///Case Study

Gardner Denver Industries

A bespoke quotation system that simplified their customer quotation process, providing pricing accuracy and sales forecasting for the aftermarket service team within 4 months.

Overview

Platforms

  • Alava

Duration

  • 6 Months

Sector

  • Industrial

Project Goals

  • Increasing productivity of the sales team by automating the quotation process
  • Reduce human errors in data entry and calculations.
  • Track the status of each quotation throughout the entire lifecycle.
  • Revenue forecasting

Challenges

Gardner Denver’s products and aftermarket servicing are highly customisable. Each product offering is configurable, most have an attached servicing (maintenance) schedule that is dependent on the equipment's existing servicing history or age. Thus, the solution had to be scalable to accommodate the variabilities.

Results

75% increase in lead cycle tracking. Also, a reduction in quotation errors by 55%. This figure is expected to increase as the quotation system is rolled out across multiple departments Australia wide.

The quotation portal removed the need to maintain spreadsheets

allowed us to centralise quotations

The quotation process functioned independently from existing applications and there was no centralised repository or software to generate and track client quotations, resulting in quotations needing to be prepared and sent manually, creating significant delays. The scope of the project was to address these limitations.

1

Solution Architecture

Maytech conducted multiple onsite and offsite scoping sessions with Gardner Denver to understand the quotation process. The project team studied quotation inputs to identify formulas and data structures used to generate quotes. Because of the complexities within service cycles and processes for each product type, it was highly challenging to create a set of mathematical formulas to achieve the expected outcome.

We decided to build a product & pricing rules engine to overcome these complexities while making it easier for users to manage price updates and other relevant information.

2

Implementation

Once the formulas and rules around pricing structures were prototyped, our team did a number of simulations to validate the logics. This part required a high level of discipline as a fractional error could create a 6 figure variance in the final contract value.

Once we carried out our internal tests, we worked with the Gardner Denver sales team to simulate all test scenarios and provided a prototype for them to test

This project brought home the importance of planning. The upfront planning and workshops ensured that we de-risked the project. We spent significant time understanding key business logics before working on CRM related elements, reporting and contact management. We worked with GD management to transition users seamlessly from using Excel spreadsheets to a new system.

3

Working with Gardner Denver

The team at Gardner Denver and Maytech worked collaboratively, which resulted in a successful project launch. Our ability to understand, deconstruct and engineer solutions to complex business requirements was a key ingredient in that success. Management at Gardner Denver were hands on from the beginning and worked proactively to answer questions and remove any roadblocks and dependencies from their side.

Since the release of the platform in 2019, we have worked beyond the original requirements and successfully launched a number of feature releases that have helped Gardner Denver to further streamline their operations.

4