- Leah Barrier
Let's Talk Steel Supply Chain and the Integration of Enterprise AI
With technological advancements compounding on evolutionary concepts, particularly those involving machine learning and artificial intelligence, it was only a matter of time before the industry known for laying the ground work (literally) for the latest industrial advancements took note and began employing data lakes in attempts to streamline efficiency.
To be clear, we are referring to the metal industry – more specifically, steel. The production of crude steel on a global level increased from 189 million metric tons to 1.8 billion metric tons over the past 68 years [according to the Worldsteel Association]. The expansive jump in steel production can be primarily linked to the overflow manufactured in China, specifically once they began exporting the excess product at a discounted rate.
This disparity in steel acquisitions will inevitably continue for some time, however the integration of automation in various sectors within the supply chain will remove human reliance while simultaneously increasing available data and analytics on a granular scale.
The following points outline the core concepts of the current steel supply chain as it relates today:
A multi-level sourcing inbound supply chain – The use of raw materials in steel production allows for a level of uncertainty when discussing steel grades in association with particular consumer demands.
Time sensitive production line – The process required for manufacturing steel must truly remain seamless with minimal shift changeovers and time in between. This requires efficient scheduling to be made based on upcoming production and current inventory.
An intricate 3PL and distribution network – Limitations, nuanced necessities required of the various steel grades, and unwarranted perplexities cause storage, tracking, and distribution of the product to be a vulnerable link in the supply chain.
Sales channels – Steel companies need multiple sales channels that target one single mega market. However, the inception of e-marketplace platforms has increased both transparency and profit by decreasing overhead, all while shortening the overall sales cycle from beginning to end.
“Enterprises are generating large volumes of data daily, and it’s growing exponentially. Data comes in both structured and unstructured forms. As in-memory computing, storage, and digital technologies become reliable and affordable, many metal companies are using them to develop advanced analytics and gain process insights. Up to now, however, most of those efforts have lacked organization-wide vision in the form of integrated supply chain strategies. The steel industry has significant room to benefit from improving its digital prowess.” [Enterprise AI offers Solutions to Steel Industry Disruption, Supply Chain Brain]
By phasing in exact virtual replicas of physical supply chain processes (e.g. creating a digital twin), digitized-physical integration will be accessible through enterprise AI, thus allowing for a seamless flow of communication and transparency between the two worlds.
According to Hiranmay Sarkar, a managing partner with Tata Consultancy Services, the primary functions of enterprise AI will be as follows:
Sensing of events at various stages in the steel supply chain.
Analyzing events and determining their impact on key performance indicators (KPIs) at different time frames.
Recommending alternative solutions.
Optimizing outcomes through continuous cognitive learning.
Investing in Artificial Intelligence, IoT, machine learning, and digitalization on a comprehensive level, all seemingly promise for a faster, more transparent, and efficient cyclical supply chain. Proper implementation of impending automation will be progressive and done so in a timely manner. We understand that the quick overhaul of technology can be a bit overwhelming. If you have any questions or concerns about your current supply chain or how you can begin to implement digital sectors, please feel free to reach out to us for a consultation.
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