Dan Brody Chief Information and Technology Officer CITO

Analytics in the Steel Industry

BI1imageHaving worked across many industries designing, building and implementing systems from consumer goods to heavy manufacturing I have benefited from seeing how analytics is used across different sectors within the global economy. In the consumer goods industry retailers gather information in detail about every dollar spent by consumers and utilize the data to improve sales and the consumer experience. Consumer goods manufacturers utilize data to determine the impact of its marketing campaigns, its brand image and hopefully improve their just-in-time delivery.

I have built and worked on manufacturing ERP and MES systems that produce huge amounts of data at every point in the manufacturing and supply chain process. But do steel manufacturers take advantage of this data in a constructive way? Can they see the data in a way that makes sense to them? Providing a deep understanding of all the new data sources and the tools designed to analyze and exploit the data that is available have changed drastically over the last few years. It is now much simpler for someone to gain access to that data are presented in a logical streamlined fashion.

This new generation of analytics tools can bring capabilities to help management make better decisions on pricing, supply-chain management, workforce trends, and overall performance of their company as it compares to their industry. What a steel company can implement analytics technology into their processes, the results can be a sharper view of patterns and signals buried deep within the data of the enterprise. This ultimately will lead to better decision-making and improved ROI.

One of the factors that is often overlooked when discussing analytics is the ferocious growth of data over the last 5 years. While the newer analytics tools are powerful companies must first deal with and organize the enormous amount of historical data well as the data they collect now. In the past this was done through manual building of data warehouses where information was neatly organized and prepared for analysis. Given today’s flow of data the paradigm has shifted; the data warehouse process must be automated. Enter the age of data warehouse automation, where tasks that could take months now happen in days or weeks. This speed translates directly and rapidly into business insights that reduced costs and increase profits.

What is analytics?

Analytics is the practice of using data to drive business strategy and performance. Analytics includes a range of approaches and solutions, from looking backward to truly understanding what happened in the past two forward-looking predictive modeling and scenario planning. To note as we see an improvement in the world of artificial intelligence I believe this will greatly affect the predictive modeling and scenario planning.

Analytics is a set of capabilities. These capabilities are the result of process that identifies business issues, assembles facts, reports on optimizing performance, provides deep insights and answers.


The components of Analytics

There are four major components of analytics including:

  • Data management. The development and execution of architectures, policies, practices, and procedures that manage the collection, quality, standardization, integration, and aggregation of data across the enterprise.
  • Business intelligence. Querying, reporting, online analysis, and alerts that can answer the questions: what happened; how many: how often; where; what exactly is the problem; what actions are needed?
  • Performance management. Advanced methodologies, comprehensive metrics, processes, and analytical applications used to monitor and manage the business performance of the steel enterprise.
  • Advanced analytics. Use of modern data mining, pattern matching, data visualization, and predictive modeling to produce analysis and I’ll go rhythms that help business make more meaningful, productive decisions.

There are new trends in the analytics world with regard to manufacturers. Manufacturing executives in all sectors need to be aware of these changes and the advantages they bring to their business. Here are several important drivers that should be considered:

  • Global data volumes continue to grow exponentially, and so do our capabilities to analyze that data volume grow along with it.
  • From strict compliance and regulatory factors are demanding deeper insights into risk, exposure, and public responsiveness, requiring integrated data across the enterprise.
  • The competitive landscape compels investment in analytics infrastructure and tools that can improve financial, economic, environmental, and market information. Manufacturing companies are using advanced analytics to help identify potential new customers and possible new niches for sales. This functionality will help determine where to focus new product development, identify challenges and improve customer retention, and provide the lowest cost to market.
  • Holistic signal detection from the traditional internal and external structured data, along with voice, email, social networking and sensor enabled products and assets, should be integrated and monitored for real-time operational insights and decision-making.
  • The growing complexity of the global business has raised the stakes at all levels of decision-making. Trying to process that huge amount of information, decision-makers need more efficient tools to quickly uncover the hidden patterns that may otherwise go undetected.

Implications for steel manufacturing industry


So how can all this data and new analytics techniques and solutions really make a significant impact on the manufacturing value chain? The manufacturing value chain is a significant place to look for initial analytic advantages. This is because of its complex city in the steel sector and of the central role the supply chain plays in a steel companies cost structure and ultimately its profits. Supply chains can appear simple compared to other parts of the business. I’ve often heard “we’ve done it this way for years” attitude masks opportunities and inhibit the ability to dig deeper into the data and adopting a predictive rather than hindsight attitude towards trends and operational changes.

