What is OEM Manufacturing?
OEM Manufacturers (OEMs) make parts and supplies for other companies to incorporate into their own products and resell under their brands. OEMs and the companies they market to, known as value-added resellers (VARs), share a symbiotic, cost-effective relationship.
VARs are able to acquire elements needed to assemble their own offerings without the hassle and costs of running a factory. At the same time, OEMs drive profit by producing specialized equipment tailored entirely to their customers’ expectations.
In recent years, both the language applied OEMs as well as their general practices have begun to shift. In some industries, the term “OEM” has confusingly become interchangeable with VARs, requiring both manufacturers and resellers to be more precise in their descriptions and marketing.
Another major change that’s impacting OEMs is that while these manufacturers have traditionally been B2B companies, consumer demand for authentic parts is slowly driving these businesses to accommodate B2C as well. Finally, a major paradigm shift that’s disrupting OEM efficiency and output are evolving analytic capabilities.
How can OEM Manufacturing use Prescriptive Analytics?
Prescriptive analytics monitor and evaluate customer data to make suggestions about future processes and actions. As manufacturers of physical goods, OEMs can gain long-term benefits from integrating embedded analytics into their existing systems. A microchip OEM can apply prescriptive analytics to optimize their machines’ configuration, maintenance schedules, QA, capacity utilization and reduce wasted time.
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Moreover, prescriptive analytics can introduce agile emergency strategies by anticipating and planning for unexpected events that could disrupt production, such as machine failure or power outages. Finally, prescriptive analytics can be used to create better schedules for replacement, understanding how worn machines are and how likely they are to fail.
OEMs can additionally leverage prescriptive analytics to gain better understanding of their expenses and revenues. Important metrics such as cost-per-good and throughput can be cross-referenced against sales patterns to provide companies with an accurate assessment of overall production ROI as well as concrete steps to cut back on redundant expenses.
Constant monitoring and analysis of sales data enables prescriptive analytics to identify sales trends as they emerge and to recommend which VAR relationships should be prioritized, cultivated, or dissolved based on these trends. These same analytics can also be employed to map out optimal communication strategies with VARs based on past attempts and outcomes.See Sisense in Action Back to Glossary