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 pricing data lake, a tailored optimization engine, and a heavily customized frontend, this architecture remains in project mode, and not all Global Business Units (GBUs) are fully onboarded yet. 

Additionally, several customizations were implemented contrary to the CRM platform team's recommendations, which conflict with Syensqo's strategic objectives of simplification and standardization.

Recommendation

Recommendation highlights of Option A: Implement PriceFx as the price optimization engine, frontend, and analytical tool, and integrate it with S/4 HANA, including replacing Dataiku.

• 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 organization.
• Pricing Strategy: Prebuilt pricing algorithms, data sets, guidance, templates, and models can be readily utilized to match the GBU's requirements. Additionally, individual pricing models specific to the nature of each 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 using price and variance waterfalls, which provide visibility into the effects of pricing strategies and changes in financial performance. Additionally, tracking price deviations and assessing their commercial impact enables data-driven decisions to optimize pricing and maximize 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 price 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.
• 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

Current pricing architecture is designed based on the following building blocks:

1. The Transparency tool, which consists of dashboards providing analysis of historical sales, volume, price, and margin evolution, as well as forward-looking cost and margin projections.
2. Price Optimization, a technique that maximizes profitability using mathematical optimization or AI machine learning models to optimize price targets and floors.
3. The Pricing Campaign tool, a module where frontline employees can view pricing recommendations from Pricing Managers, review and commit price guidance, and create pre-populated quotes.
4. The ONE Quote tool, which supports price setting and delivering the price to customers.

 This architecture presents several challenges, including the following:

• Integration Challenges: The current architecture uses multiple connections to diverse systems, including numerous non-standard XLS files (e.g., 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 result in significant technical debt, making maintenance and future enhancements increasingly challenging and costly.
• Limitations of homegrown optimization: The machine learning approach, which employs multiple independent models for each product family, requires significant resources and maintenance. However, Complex machine learning models are difficult to interpret, making it challenging to understand the relationships between price levers and pricing decisions. Furthermore, relying on median price for recommendations fails to capture nuanced market dynamics. The approach is also vulnerable to data quality issues, model performance variability, and potential biases in SHAP value calculations.
• Lack of Pricing Visibility: There is a lack of transparency beyond the net price at the transactional level, with key price components missing, such as transportation costs. Additionally, discounts and rebates are not readily available, making it challenging to understand the true breakdown of the final price. This opacity hinders a clear understanding of the pricing strategy and can lead to potential errors, compliance issues, and missed opportunities for optimization across GBUs.
• 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 (e.g., Product Taxonomy, Variable Costs, Freight, Duties, Production rates etc.,) require manual validation which is resource-intensive and prone to human error.
• Pricing Updates: There is a delay between the release of a quotation and the implementation of new prices for pending orders. This delay can lead to revenue leakage, pricing inconsistencies, and customer dissatisfaction.
• 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 throughout the sales cycle.

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.
• Recommended approach is agnostic to the CRM solution implementation.

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 framework is essential for achieving agreement among Global Business Units (GBUs) on adopting standard solutions offered by the PriceFx cloud provider.

Impacts

• Pricing is a crucial commercial tool that converts product value into sales revenue, thereby driving business profitability.
• System and 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 or contract is required before creating a sales order to ensure that accurate prices are reflected in the transactional processing. The latest prices in the quotation or contract are derived from the optimization engine's recommendation.

Further assessments will be done in detailed design phase.

Options considered

Option A: Implement PriceFx as the Price Optimization Engine, Frontend & Analytical tool and integrate it with S/4 HANA including as a replacement of Dataiku. 

Objective: This solution leverages PriceFx as a price optimization engine, frontend, and analytical tool to manage the end-to-end sales pricing process, integrating with S/4HANA. This approach aims to enhance pricing accuracy, efficiency, and scalability, while harnessing S/4HANA's capabilities to streamline pricing processes and inform business decision-making.

Key Advantages:

• 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.
• Automating pricing calculations and eliminating manual errors, helps to reduce costs and optimize pricing operations.
• Enables real-time collaboration among price teams and stakeholders, streamlining pricing decisions and ensuring alignment across the organization.
• Pricing conditions will be available in S4 HANA and all the peripheral systems will have to be source the pricing information from S4 HANA
• 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 across diverse Global Business Units (GBUs).
• Complex data migration and system integration is expected as many price drivers are maintained in custom objects and XLS sheets.