Steel manufacturing companies can gain larger increases by applying a more holistic approach to using analytics. Many of the first and most significant applications of advanced analytics in the steel industry will likely be in the supply chain operations, or in other words the service center of the steel business.

Applications of advanced analytics for the steel sector

Here are some early examples of applications of advanced supply chain analytics in the global steel manufacturing sector:

  • Product costs of margins. An established steel producer found that its legacy systems were not providing sufficient granularity and into its costs and profitability byproduct, order, mill or customer. In particular, the existing system could neither respond quickly enough to the increased volatility of iron ore, scrap and energy prices, nor adequately deal with the increased pricing volatility on the demand side. This typical company by implementing advanced analytics capabilities on top of the contemporary enterprise resource planning solution. The combination can give management faster and more accurate insights into costs and margins for each order and will help improve decision-making on pricing, demand management, production scheduling and operational investments.
  • Customer insights and risk management. The most recent financial crisis and recession hit the steel industry especially hard. Most large suppliers faced operating losses and cash constraints. This of course led the steel industry to plummeting market prices, high raw material costs that significantly effected the bottom line. By having an industry scorecard combined with traditional financial metrics and with nontraditional indicators from many of the new sources of data noted above. A resulting dashboard, updated in real time as conditions changed would allow manufacturers to make informed decisions at both a tactical, daily order level and for long-
  • Capital investment decisions. The steel and processing industries have long had tools available to stimulate production processes. Companies have used these tools for years to assist with greenfield projects and rebuilds. In the past, however the tools were cumbersome required highly trained experts to use them and took weeks to provide answers. The next generation of tools combines visualization of dynamic simulations with traditional numerical results, and does so in a vastly more user accessible fashion. Answers can be generated in days, not weeks. Consequently, several major steel companies now use these more advanced tools to support a broader range of capital investment decisions, power of moving bottlenecks an individual unit to investment in fork trucks and coil handling equipment.
  • Consumer product demand sensing. Getting a hold of the downstream users of steel, such as manufactures of white goods and consumer durables are using analytics to better sense to man through the value chain. Steel users are collaborating with their trading partners to generate improvements in customer service while at the same time lowering the overall inventory levels and reducing scrap and obsolescence. Consumer product companies are collecting and sharing data with retailers – data such as point-of-sale information, product promotion calendars, and new product launches to jointly plan and predict consumer purchases an activity. With this information, consumer product companies are better able to predict demand and reduce uncertainty. Using similar analytics tools and approaches, steel manufacturers can utilize this improved insight into and of value chain demand, enabling better production planning and demand matching, reducing inventory levels and improving responsiveness.

Pricing, profitability

There are other compelling applications for analytics with in the steel sector for example: Pricing and profitability management. Based on my personal experience in the steel industry pricing was often handle as this is the market price whatever my shop cost costs and so the service that was provided severely discounted against the cost of raw materials. However, a diversified steel company facing increased commoditization of its product lines wants to improve margins by pricing its products based on customer specific cost to serve. Additionally, they want to add insights into customer value from alternative products and service mixes. To do this the company has to better understand its customers buying behaviors in the global market and to have a better understanding of how it’s all business units in various sales channels are actually pricing in the real world. If the company employed advanced analytics tools to combine and analyze the internal data on costs and the prices with external data on the market and individual customers. The result would be new pricing strategies that would boost margins.

What’s next for analytics in the steel industry?

As steel companies continue to get squeezed by market volatility and rising raw material costs they need to address the supply chain challenges through early detection by using advanced analytics they can create a positive impact on their overall business strategy. Utilizing advanced data warehouse automation and supply chain analytics represents operational shift away from management models that were previously built to respond two issues and upsets. The realignment refocuses the internal cross sharing of data to greater coordinate and understand the information across the value chain.

Analytics can be applied to some of the more complicated issues that face steel manufacturers, including improving safety, meeting environmental requirements cost-effectively, and managing price volatility more effectively. New analytics tools are giving management new insights and lovers to address these continuing challenges.

Analytics is not some fad that just going to pass by, it’s here to stay the amount of data week produce keeps increasing exponentially. Steel manufacturers knees to understand how these emerging tools can help them improve their strategic thinking and the future of their business plans. It is often said of the steel industry the the operate like dinosaurs, but if we use the analogy from Jurassic Park the dinosaurs evolved and birds! That analytics has the ability to be transformative in giving our old industry the capability to evolve and grow.

I have been advising a Data Warehouse Automation, Analytics and BI reporting company that is ready to help manufactures and distributors in the metal industry. For a free demo of data warehouse automation and data analytics, please visit AnalyticsROI –



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