Predictable Semiconductors?
An oxymoron to say the least due to the historical turbulence of the high-tech semiconductor segment, some would say that the only predictable element of the semiconductor segment is the unpredictability, riding a continuous wave of change and innovation through downturns and consumer demand swings. However, application technology has caught up to the data that semiconductor companies collect to provide opportunities for predicting a trend that may result in an advantage in a hyper competitive landscape. The advent of ‘in-memory’ databases further provides the analytical strength required to determine data trends over the required durations to establish predictability, coupled with overlays of economic pressure variables and an established seasonality effect. As a result, we see semiconductors leveraging predictive solutions in creative ways resulting in business value and changing the common ‘unpredictable’ perception often tied to this segment.
Common challenges that predictive analytics is being tasked to solve is in the area of distribution relationships. In order to maximize revenue opportunities, semiconductors often rely upon distributors to sell upwards of 50% of their revenue. In return, the distributors are contractually obligated to provide point-of-sale data on a timely basis in order to recognize revenue on the sell-through and track channel inventory levels. Customers are evaluating predictive technology to sense the future sell-through velocity for each distributor relationship, assessing which distributors are most effective and incentivized to move product. Based upon this output, a determination can be made to determine which distributors are prioritized in the event of a product allocation situation. Also, the prediction of monthly ship & debit claim accruals based upon historical sell-through trends coupled with a seasonality effect is a compelling use case for predictive analytics, providing a higher level of accuracy in order to establish the confidence to free up cash flow. Lastly, recognizing trends of inventory sell-through at each distributor can alert the analyst when abnormalities occur, identifying a potential distributor transfer scenario prior to mass systematic proliferation or the need for conducting and prioritizing distributor audits.
Another area of predictive interest is the identification and potential effect upon sales data. As an example, the identification of product bundling opportunities based upon historical correlations of multi-line product buying tendencies coupled with the new product introduction process has considerable traction in the area of predictability. A marketing organization can then use these analytics to assist with modeling discount offering scenarios and estimate potential revenue opportunities. Conversely, the recognition through prediction of the product cannibalization effect and its potential impact on forecasted revenue can assist companies in identifying the optimal time for new product introductions.
Finally, I’ll touch on predictability interest for semiconductors in the area of manufacturing yield management, both for fabless and IDM models. Predicated upon parametric data collected by either in-house or subcontracted testers, the recognition of abnormality trends correlating to production routes can assist planners in determining the most optimal routes for manufacturing product. This predictive guidance can then be weighed against supplier pricing and internal activity costs to understand the trade-offs when paying for premium services, where to strategically focus pricing negotiations and when to focus manufacturing internally. Also, predicting failures based upon historical trends with machinery usage can help gauge when to perform preventive maintenance in an effort to ensure expected capacity is retained, ultimately reducing the standard cost of producing the product.
As the consolidation of predictive analytical algorithms and ‘in-memory’ technology matures, the SAP high-tech industry team has explored many use cases within our semiconductor community, recognizing the innovative thought which continues to be a distinguishing attribute of this segment. As discussed above, predictive analytics has the potential to reduce costs, expand revenue opportunities, free working capital and strategically focus your organization’s efforts. To learn more, please click on the following link:
http://www.experiencesaphana.com/community/solutions/predictive-analysis
Jonathan Hall
SAP Industry Principal – High Tech
Email jonathan.hall01@sap.com
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