Option B: Implement PriceFx as the Price Optimization Engine, Frontend & Analytical tool and integrate it with CRM including as a replacement of Dataiku. 

Objective: The objective is to replace Dataiku with PriceFx as the Price Optimization Engine and Frontend tool, integrating it with CRM to create a seamless sales and pricing experience. This approach aims improve sales performance and increase revenue by leveraging PriceFx's advanced pricing capabilities and customer insights.

Key Advantages:

• Aligns with the existing architecture of having pricing in CRM and available for Quotation management for customer negotiations.

Key Challenges:

• Pricing conditions will be integrated with CRM and only when the Quotations are created and released, the pricing will be available in the S4 HANA

Option C: Refine and streamline existing Pricing Optimization and Frontend tool and integrate them with S/4 HANA

Objective: The objective is to refine and streamline existing Pricing Optimization and Frontend tools, integrating them with S/4HANA to develop and optimize pricing processess and improve system efficiency. This approach aims to maximize the value of existing investments and enhance pricing agility while leveraging S/4HANA's capabilities to support business growth.

Key Advantages:

• Aligns with the existing architecture of CRM and prices available in CRM Quotation management for customer negotiations.
• Avoids the high costs and disruptions associated with major system changes or redesigning efforts.
• Maintains continuity by keeping existing processes and systems in place, minimizing the learning curve and operational disruptions.
• Allows for focused resource allocation on necessary S/4 HANA adaptations rather than widespread Pricing process changes.

Key Challenges:

• Continued management existing pricing systems, processes and integration, leading to ongoing operational complexity and potential inefficiencies.
• Increased technical debt over time as the homegrown optimization solution needs constant updates tuning to internal and external impacts.
• Limited ability to standardize and optimize processes across GBUs, which may lead to inefficiencies and hinder collaboration.
• No alignment with ERP Rebuild program objectives to standardize & simplify the IT ecosystem, potentially leading to strategic misalignment and missed opportunities for synergies.

Evaluation

	Option A: Implement PriceFx as the Price Optimization Engine, Frontend & Analytical tool and integrate it with S/4 HANA including as a replacement of Dataiku.	Option B: Implement PriceFx as the Price Optimization Engine, Frontend & Analytical tool and integrate it with CRM including as a replacement of Dataiku.	Option C: Refine and streamline existing Pricing Optimization and Frontend tool and integrate them with S/4 HANA
Alignment with Simplification principle	Simplifies pricing architecture, removes bespoke developments and streamlines integration reducing complexity and enhance collaboration.	Simplifies the pricing architecture, removes bespoke developments and streamlines integration.	Retaining current complex architecture leads to missed opportunities and pricing efficiencies.
Alignment with Standardization Principle	Apply out-of-the-box strategies as much as possible however with customization to fit to GBU's needs.	Apply out-of-the-box strategies as much as possible however with customization option to fit to GBU's needs.	Retaining bespoke developments for both backend and frontend pricing tools prevents standardization.
Integration Complexities	Leverage out of the box APIs to integrate with S/4 HANA & CRM.	Leverage out of the box APIs to integrate with S/4 HANA & CRM. The pricing details will be available in S4 HANA only until CRM Quotations are created and released.	Redirecting the integration touchpoints to S/4HANA without optimizing existing architecture will increase complexity and overall TCO.
Single source of truth	Provides single data set for both Price Setting and Analytics processes.	Provides single data set for both Price Setting and Analytics processes	Continuing the existing architecture with fragmented data and XLS inputs will create data inconsistencies and
Collaboration	Enables streamlined collaboration between CEM, GBU and CSR teams, improving efficiency in managing price setting and execution process.	Enables streamlined collaboration between CEM, GBU and CSR teams, improving efficiency in managing price setting and execution process.
Reporting tailored to GBU requirements	Out of the box reports and interactive dashboards to provide In-depth analysis for Global and GBU level filters to drive targeted actions	Out of the box reports and interactive dashboards to provide In-depth analysis for Global and GBU level filters to drive targeted actions	Separate reporting tool increases overhead of data replications increases the risk of data fragmentation.
User Adoption and Experience & Change Management	Requires significant change management, impacting adoption.	Requires significant change management, impacting adoption.	Tailored experiences for each business unit with minimal change management required.
Testing Efforts	Significant testing to be considered to fine tune the algorithms to fit to the GBU specific pricing strategies.	Significant testing to be considered to fine tune the algorithms to fit to the GBU specific pricing strategies.

See also

Change log

Workflow history