August 1, 2025

Win the clock: Accelerating quote-to-cash cycles for manufacturers

Win the clock: Accelerating quote-to-cash cycles for manufacturers

Manufacturing companies selling complex, configurable products often experience lengthy quote-to-order cycles. The quoting process involves multiple teams—sales, engineering, finance, logistics—each needing specific inputs in order to give their approval. Delays happen when approvers lack timely visibility into key details, causing friction that slows the business down.

In highly competitive markets, speed and accuracy in quoting can define winners. Each extra day spent waiting for internal approvals creates opportunities for competitors to step in. By shortening the time required for final quote approval, companies directly improve:

  • Deal conversion rates: Faster quotes improve the likelihood of securing deals.
  • Customer experience: Quick responses enhance customer trust and satisfaction.
  • Competitive differentiation: Businesses known for fast, accurate quoting set themselves apart, winning more business more consistently.

And perhaps most importantly, accelerating approvals has a direct financial impact, shortening the time between initiating the deal, quoting and receiving payment, thereby improving Days Sales Outstanding and the revenue cycle.

Where quotes stall: Approval bottlenecks

Every quote for complex manufactured products typically needs approvals across multiple dimensions, for example:

  • Pricing and margins: Ensuring profitability thresholds are maintained
  • Engineering feasibility: Confirming configurations can actually be built
  • Freight and logistics: Validating shipping costs and delivery timelines

Each approver needs specific information like product configurations, engineering workloads, customer expectations, pricing structures, and delivery constraints. While quoting software, like SAP CPQ and Infor CPQ, excels in managing and configuring quotes, the critical information required for timely and accurate approvals often exists across multiple systems. Account and opportunity details are typically housed in CRMs while crucial financial information like pricing and costs resides in ERP systems. Engineering teams rely on separate Product Lifecycle Management (PLM) systems to access bills-of-materials (BOM) and feasibility assessments. Similarly, logistics teams depend on other applications to obtain accurate freight costs and shipment timelines.

This fragmented ecosystem, if not managed tightly, can lead to time-consuming cycles that ultimately result in inconsistent and error-prone sales quotes.

The solution to reducing approval cycles

To accelerate quote approvals, business process leaders need a transformation strategy that automates the consolidation of information from the various business systems into a cohesive and actionable view. By aggregating key data elements like pricing and product configurations from CPQ software, customer account and opportunity insights from the CRM, financial details like pricing and margins from ERP systems, engineering feasibility and BOM data from PLM systems, logistics data, and workload management details, manufacturers gain a unified model that empowers informed, rapid quoting and quoting approvals processes.

This unified approach enables role-specific analytics tailored to the unique needs of each approver, ensuring they receive critical insights exactly when needed. Additionally, operational alerts can be triggered in real-time to proactively address potential bottlenecks before they impact the quoting timeline.

Cross-functional impact and how each team wins

When CPQ, CRM, and enterprise data are unified effectively, stakeholders across the business benefit.

Sales Teams: Gain real-time visibility into quote statuses, predicted approval times, and immediate alerts when SLA breaches occur, enabling proactive customer communication.

Engineering Teams: Quickly assess configuration feasibility, workloads, and required BOM adjustments, reducing rework and delays.

Finance/Pricing Teams: Automate margin checks, analyze discounting trends, and maintain a clear audit trail of decisions, ensuring financial discipline.

Logistics Teams: Access early freight-cost and delivery-time visibility to minimize late adjustments and ensure accurate commitments.

Operations/Leadership: Use detailed approval funnel analytics and bottleneck heatmaps to identify process improvements and drive accountability.

Real-world impact: How one manufacturer accelerated its quote-to-cash cycle

A mid-sized industrial equipment manufacturer was losing deals to faster competitors. Their complex product configurations required approvals from engineering, pricing, and logistics teams, but critical information was scattered across SAP S/4AHANA, SAP CPQ, and SAP Cloud for Customer (C4C). Quote approvals that should have taken 2-3 days were stretching to 7-10 days, with sales reps spending hours chasing down information and approvers making decisions with incomplete context.

The business process and technology teams undertook an integration transformation project with Precog to bring disparate information parts into SAP Datasphere for unified analytics. Now, when a quote is generated in SAP CPQ, approvers automatically receive enriched context including real-time margin calculations, customer payment and relationship data, current engineering workload and BOM feasibility, and accurate freight costs and delivery timelines.

As a result, the company:

  • Improved quote approval speed and accuracy
  • Shifted from sequential to parallel approval processes
  • Significantly improved SLA adherence
  • Notably reduced days to quote approval and higher deal win rates

Take action to accelerate quote-to-cash

Faster, smarter approvals yield substantial business outcomes: winning more deals, delighting customers, and improving cash flow. Are you a manufacturer ready to accelerate your quoting process?

Contact us to schedule a demo and see how Precog takes the complexity out of integration so you can focus on turning data into analytics and operational outcomes.

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