Sept. 29, 2021
Manufacturing organizations learned some hard lessons over the last two years. Prior to the pandemic, digital transformation initiatives were conducted mostly as speculative and experimental. In 2018, $1.3 trillion was spent on digital initiatives globally, yet 70% was wasted. A lesson learned from the pandemic era is that the robustness and resiliency of operations need to be prioritized for digital initiatives.
Digital initiatives prior to the pandemic were poorly structured activities, often performed without sufficient operations involvement. These initiatives introduced some machine-learning-based solutions, but they were unfocused, with little payoff. What has become clear is that machine-learning systems cannot be used in isolation from operational teams if we are to solve important labor market and utilization challenges.
We propose an approach that uses data-driven machine learning directly in support of process experts and operational teams to accomplish digital implementations. We co-opt the term “mixed initiative” from artificial intelligence (AI) literature on human-computer interaction to describe our approach.
Process experts, especially (Six Sigma) black belts, possess immense knowledge and are trained to analyze data, but they run into constraints with:
the amount of data they can process
the number of confounding variables
lack of real-time data
On the other hand, machine learning can operate on vast amounts of data, but is limited by:
lack of reliable data about observations and outcomes.
lack of contextual information
relationships that are hard to model
Black belts understand the types of relationships and insights needed for transformational change. Machine-learning models can quickly identify patterns of interest by sifting through large amounts of data. In our mixed-initiative approach, we leverage these strengths jointly. We propose that black belts define the context and objectives, while machine learning engines extract relevant models and patterns from available data. Black belts then validate results and use machine-learning systems to operationalize meaningful process improvements.
We illustrate this approach on the problem of identifying and operationalizing a “golden run.” A golden run is a benchmark performance period where the process achieves the “best” performance.
Digital initiatives prior to the pandemic were poorly structured activities, often performed without sufficient operations involvement. These initiatives introduced some machine-learning-based solutions, but they were unfocused, with little payoff. What has become clear is that machine-learning systems cannot be used in isolation from operational teams if we are to solve important labor market and utilization challenges.
We propose an approach that uses data-driven machine learning directly in support of process experts and operational teams to accomplish digital implementations. We co-opt the term “mixed initiative” from artificial intelligence (AI) literature on human-computer interaction to describe our approach.
Process experts, especially (Six Sigma) black belts, possess immense knowledge and are trained to analyze data, but they run into constraints with:
the amount of data they can process
the number of confounding variables
lack of real-time data
On the other hand, machine learning can operate on vast amounts of data, but is limited by:
lack of reliable data about observations and outcomes.
lack of contextual information
relationships that are hard to model
Black belts understand the types of relationships and insights needed for transformational change. Machine-learning models can quickly identify patterns of interest by sifting through large amounts of data. In our mixed-initiative approach, we leverage these strengths jointly. We propose that black belts define the context and objectives, while machine learning engines extract relevant models and patterns from available data. Black belts then validate results and use machine-learning systems to operationalize meaningful process improvements.
We illustrate this approach on the problem of identifying and operationalizing a “golden run.” A golden run is a benchmark performance period where the process achieves the “best” performance.
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