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customers, forecasts, and pipeline, and hold a manageable level of inventory that guarantees delivery times,” he notes.“ Then there is our procurement team. I think of them as my day traders. They scan the global market all day, every day. Some people overbuy, and some products come onto the market through brokers, so there are narrow windows where parts are available with short lead times. We have to know when that happens and be ready to act, and we identify customers who can accelerate their schedules or pre-buy for an anticipated order.”
For defense work, a Defense Priorities and Allocations System( DPAS) rating also moves certain orders up vendors’ queues. OSS also runs a formal obsolescence plan, with software that flags long-lead or end-of-life parts early so its engineers can design around them.
The same data-led approach now runs through the production floor. Around two years ago, OSS appointed a Vice President of Operations with a background in lean manufacturing.“ You can now walk onto our floor and see television screens showing live metrics and timing,” Mike highlights.“ We use Power BI and other tools to track performance from execution to quality, and we’ ve added manufacturing engineering and quality engineering to keep reducing waste. This year, we’ ve started pulling all the data from our builds, forecasts, and supply chain into one large dataset and applying AI to it. We can ask it about trends and where we’ re heading, and it highlights actions we should take to ensure quality, delivery, and supply.”
The company’ s product roadmap spans compute servers, expansion systems, and storage, and moves in step with GPU vendors such as NVIDIA and Qualcomm, which release new revisions every 12-to-18 months. Its manufacturing model is built to grow with
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