined with bespoke enhancements and custom integrations, hinders data-driven decision making and impedes revenue growth, profitability, and operational efficiency across the end-to-end sales cycle.

Recommendation

Recommendation highlights of Option A: Replace Dataiku with PriceFx as Price Optimization Engine & Frontend tool and integrate it with S/4 HANA

• Simplify Pricing Tools, Systems and Infrastructure: Implementing an innovative cloud-based price optimization and management solution to support the end-to-end pricing process, including price setting, publishing, approvals, and execution, will drastically simplify the pricing infrastructure and bring in operational efficiency and better collaboration across.
• Pricing Strategy: Prebuilt pricing algorithms, data sets, guidance, templates, and models can be readily utilized to match GBU's requirements. Additionally, individual pricing models specific to the nature of the GBU's business will be implemented.
  a.    Predictive Commodity Pricing using market data, feedstock prices, industry inventory levels, and macroeconomic data to predict price changes.
  b.    Dynamic Transactional Pricing using a business-specific pricing algorithm by selecting and combining the right analytic tools, rules, and guardrails to frequently update and set precise target prices.
  c.    Value Based Pricing using regular segmentation of customer-product combinations, insights on key buying factors, perceived value and market conditions to set prices.
• Pricing Transparency and Execution: The bottom-line impact can be monitored through the use of price and variance waterfalls, which provide visibility into the effects of pricing strategies and financial performance changes. Additionally, tracking price deviations and assessing their commercial impact enables data-driven decisions to optimize pricing and profitability.
• Improved Collaboration: A unified platform facilitates seamless collaboration among sales, pricing administrators, and customer service teams, streamlining communication and minimizing errors throughout the entire pricing process, from setting to approval and tracking the status of commercial pricing actions
• Simplified Integration: Standard APIs facilitate seamless integration, simplifying setup and maintenance, while enabling real-time data synchronization for up-to-date and consistent information across both systems. Additionally, robust security measures, including encryption and authentication, ensure the protection of sensitive data.
• Data integrity: Protected by international data legislations such as the EU’s GDPR and the US’s Federal Trade Commission Act, Health Insurance Portability and Accountability Act, and Electronic Communications Privacy Act.
• Common Reporting: Unlock data-driven insights with a unified reporting and analytics platform, providing transparency into price, margin, and volume trends, price dispersion analysis, and margin leakage breakdowns. This centralized hub enables informed decision-making and allows for refining pricing strategies, reducing costs, and boosting revenue.

Background & Context

Assumptions

• Alternative solutions in this space, such as PROS, Vendavo, and Zilliant, have already been assessed in previous evaluations and therefore will not be re-evaluated in this current assessment.
• SAP S/4HANA will serve as the ERP (Enterprise Resource Planning) application for managing and executing customer records, sales contracts, sales orders, logistics, warehousing, transportation, billing, and rebates.

Constraints

• Gaining buy-in from stakeholders who may be attached to existing customizations and interfaces.
• Securing proper sponsorship and executive support to drive transformational change, ensure resource allocation, and champion the initiative across the organization.
• A clear and strong Governance is key to achieve agreement (GBUs like Novecare who need more of a commodity approach who changes quotations on a quarterly basis) to use standard solutions offered by the PriceFx cloud provider.

Impacts

• Process streamlining will impact certain GBUs, requiring change management efforts to ensure a smooth transition.
• Ongoing projects

- Price Optimization is being introduced to Novecare

- Pricing Module Improvement: Expected in October 2024, this change will unify List Price and Recommended Price into a single field, reducing complexity.

Business Rules

• A quotation is required before creating a contract to ensure accurate recording and approval of price negotiations.

Further assessments will be done in detailed design phase.

Options considered

Option A: Replace Dataiku with PriceFx as Price Optimization Engine & Frontend tool and integrate it with S4 HANA

Objective: This solution represents a strategic initiative to replace the Dataiku AI tools with an innovative cloud based PriceFx pricing tool and integrate with S4 HANA for GBU’s.  It aims to align pricing process, foster collaboration, and achieve efficiencies across all units while maintaining data visibility and flexibility to support specific needs justified by a strong business case.

Key Advantages:
•    Enables real-time collaboration among price teams and stakeholders, streamlining pricing decisions and ensuring alignment across the organization.
•    Automating pricing calculations and eliminating manual errors, helps to reduce costs and optimize pricing operations.
•    Provide pre-defined mappings that outline the fields, formats, and transformations required to ensure data consistency and accuracy during the integration process.
•    Quickly respond to changing market conditions and customer needs, enabling faster time-to-market and increased competitiveness.
•    Advanced analytics and reporting capabilities, enabling data-driven pricing decisions and improved business outcomes.

Key Challenges:
•    Strategic change management and executive sponsorship are required to drive adoption and alignment.
•    High stakeholder involvement is needed to design and agree on new, unified processes across diverse Global Business Units (GBUs).
•    Complex data migration and system integration are expected due to the consolidation of multiple legacy systems into a single instance.
•    Managing security and privacy requirements across diverse global units may pose potential complexity.

Option B: 

Option C: 

Option D: 

Evaluation

	Option A	Option B	Option C	Option D
Criterion 1	Pro Con	Pro Pro	Pro Con	Pro Con
Criterion 2	Pro Con	Con	Pro Pro	Con Con
Criterion 3	Pro	Con	Con	Pro

See also

Change log

Workflow history