Digital Transformation Value Indicators for a Sustainable and Circular Economy
• Reduces between 4000-5000 Tons of CO2 per service incident
• Decreases the need for an OEM technician ’ s travel
• Reduces the duration of sub-optimal line operation
• Eliminates additional labor costs
• Potentially avoids customer order fulfillment issues .
Other considerations and capabilities such as additive manufacturing can reduce dependence on spare parts and inventory reducing carbon footprint for conditioned storage space , shipping , manufacturing and packaging . Artificial Intelligence ( AI ) provides insight to operational efficiency that directly relates to reduction in energy , improved supply chain value streams , logistics and inventory control , and preventing the equipment from being brought down or working suboptimally .
As with many transformations and change in manufacturing , engineering teams are often asked to demonstrate how investments , some significant , can deliver the desired return-on-investment ( ROI ). This can be challenging at times as transformation and change requires investments that may not produce immediate benefits , but are foundational , to allow enablers as described above to function .
Many engineers and IT partners who have been tasked with transformation efforts have encountered automation and IT systems that are running on old operating systems , PLC controllers and communication protocols . Building enabling digital capabilities on top and access to data often requires long term financial planning and a digital transformation strategy and program that is scalable across the entire manufacturing footprint of an organization and not just a “ unicorn ” site project .
Antiquated or inefficient network designs negatively contribute to sustainability and carbon reduction . Two examples and impact are described here .
First , the cloud and stored data comes at a cost . It is simple to assess the first and ongoing cost of storage ($/ terabyte ). As factories move through the digital transformation journey , process , equipment , safety and quality data is accessible from IoT , manufacturing production and ERP systems are being added at an amazingly fast pace .
However , before thinking , just collect everything , it is important to consider the process , equipment , or quality attribute of interest and what is required to measure , control , or predict the process . It is also important to understand that there are business , compliance and legal upsides and downsides to the retention of all IoT data . IoT data may have a business and
142 April 2023