Data Science & Manufacturing
Industry 4.0, Smart Factory – manufacturing of the future has many buzz words, which refer to the intelligent utilization of accessible machine data to enhance and improve operational performance. Analysis and modelling of production processes enable the sustainable reduction of down times. At the same time, big data equips production lines to reliably handle great product variety at maximum machine utilization. Shop floors at German (and international) manufacturing sites are – indeed – already highly optimized and data driven. Data availability is rarely an issue and – first and foremost – the goal is clearly defined: maximum output at minimum costs. End of story? Not really, because data science holds the key for new areas of value creation, which go beyond traditional shop floor optimization.
With structured and unstructured information at hand, manufacturing companies gradually move beyond the shop floor and aim to integrate data from various departments, in order to gain new overarching, intelligent insights.
Predictive maintenance and new business models
Intelligent predictive maintenance has been on the agenda of manufacturing executives for quite some time, as it is highly entangled with machine downtime prevention. Nevertheless, maintenance is not only an issue for continuously optimizing the own production lines. With sensors and wireless communication as an integral component of manufacturing equipment, it can be utilized to optimize beyond the company’s organizational borders.
Especially for companies, which are in the business of selling heavy machinery and other investment goods, services gradually become a major revenue stream. Preventively sending service teams to customers is crucial to actual prevention of machine disruption and to maintain a high service quality perception. Providing high service quality is enabled through data science and customized algorithms, which take in machine data and effectively tailor predictions to the respective environment at the client’s site. Additionally, companies might experiment more with leasing their equipment and machines and establish new pay-per-use models. Data science enables organizations to reliably predict revenue streams per customer and provide the basis for business model innovation, if explored on a continuous basis.
Holistic, integrated business and manufacturing steering
Shop floor performance is not an isolated endeavour anymore, but its optimization is carried out along various dimensions. Integrating manufacturing output indicators with other business-relevant metrics is key to sustainably improve performance. Data science and machine learning can effectively help to quickly establish, challenge and test hypotheses about causal relations and pattern and thus assist to build strategies upon those insights.
Data science unfolds its key value proposition especially in those scenarios, where data from different domains is merged and mined for insights. Manufacturing executives are on the constant quest to understand their business even better, identify new levers for optimization and innovate across all dimensions. Establishing a powerful data science culture across all domains, which delivers unique insights and thought provocation, is crucial for advancements on quality control, business steering and bottom-line performance.
Elimination of overhead costs
While process optimization itself usually encompasses the elimination of overhead costs, wherever feasible, it is not only manufacturing processes, which need to be assessed with a new lense of technological advances. Indeed a new culture of the lean organization should be fostered with data science and rolled-out in manufacturing companies.
For example, service processes can be streamlined by exploring techniques of natural language processing on existing service documents, mapping the service processes accordingly and starting to automate some components of the service process. Within the sales process, sales agents might automatically be provided with relevant documents which enable them to deliver more value to potential clients and optimize conversion rates and handle more leads. Continuous improvement attitudes, which are inherent in the lean manufacturing culture should be actively championed and disseminated to all other departments of the company. Nurturing this culture could further cut overhead costs, because data science opportunities are actively sought and implemented.