There is not a single day without the metals industry making the headlines: from the Guinea coup to price volatility, to the semiconductor crisis impacting the automotive industry and consequently reducing the demand for metal manufacturers. The industry is currently facing unprecedented operational challenges that are amplified by longer term issues such as meeting the target of net emissions of greenhouse gases by 2050.
The supply chain planning function is at the heart of these challenges. And while there are many differences between operating a steel company, an aluminum company, or any other metal company, there are common business challenges they all are trying to solve. For example, planners and supply chain managers must ensure the continuity of operations while also optimizing profitability and sustainability.
Digitization is a key enabler to address these issues. As a supply chain planning and the AI/ML-driven software platform provider, o9 Solutions is uniquely positioned to help address the challenges that the metal industry is facing. As such, we want to share some of our findings.
Typical business challenges supply chain leaders in the metal industry want to solve with digitization
Over the last six months, o9 held discussions with 50+ supply chain leaders and determined the most common business challenges they want to solve with digitization.
Most common supply chain challenges
in the metal industry
Run a faster and more efficient S&OP process
Set up multi-site supply chain planning
Improve demand planning process
Optimize production scheduling
Get visibility & alerts across Supply Chain (Control)
The most common business challenge is the ability to run a faster and more efficient S&OP process (60%). This category includes business challenges like running a data-driven/rule-based S&OP process at the right aggregation level (e.g., alloy grade by region), running demand scenarios (volume and product mix) and evaluating their financial impact as well as their supportability within the supply chain.
The second most pressing issue is the need for multi-site supply chain planning (50%), complete with the respective production site capacity (e.g., mills and cast houses), as well as taking into account the prime metal and scrap constraints. Too often, companies also struggle to define the right inventory level, at the right place to guarantee a given service level.
Equally important is the ability to run a better demand planning process (45%). The first and most urgent need is the capacity to run demand forecasting at product family level (e.g., grade) and translate it into attributes (dimensions, coating, etc.) to be able to then assess the impact on the supply chain. In addition, companies want to generate forward-looking forecasting, based on leading indicators of metal demand (e.g., construction, automotive, etc.). Finally, more metal manufacturing companies have made the move to online sales and they want to leverage the data from their customers.
The fourth challenge is the capacity to run optimized production scheduling (17%). Based on the production constraints, supply chain teams want to maximize production capacity (e.g., through better sequencing, campaign optimization, etc.) and increase their service level, while minimizing their CO2 emissions and their energy consumption.
Finally, more supply chain leaders want to have a Control Tower capability (9%) to react faster to disruption as well as balance the load across the network. While control towers are more traditionally associated with the consumer goods industry and retail, recent events, like the Suez Canal blockage, the Guinea coup over the bauxite mine or the Covid-19 pandemic are creating the need to quickly identify as soon as possible the consequences of such disruption and respond accordingly.
While business leaders see clear benefits in digitizing their supply chain, they are hesitating to pursue it. Here are some reasons why.
Today’s digital supply chain solutions are uniquely positioned to solve the above mentioned challenges, with integrated end-to-end planning capability, linking together market, sales (forecast and PO), supply chain (capacity constraints, targeted service level, and inventory policy) and finance data (product price and costs, non-standard costs, projected contribution margin). All supply chain leaders we talked to were convinced that the capabilities are available today. Yet very few in the metal industry have actually decided to launch a digital transformation of their supply chain.
Here are the top three reasons we hear most often.
Data quality and different ERP
“Our IT system is outdated: each production facility is running on a different ERP. Our data is of poor quality.” The current patchwork of different IT systems finds its roots in legacy systems from prior acquisitions or in previous local IT strategies, resulting in a heterogeneous landscape that is hard to harmonize.
Fear of failing (again) in the transformation
“IT transformations are resource-heavy and require a lot of attention. We have failed multiple times in the past to harmonize our data in a single ERP system.” Since the 2000s, IT transformations touching ERP systems have been synonyms for “big bang” in the organization, since they impact both transactional data and day-to-day processes. All the leaders we talked to had “horror stories” to share, making them extra-cautious. And with 70% of transformation failing, you can’t blame them.
Lack of end-to-end metal-specific solution
“We have tried an AI-driven Demand Management solution, but it wasn’t flexible enough to capture our market. The companies we talked to keep talking about SKUs and do not understand our attribute-based planning.” The metal industry has unique planning needs that are complex to handle. For example, an ingot is rolled and cut to generate multiple final products, for different clients, which is the opposite of planning assembled products (e.g., cars).
Financial justification and board approval
“The cost vs. benefit is hard to quantify. We haven’t quantified the business value so we cannot get funding.” The direct consequence of the fear of failure is that any new significant IT implementation will often require board approval. This pushes the team to quantify the financial and/or operational benefit of digitizing the supply chain, which isn’t always a straight-forward analysis. As a result, companies are postponing their investment and continue to struggle with their legacy system.
So, how can companies overcome these issues to solve urgent business challenges in a matter of months instead of years?
A look at today’s most viable technology solutions: Knowledge graphs and cloud-native platforms.
Knowledge graph-based solutions offer the capability to connect, structure, and cleanse data from different sources. When supply chain leaders say their data is not “clean,” they are not referring to missing data or wrong information. Instead what they mean is data is sitting in different places (ERPs, spreadsheets, and data lakes) with different names in each system. The good news is that today’s knowledge graphs are capable of ingesting, cleansing and structuring data, to effectively create both a data lake and the “digital twin” of the organization. Moreover, modern supply chain planning solutions like o9’s can ingest external data in real-time (e.g., leading indicator of demand like manufacturing index, GDP growth), run demand forecasting, and store the results at the right level in the knowledge graph (e.g., region, customer, product family, etc.).
This is game-changing because what used to be a time consuming task becomes automatic and sets the foundation for advanced analytics, knowledge sharing, and digital meeting in a single platform.
Recent reference model and cloud-native solutions allow for significantly faster implementation
Traditional planning systems have rigid data models because of the limitations of previous-generation database technologies: Either the data model fits the company and the metal industry’s specific challenges or it doesn’t, which usually leads either to a greater level of customization or to reverting to Excel, to fill the gaps. This translates into data silos, inefficient processes, and projects that take several years to implement.
Today’s digital platforms use a data model based on new technologies like o9’s Graph Cube, which provide flexibility, extensibility, and scalability. This allows metal companies to move beyond a simplistic model of their business to a model that covers all their specific needs like hierarchies, attributes, business rules, etc. To provide an analogy, this new approach allows metal companies to digitize their supply chain piece by piece—like Lego bricks—as compared to previous transformation approaches that are closer to sculpting a masterpiece in marble (it was possible, but required great talent).
Latest progress in supply chain technology solutions allow for incremental implementation by value case
What advanced knowledge graphs, cloud, and industry reference models mean for supply chain organizations is that they can start implementing a given scope and later expand. For example, they can start with optimizing the throughput capacity in hot rolling, cold rolling, and finishing and, six months later, integrate ingot casting and raw material procurement.
Supply chain leaders will more easily be able to quantify the target impact on a limited scope (e.g., inventory reduction by x%, capacity increase by x%), which can facilitate board approval. Additionally, implementation waves can be built as needed, with as many as needed. Each wave can be added subsequently on that same platform and data model until companies reach the full digital transformation of their supply chain.
o9 Solutions provides capabilities that enable a faster digital transformation for organizations in metal manufacturing. To learn more about our impact in the metal industry or arrange a meeting to discuss the specific challenges you are trying to solve, please request a demo with us.