Digital Transformation in Supply Chain Management

A Case Study of the Steel and Mining Industry

Introduction

There are many evolving technologies affecting the mining industry with different magnitudes, but not to complicate things and keep everything as smooth as the old-fashioned way is within the expertise of digital intelligence. The call of the ESG agenda has rendered many operational mines obsolete, and in order to stay in business and maintain competitive power, the administration has no choice but to adapt with these rapid changes. In this article, we will discuss the role of business intelligence in the steel and mining industry, and we will also provide real-time data to fully illustrate the landscape that we are trying to explore. Throughout the paper, we will argue how efficiency and responsiveness, as two pillars of any value chain, are the objectives of digital transformation. We will encourage the use of business intelligence methods in revolutionizing supply chain management in the steel and mining industry, using advanced analytics, automation, and AI-driven algorithms to monitor and optimize key supply chain processes such as inventory management and demand forecasting. We will also mention the potential challenges and benefits associated with implementing state-of-the-art technologies.

Do not take silver for granted

Silver, as a critically rare element with both industrial and precious applications, is without a doubt the first place to go if you want to find strong market fundamentals, record high supply deficits, and, for some miracle reasons, corresponding to a very cheap price relative to other precious or industrial metals, such as palladium. Since this article is focused on digital transformations, we should leave the silver price case to the experts to figure out. As far as we are concerned, a tight and volatile market leads to tougher competition for miners to be efficient or, at the very least, be able to pay the bills. At the same time, natural resources are becoming scarce, which means deeper mines, rising energy costs, and infrastructure shortages. The list never ends, and it is reasonable to assume that any miner would want to cut costs and improve efficiency. That is the case with the ongoing challenges faced by any mining industry, and now is the time to ask what technology can do to mitigate the risks surrounding the future performance of the mining industry. In order to maintain a practical approach, we will look at two main problems in the steel industry. Additionally, we will discuss the suggested solutions regarding the business intelligence capabilities.

1. Harsh environment and asset failure


It is well known that the industry heavily relies on high-temperature thermal or chemical transformations to make the final outcomes available. However, from the safety and reliability point of view, these kinds of environments pose significant risks to the workers. Adding to all of this is the maintenance of machinery, consisting of vital components that are exposed to corrosion, wear and tear, and structural integrity issues, often placed in hard-to-reach locations. As we move forward, we will see that workers follow analog processes, leaving them unable to comprehend the priorities correctly as the industry needs it. It is evident that the inherently unsafe and stressful work environment affects the overall efficiency. 
We know many miners who have already adopted automation and remote operating centers (ROCs) have had a better status against the Covid-19 pandemic. Although ROCs and remote monitoring sound interesting enough and, in fact, they could reduce many risks, it is the predictive technology within them that matters. You see, a mining industry produces a massive amount of data every day, which is impossible for an operator to fully grasp and make data-driven decisions at the moment of need, which is often unknown.
If we aim to minimize downtime, disruptions, and optimize maintenance schedules, business intelligence can provide advanced statistical and model-based comparison algorithms with alerts indicating when equipment or machinery deviates from the norm. By looking at maintenance strategies, business intelligence predictive analytics solutions are capable of predetermining the actions required to mitigate potential failures and optimize the maintenance strategy. For example, diagnosing equipment issues days, weeks, or even months before failure falls within the realm of these intelligent transformations. 
These data-driven decision-making processes would make the transformation from analog systems possible via many software applications such as ERP, in which the consistency of getting issues investigated, managed, and resolved is fully taken care of. As a result, human resources can find some relief and prevent the exhaustion and collapse of the system.


2. Process monitoring


Given the critical nature of the industry, it is not surprising to say that most corrective procedures happen concurrently with production, without any significant pauses. Unfortunately, the current monitoring approach has significant weaknesses in terms of accuracy when it comes to predicting potential drawbacks and inefficiencies due to its conventional analog methodology. In order to achieve reliable predictive outcomes, it is vital to use predictive analytics models combined with a deep learning approach. Such algorithms can even suggest the remaining useful life of machinery and provide actionable insights for better resource management.
To inspect and monitor the steel mills with the purpose of addressing and anticipating the most probable fatigue points or system failures before they occur, it is logical to embrace a data-driven proactive approach rather than reactive, condition-based strategies. Knowing the issues in advance could save millions in averted asset failures. For example, operators can schedule maintenance activities during planned downtime or during periods of lower production demand, leading to higher levels of resources management. Increasing productivity and enhancing operational reliability as a result of implementing business intelligence approaches can lead to reduced costs and sustainable production. Additionally, the need for any emergency maintenance purchases could be eliminated.
A complicated, out-of-control market with other challenges emerging from a changing world, such as ESGs, puts the flexibility of any supply chain into question. The previously functional supply chain, with a reasonable cost-to-reward ratio, suddenly becomes burdensome, just to cover losses because they didn't foresee many things coming. Given the growing importance of cost-effectiveness, there is a need to invest in areas with the capabilities to cope with the needs of the industry. Intelligent, business-level 5 approaches.

