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Introduction and challenges

The need to maximize asset utilization and optimize productivity are pre-requisites for sustained profitability for any manufacturing organization.

Production processes are typically very complex and subject to significant input variation from people, assets, materials, and technologies. Interrelationships between inputs are rarely fully understood and are infrequently controlled to the extent required. Accordingly, few manufacturing organizations can realize the maximum potential output from their existing production assets.

Production planning is therefore often based on an inherent assumption of sub-optimal performance from the outset. Subsequent efforts to manage and improve production process efficiency can, as a result, be focused inappropriately and result in limited overall impact. Increased output requirements are often met with a corresponding request for capital expenditure to create incremental process capacity.

Understanding and addressing process input variables, their interrelationships, and the true causes of productivity shortfalls can help manufacturing organizations exploit the full potential of their existing assets and drive profitability through optimization of cost, quality, quantity, and delivery outputs.

How we can help

Stable Ops™ reveals and exploits untapped capacity potential in existing manufacturing processes. Through a granular data-driven perspective on prevailing process variations, Stable Ops™ can drive improvements in process output of over 25% from existing assets.

Stable Ops™ mandates strict control of all input variation. Reduced variability results in improved reliability, increased predictability, higher productivity, and increased available capacity. Process stability will also drive other positive consequential impacts in safety, product quality, delivery, cost, and environmental performance and will typically also improve workforce morale and engagement.

We have worked extensively with some of the world’s largest manufacturing organizations and helped them stabilize their production processes, driving significant performance improvements:
Initial capacity improvements of 10-15%, followed by incremental increases of 10% from addressing newly identified process bottlenecks
Increased process stability from a baseline of 60-70% to 95%+
Significantly increased levels of process quality
Reduced manufacturing lead times, lower inventories, and better on-time delivery performance
Positive step-change in ROCE (return on capital employed)