"Smart" Pricing & the Future of the Steel Industry

An overview of the fall & rise of pricing in the steel industry and the techniques to employ for smart pricing in the future.

One of the most significant, versatile, and adaptable materials that are essential to improving the quality of life is steel. Steel is the foundation of a country's economic growth because it is the primary raw material for a wide range of manufacturing processes. Because of the crucial role it plays in a nation's total industrial growth and in the construction of its infrastructure, the steel industry is frequently regarded as a sign of economic progress.

Global Pricing Conditions

Steel prices had experienced a sharp decrease before the upswing, which started in May 2022. The uncertainty surrounding the demand picture for 2022 and 2023 was increased by the crisis between Russia and Ukraine, surging inflation, Covid-19 lockdowns in some areas of China, and the weakening of the worldwide market in late March 2022. However, post-COVID, financial recovery has followed a steady pace. With more than half of the world's supply and demand for finished steel, China has been both the biggest producer and consumer of steel. It produced ~1,030 billion tonnes of crude steel in 2021. In 2020, the nation utilized 52% of the world's total steel use. The Census Bureau reports that stocks were at a three-and-a-half-year low in Q4 2020 and that the number of unfulfilled steel orders rose to its highest level in five years. The benchmark price for hot-rolled steel reached a 13-year high in February of this year. Despite some indications that prices may have reached a peak, investors and business insiders alike continue to lack confidence. According to Barron's, the steel futures market has increased by 40% only in 2021, and Barron's does not anticipate a slowdown anytime soon. In contrast, a survey of key players in the steel industry by S&P Global Platts revealed that 22% of respondents expected price increases of more than 10%, while 44% predicted further increases in domestic steel finished pricing over the following six months. Hence, steel prices have seen a subsequent upsurge in the global market. Rising expenses have thus been transferred to distributors, who have ultimately transferred them to clients, endangering continuous demand with excessive prices.

How to face the unpredictable, fluctuating market?

Businesses need flexible pricing alternatives to stay competitive and maximize client value in a complicated, rapidly changing market. Pricing decisions for goods and services are a crucial tactic that can determine a company's success.

  • Flexibility and expertise play a vital role in creating a pricing plan.
  • Awareness about the genuine cost of the product facilitates being certain that all expenses are met as well as considerable surplus is obtained via sales.
  • Being conscious of your clientele’s needs & demands act as a strong source of guidance in deciding appropriate margin levels.
  • In situations when the competitors are out of stock on high-demand items, we could easily stock them up for that time frame if we are familiar with the crucial market shifts & trends.

When it comes to choosing pricing tactics, there is no universal law. After all, one solution does not fit all problems. Tiered pricing may be the standard in one market while cost-plus may be the criteria in another.

How to make price augmenting productive?

Three essential elements must be present for price augmentation to be productive:

  • To implement the pricing strategies, the existence of a pliable mechanism.
  • To make wise and proficient pricing decisions, availability of means & techniques to retrieve historical data & analytics.
  • To analyze all costs linked with the attainment of the product so as to realize the actual cost of the products.

Effective Pricing Strategy

We envision the following four steps for creating an effective pricing strategy:

1. Access and arrangement of the appropriate data: Steel producers require information on the short- and long-term variables that influence steel prices. To better comprehend and address data abnormalities and missing values, that data should be cleansed.

2. Construct fresh models: To comprehend the dynamic relationships between distinct variables of the industry including the prices of steel and raw materials, the drivers of demand and supply, the potential productivity of steel at global and domestic levels, and macroeconomic indexes like GDP, PCE, and industrial production, businesses will need sophisticated diverse models. A cautious analysis of the elements that are unique to their sector, such as imports, exports, and shipments must be a prerequisite for the companies.

3. Pilot the Models: Companies can evaluate the effectiveness of the models that are used to forecast the worth of regressors, that is, the prices of iron ore, coal, and scrap. These models are accustomed to performing appropriately under ordinary circumstances but during the time when the market is strained, they may encounter trouble in predicting the market trends. Following are some of the suggestive measures that can be adopted by the data scientists to overcome this obstacle:

  • Regularly monitor the validity of univariate models to enhance their efficiency.
  • Employ relevant methods to predict better steel prices. Avoid the time series method. Instead, incorporate external forecasts from third-party sources into the prediction model.
  • Examine the feasibility of using future contract prices for regressors from the markets.
  • Use multivariate models to project the aforementioned regressors.

4. Practice the Inquisitive approach: Even sophisticated models may struggle to comprehend and predict prices under highly volatile market situations. To help firms effectively integrate market and business knowledge into pricing realization, algorithms of steel price prediction algorithms can collaborate with inquisitive skills. The effect of market conditions on steel prices can thus be examined via envisioning multiple scenarios.

The emergence of the 'Smart' Pricing Software

AI and data science has taken the world by storm. Due to their diverse applications in the market, "Smart" pricing software has slowly become more widely available, that too at reasonable prices. This has opened doors for industries to digitally revamp their pricing strategies. There are several advantages of data-led pricing that are particularly pertinent to the turbulent market climate of today:

  • Speed: At astonishing rates, contemporary data analytics algorithms query data sets and identify patterns that can be utilized to guide decision-making. Leading price-optimization and management solutions have the ability to implement those judgments automatically across various systems at equally amazing speeds. This eliminates the need for price-change choices and their execution to lag behind in terms of pricing. One important aspect of what we mean when we talk about dynamic pricing is that smart pricing systems can keep up with the most rapid changes in market conditions.
  • Specificity: Businesses can choose a more specialized, granular approach to cost-passing than the conventional "peanut butter-spread" strategy, in which price increases are implemented equally across the board. AI can recognize micro-segments and then determine each micro-segments price elasticity when utilized to optimize prices. By doing this, pricing tactics can be modified for different customers based on which ones are more and less susceptible to price changes for a certain product. Before setting new pricing, you can also forecast how these tactics will perform. For instance, you can know how a 1% price increase will affect sales and margin in one area versus a 5% price rise in another. As a result, there is a better balance of price rises without risk.
  • Scale: These platforms can gather enormous amounts of data from countless sources without slowing down, as well as compare and collate dissimilar data sets to produce novel and distinctive insights at a granular level that even the most seasoned team of analysts couldn't achieve with manual tools. For instance, comparing internal data sets with raw material cost indices and competitive pricing to inform pricing plans in the situation of cost volatility.

This offers a comprehensive approach to pricing. Changes in pricing don't take place in a vacuum. The effects of passing on commodity price increases to customers on demand, reputation, and sales volumes must be taken into account in the context of a competitive market with rivals making their own decisions. Steel will probably continue to be crucial to economic expansion. However, new tendencies like the push for environmentally friendly industrial methods (such manufacture using recycled materials) will cause more market disruption. Understanding supply, demand, and pricing changes will include numerous additional drivers. To comprehend these elements and provide intelligent pricing to their clients, steelmakers will use all the resources at their disposal, including AI, machine learning, cloud, analytics, modeling, and other cutting-edge technology.

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Swara Keni

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