Hold on to your golden ticket

It's always about when, where, and how much. The same question over and over again, just to ensure that resources are allocated according to industry demand. But how do we face disruptive times when no one could possibly know what lies ahead for the mining industry? 
It is not possible to fully forecast the future. No one in their right mind would believe such a thing, right? 
If you find that relatable, you, like many others, have the wrong idea about forecasting. When it comes to predicting something, most people think of a fortune teller showing up in a weird costume. The fact is that prediction is completely subjective, meaning you can never find two similar mining companies with the same outlook on the market, even though they share the same supply chain. That's because the future does not treat us all alike. Each industry, based on its supply chain policies and agendas, moves in a path towards what they consider efficient and sustainable, but that doesn't necessarily mean the future agrees too! To wrap up this conversation, forecasting is the art of understand the current reality and trying to find a way to coexist with it and make the most out of it. It's not about making sense of the million possibilities coming from ten thousand probabilities. 
The only point in forecasting the future is to improve performance. In the case of mining industries, predictive analytics empowers systems engineers and other personnel to make decisions that improve the responsiveness and efficiency of the supply chain. Technological transformations are easy to implement, and in most cases, no data scientists are needed to model the data. However, moving toward full automation and robotic advances would require people familiar with the algorithms to configure the applications. 
Using the predictive powers of business intelligence in demand forecasting will ensure that resources are allocated in the right place at the right time. The plans and strategies resulting from different learning approaches, such as machine learning or deep learning, are key in helping mining and steel organizations not only face demand challenges head-on but also work towards achieving their ESG goals by optimizing asset and process outcomes and minimizing energy use. Let's take the steel industry as a practical example.
1. Historical Data Analysis: Demand forecasting in the steel industry often involves analyzing historical data, such as sales figures, customer orders, and market trends. By examining past patterns and trends, companies can forecast future demand for different steel products, grades, and applications.
2. Market Research and Analysis: Conducting market research and analysis is crucial for demand forecasting in the steel industry. Companies study factors such as economic growth, infrastructure development, construction projects, and automotive industry trends to estimate future steel demand. This includes analyzing data on new construction projects, government policies, and regional market dynamics.
3. Supply Chain Collaboration: Demand forecasting in the steel industry often involves collaboration with the entire supply chain. Steel manufacturers coordinate with raw material suppliers, distributors, and end-users to gather information on anticipated demand and market conditions. This collaborative approach helps optimize production planning, inventory management

The fear of AI is still out there, do they mean something real? 

It is true that AI and digital transformations are swiftly moving to replace human beings in many jobs, and some of them are working significantly better than us! On the flip side, the AI revolution started in 2012, and the progress since then has been amazing. However, you should know that the current AI is at the intelligence level of dogs and cats. Numerous reports of Tesla cars crashing while on auto driver mode further prove this statement. 
With that in mind, we can see potential challenges in implementing digital technologies into old industrial plants. Here are some risks and possible hazards to consider: 
1. Data Security Risks: The use of AI involves processing and analyzing vast amounts of data. Without proper measures in place to secure this data, there is a risk of unauthorized access, data breaches, or theft. Sensitive information, such as proprietary steel formulas or customer data, could be compromised, leading to financial losses, reputational damage, or legal consequences.
2. Ethical Implications: AI algorithms rely on data to make decisions and predictions. If the data used to train the AI models is biased or flawed, it can lead to discriminatory or unfair outcomes. In the steel industry, this could impact employee safety, workplace practices, or environmental regulations. Ensuring ethical considerations are embedded in AI systems is crucial to mitigate these risks.
3. Job Displacement and Workforce Transition: The introduction of AI technologies in the steel industry could automate certain tasks or processes, potentially leading to job displacement for workers performing those tasks. This can create social and economic challenges if adequate support and retraining programs are not implemented. The industry must plan for a smooth transition, upskilling, or reskilling workers for new roles that align with the evolving AI-driven landscape. 

Stay tuned!