Responsible Augmented Intelligence for Those who make the Worldtm Augmenting Intelligence in a Diversified Industrial

Mark T. Maybury, Chief Technology Officer, Stanley Black & Decker

Commercial enterprises in all sectors are increasingly bringing intelligent systems to the market to improve productivity, safety, learning and play. For example, as a $20 billion diversified industrial with over sixty thousand employees that manufacture one half million products in 140 facilities in 60 countries, the 179-year old Stanley Black & Decker increasingly leverages Artificial Intelligence (AI) in products and processes.

As Figure 1 illustrates, our broad set of AI activities are accelerated by a center of excellence in AI and Data Analytics (AIDA) and a Chief AI Officer (CAIO) who champion AI across businesses and functions. AI plays a key role in augmenting human intelligence by automating routine tasks. For example, we employ over 150 intelligent process automations delivering over $50M in value. In our global tool business, in addition to smart tools, intelligent building information management systems enable increase awareness, coordination and productivity through natural language process and document understand to create situational awareness for various construction stakeholders. In our industrial business, we deploy intelligent IoT for attachment predictive maintenance via InSite. In health and security, systems such as our Foresite vision-based gate analysis system can anticipate and warn patients 30-40 days prior to prevent falls and simultaneously preserve their privacy. And Pria, a conversational home health companion, can track medication dispensing and monitor health for caregivers. Finally, our supply chains and manufacturing processes increasingly augmented by machine learning


Since 2014, our innovation journey has accelerated, empowered by the fourth industrial manufacturing revolution which emphasizes transformation to intelligent cyber physical systems (de Boer et al. 2018). These systems enable a symbiotic relationship between human and machine intelligence resulting in manufacturing excellence. Organizations that fail to adopt, adapt or augment this intelligent manufacturing trend and compete effectively on the rate of learning and creativity risk extinction (Kimura et al. 2019).

Our Industry 4.0 investment and strategy aims for socially responsible enterprise excellence, focusing on the most important value streams. We leverage a digital thread enabling Stanley Black & Decker to increase the speed and quality of manufacturing, accelerate our agility to refine or redesign products in manufacturing, and grow profitability through operations excellence and upskilling. Our intelligent manufacturing automation strategy is founded on a connected factory and enterprise that enables data collection for situation awareness, anticipatory analytics, distributed collaboration and remote command and control. As summarized in Figure 2, our advanced manufacturing strategy incorporates IOT connected machines which provide an ability to sense infrastructure, machine, and human activity, enabling modeling and digital twins which can be leveraged for operational and f inancial advantage.

"Artificial Intelligence (AI), which includes Machine Learning, promises to help us elevate that mission to make our lives more productive, safer and more joyful"

To date we have connected twenty factories with plans to connect 65–70% of our machines and manufacturing lines in our 140 factories by 2022, including deploying approximately 475 data kits (IoT and analytics) to enable advanced analytics and process optimization. For example, our LiveView Application, was deployed in 3 months in our Fort Mill Distribution Center with an 18-month payback. We employ cooperative (50+), mobile (60+), and traditional robots in addition to computer-numerical control (CNC) machines. Robots enhance worker safety and productivity by delegating and automating dull, dirty, and dangerous operations to machines and can improve productivity by double digits with rapid payback. Finally, digital twins can accelerate innovation. For example, in a few months Stanley Black & Decker saved $2M through simulation to refine a new US Craftsman plant design, removing expensive capital equipment when simulation revealed an opportunity for process and production streamlining.


Our deployment of AI and products and manufacturing is driven by our purpose of supporting the makers of the world, focused on building, creating and securing a better world for our customers, employees and communities. Artificial Intelligence (AI), which includes Machine Learning, promises to help us elevate that mission to make our lives more productive, safer and more joyful. Appreciating that new technologies and methods can pose new risks to society, we have developed the following principles that will guide the development of safe AI systems (IEEE 2021) at Stanley Black & Decker in alignment with our purpose as illustrated in Figure 3.


We will apply standards and protocols to create clarity and ensure understandable explanations (Maybury 2004) of the basis for and reasoning underlying an AI system’s decisions and behaviors and ensure user awareness of conditions that could lead to adverse outcomes (e.g., a biased decision, a risky action).


We will design solutions that use methods such as data and information minimization, aggregation, and deidentification to reduce privacy attack vectors. We will apply digital product security practices that ensure confidentiality, availability, and integrity


When designing solutions, we will ensure sufficient diversity and inclusion in their data, learning and testing to avoid biased training data, models, decisions, and outcomes. Diversified teams (e.g., SMEs, end users, security/safety experts) are needed to avoid unbalanced focus on system performance and optimization at the expense of system reliability, fairness, and/or safety. We aim for AI development and deployment teams that are inclusive across multiple kinds of diversity.


We aim to design solutions that effectively distribute tasks and workloads across humans and machines, ensuring appropriate roles and responsibilities (e.g., oversight over key decisions by humans, delegation of dull, dirty or dangerous tasks to machines). We seek to design effective human machine interfaces (Maybury and Wahlster 1998) including affordances that enable graceful degradation.


We will strive to ensure that AI solutions enforce appropriate self-limitations and design machine intensions and actions that are task and situation appropriate.


We will design solutions that are resilient by using methods such as diversified training and test sets that avoid classification failures, utilization of V&V methods and stress testing appropriate to the application and when switching environments. After systems are fielded, we will strive to actively monitor their performance in various deployment contexts and continuously assess their performance with their (reinforcement) learning over time.


Stanley Black & Decker is leveraging artificial intelligence and machine learning enhance products, accelerate manufacturing agility, and grow profitability. We are committed to equity, inclusion, safety, and sustainability. Given the criticality of AI and ML in work, home, and play, we aim to ensure our values are reflected in the realization of responsible products and services. Enabling responsible AI enables us to better serve humanity and be a force for global good for those who make the world.

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