As a child, and maybe still even today, one of my favorite acts at the circus was the juggling act. I was always so impressed with how the performer was able to keep track of each prop, paying close attention to where each prop would land, knowing the path it was going, and making the slightest adjustment when the circuit got a little off rhythm. No matter the number of props, I knew the performer would always keep each one up in the air.
It is not unusual for commodity managers of high-tech manufacturers to feel like they are on center stage performing a juggling act while overseeing price negotiations with many suppliers. With high-tech products often composed of myriad parts, high-tech manufacturers need hundreds of suppliers to supply hundreds of thousands of parts to produce their end products. Commodity managers are barely juggling three balls while trying to figure out how they are going to keep the circuit going once the ringmaster adds one more, or two, or three. When tasked with managing many negotiation cycles while meeting savings targets, sourcing professionals are bound to find themselves dropping balls or jeopardizing quality, responsiveness, and supply continuity.
With 90%-plus of the overall product cost attributed to product components in the high-tech industry, effectiveness of negotiations is increasingly important. When aiming to secure the best price for each component while juggling many direct material suppliers, it is critical to negotiate using innovative technologies that eliminate guesswork, manual processes, and unnecessary costs. The use of intelligent analytics, machine learning, and what-if simulation and optimization can reduce direct spending and funnel directly to the bottom line.
Applying predictive analytics, machine learning, and market benchmarking
Information has always been power, and there are few places where this is truer than during negotiation cycles with suppliers. Effective high-tech businesses will aggregate as much information as possible and utilize it to make award decisions. But this often means sifting through spreadsheets and seeking information across different platforms and regions to pinpoint relevant data. It is vital that businesses have the ability to aggregate information across platforms, regions, and quarters so they do not wind up two steps behind without the data points to support negotiation and award decisions.
By applying predictive analytics, machine learning, and simulation and optimization algorithms, and including market benchmarks companies are able to make data-driven award decisions using accurate and timely data. These technologies automatically pull relevant data points, like historical prices and transaction data, across multiple platforms saving commodity managers valuable time and empowering them to make justifiable award decisions.
Using simulation and optimization
Imagine a world where you can test the outcome of an important decision before committing to a final choice – essentially minimizing all risks. The power of what-if methods and optimization allows commodity managers to make this scenario a reality.
Simulation and “what-if” methods allow high-tech manufacturers to analyze multiple supplier award scenarios, make any recommended adjustments from the optimization engine, and then come to a decision. The simulation and optimization are based on predefined business rules modeled by the high-tech manufacturer. With additional support from in-context data, commodity managers can make better business award decisions.
Realizing new value
Using these advanced mechanisms, high-tech companies are apt to see gains in:
- Sourcing to demand effectively: Being able to pull in historical data from planning systems allows companies to make educated decisions about how much to spend with each component supplier.
- Planning for trends ahead of time: Big Data and intelligent analytics allow companies to predict and plan purchasing decisions months in advance.
- Reducing total landed costs: Optimized sourcing decisions can reduce total landed costs to improve the company’s overall performance.
- Creating a transparent supply chain: Supply chains supported by data analytics encourage transparency, reduce costs, and improve the bottom line.
- Eliminating the guesswork: Analytics can help identify root causes when companies run into supply issues.
Through automated data collection and enriched analytics, business leaders have a stronger leg to stand on when approaching sourcing decisions.
Attaining intelligent results
With customer demands only becoming more complex within the high-tech industry, it is crucial that high-tech manufacturers are equipped with state-of-the-art technology that allows them to be increasingly efficient and ensure they are juggling all props of the supply chain without any hitting the ground. Sourcing simulation and optimization solutions for industries provide advanced decision support that ensures negotiation objectives with direct material suppliers are defined properly and then met. The combination of predictive analytics, machine learning, market benchmarking, and simulation and optimization allows high-tech manufacturers to set intelligent negotiation targets, scale these decisions across many suppliers, and overall reduce direct spend.
For more on the power of analytics within the high-tech industry, Take a look at “Accelerating Digital Transformation in High Tech” to learn more about the business value that can be realized with intelligent solutions.