Abstract
Traditional approach to improvement is driven by good ideas from good people, but whilst the business case may sound good, when the project is complete, we often have not delivered on expectations. Why not?
The best way to successfully predict the outcomes of an improvement effort in a complex process is through using statistical modelling, which considers the impact of uncertainty within interacting processes. Applying Theory of Constraints in a model helps leaders to quickly determine:
- Where the constraining process(es) is/are
- Where improvements will add the most value
- How much improvement should be targeted - before the next constraint takes over
- The confidence level of achieving a given target, which gives us
- a clear pathway to improvement.
This paper presents an analysis of statistical modelling applications at Vale’s Manitoba Operations to achieve the 2025 targets. Using statistical modelling of the Business Structure, the team identified a series of process improvements through a Theory of Constraints Analysis. Value Stream Maps (VSMs) were used to define the required data, apply statistical modelling, and conduct training sessions enabling the Manitoba team to identify the constraining influences. Scenario testing in the Statistical models helped identify the Hierarchy of Constraints and specify the pathway to improvement.
How to Improve a Business Video link