Price Optimization for a Fortune 500 Silicon Wafer Manufacturing Company – Quantzig’s New Success Story
Analyzing the Role of Price Optimization in Silicon Wafer Manufacturing Industry Over the past few years, the growth of the silicon semiconductor industry has been majorly driven by the growing demand for embedded devices and smartphones combined with the rising application of IoT and cloud-based computing systems. To sustain a competitive edge in such a [...]
Analyzing the Role of Price Optimization in Silicon Wafer Manufacturing Industry
Over the past few years, the growth of the silicon semiconductor industry has been majorly driven by the growing demand for embedded devices and smartphones combined with the rising application of IoT and cloud-based computing systems. To sustain a competitive edge in such a scenario is quite challenging, especially when it comes to price optimization which acts as a competitive differentiator within the silicon wafer manufacturing industry. To contend with the dynamic business and economic environment, silicon wafer manufacturing companies need to devise an analytics-backed, structured approach to price optimization- one that considers material acquisition costs & competitors’ prices and leverages pricing intelligence to uncover the perceived value of offerings.
Silicon Wafer Manufacturing Industry Challenges
About the Client
The client is a Fortune 500 silicon wafer manufacturing company based out of Belgium. With the increase in market volatility and the rise in competition, the company’s silicon wafer manufacturing segment witnessed a sharp decline in the total sales volume. This is when they realized that price optimization would help them improve outcomes in certain segments.
Working in a highly dynamic and data-intensive semiconductor industry implies there is a significant chance of human error at any point in time. Given the extent of price accountability, the silicon wafer manufacturing company was looking to devise an analytics-based price optimization system that could predict prices by considering the supply & demand variations and market dynamics while also focusing on margin enhancement. The aim was to analyze market dynamics and find avenues for margin improvement by devising a smart, data-driven price optimization system.
Though complex decisions on pricing were made using both real-time and historical data sets, the in-house tools and reports generated by automated systems were not accurate enough to help them drive margins. This is when the silicon wafer manufacturing company’s executive approached Quantzig to leverage its pricing analytics solutions to drive greater outcomes.
Why is price optimization crucial in the silicon wafer manufacturing industry? Speak to our analytics experts to find out.
To help the silicon wafer manufacturing client tackle their challenges, we adopted a complex three-phased approach that involved a combination of data modeling, machine learning, and advanced algorithms to develop a sophisticated, NLP based price optimization model that offered real-time insights on the impact of price optimization on sales.
Phase 1: Integration and analysis of historical price data
To analyze and generate insights from historic pricing data, our price optimization experts developed customer profiles based on their past buying behavior. Various segments based on product attributes, demographics, and distribution across different geographies and business units were also built to better understand the buying behavior and patterns in customer data sets.
Phase 2: Creating differentiated value-based price optimization models
The second phase of this pricing analytics engagement focused on developing value-based price optimization models in which the price of the silicon wafer was set at the based on the customer’s perceived value rather than relying on market trends. Leveraging pricing analytics to devise such a model enabled the silicon wafer manufacturing company to develop new workflows and analyze the velocity of fulfilment based on the revenue generated.
Phase 3: Improving accuracy through continuous monitoring
The final phase of this pricing analytics engagement revolved around continuous monitoring of the devised price optimization models to enhance accuracy through adjustments in algorithms. The adopted methodology applied NLP and advanced algorithms to integrate historic data sets with real-time data from the live silicon wafer manufacturing environment to make necessary adjustments for driving continuous improvements.
The current scenario within the silicon semiconductor industry has made price management indispensable from a business perspective. Most silicon wafer manufacturing companies use ERP platforms and best-of-breed systems to address their price optimization challenges. In this case, the custom-built price optimization models proved to be cost-effective and efficient enough to address the client’s specific needs. The devised price optimization model also provided the flexibility of being configured to make it suitable to address the dynamic pricing needs of the client.
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With regular feedback on the stability of the model and market dynamics-based price changes, the deployed price optimization model proved to be extremely powerful and empowered the client to achieve measurable results, including:
- 48% improvement in win-rate
- 65% increase in revenue
- Improved efficiency of business operations
About Quantzig’s Price Optimization Solutions
Quantzig’s comprehensive portfolio of pricing analytics solutions aims to help businesses drive improvements by identifying new revenue streams and opportunities. Our price optimization experts leverage their industry experience to advise silicon wafer manufacturing clients on each step of pricing, from deciding the business rules that best suit business needs to creating price optimization models, which can identify and plug revenue leakages without any delay.
In one of our recent engagements, we helped a leading manufacturing client to identify and set new prices for their offerings and deployed a price optimization model to analyze price fluctuations. Wonder how? Request for more information now!