Metals Production in the Streaming Age: How to Make Uncertainty a Competitive Advantage
Digitization has changed customer behavior. Instead of choosing a product from a limited set of options, customers search for the product on the Internet and place orders through a seamless acquisition process, thus creating a streamline of products delivered to their door. This behavior change does not just affect retailers, but industrial manufacturers as well. Is your company ready to play along?
Influenced by this new type of customer behavior, industrial manufacturers have difficulty making accurate forecasts as product demand is unpredictable. This is why they need suppliers who offer
- short delivery times,
- accurate estimation of the delivery date and
- tracking identification at the moment of order acceptance.
Why Customer Experience Matters
Metals producers have so far mostly followed the efficiency-oriented business model, which deals with the tradeoff between working capital, due date performance and machine utilization. However, this business model is no longer sufficient in the current environment - rather, a rethink and restructuring towards a service-oriented model is required. In a service-oriented production, the efficiency is necessary in order to keep costs under control. But it is not a goal itself.
A service-oriented business model must be focused on the interaction between the organization and the customer over the duration of their relationship, also known as customer experience.
Let’s take a look at the well-known customer life journey concept which describes the different stages of interaction between the customers and the product. For this analysis we will group the stages in three steps: Product Acquisition, Product Delivery and Traceability.
Product Acquisition - Focus on Dynamic Product Design and Due Date Quoting
From the customer's perspective, the metals producer must confirm the feasibility and requested delivery date for each product request. From the metal manufacturer's perspective, each requirement triggers an acceptance process. Upon receiving the customer request, a validation against the product agreement and plant capabilities is performed by the technical sales support. Once the technical request is validated, supply chain confirms the delivery date aligned with the plant production plan. Finally, all this information is provided to the sales department which communicates to the customer and creates a sales order through the ERP system. As a result of all these interactions, the whole acceptance process takes several days.
A service-oriented business model for metal production must be able to answer the same questions - with more precision and in real time! In an industrial environment it could be achieved through the combination of a dynamic product design and due date quoting tools.
Dynamic Product Design
A dynamic product design tool consists of an engine that is able to calculate orders based on a knowledge base of rules. These rules can describe how to create a production order (even for products that have never been produced). Such a tool provides a key capability for the metals producer to provide real-time answers to customers about what products can be produced and how they would be produced, with the same level of detail that an expert could provide. It can even calculate production orders to transform existing materials to meet final customer requirements.
Due Date Quoting
Due Date Quoting (DDQ) is a method of calculating a delivery date for an order. Typically, this calculation is done based on dispatch quotas calculated out of the Sales and Operations plan. The result is that not only does the assigned job lead to a better estimate, but so do future orders, as the resources consumed by each order are more precise and a more accurate Sales and Operations plan can be created.
To support the product acquisition business process, both dynamic product design and DDQ tools should be integrated to create an automated process for both agreement evaluation and delivery date confirmation. Figure 2, shows a schema describing how these tools must be integrated to create a better customer experience. The customer sends a request through a request management system that is then evaluated by the dynamic product design tool. Once the product is successfully evaluated and the PO is calculated, the DDQ is able to provide the customer with a high accuracy delivery due date, and the order is created in the ERP.
Product Delivery – Enhance Visibility Across the Whole Supply Chain
Meeting a delivery deadline for a metals producer requires end-to-end visibility across the whole supply chain and not just the “last mile” as retails stores do (Figure 3). To achieve this level of visibility, it is necessary to utilize an advanced planning and scheduling (APS) tool that is fully integrated with a production execution system and provides real-time information with the exact location and status of each material in the plant.
Aimed at providing real-time information, the production execution system needs to centralize and organize distributed data along the plant. Such information is collected with various IoT devices such as sensors, PLCs, etc. Scheduling tools use all of this information along with advanced planning algorithms to create schedules that reduce production lead time. These scheduling tools are able to analyze the solution space and create optimal and accurate line schedules that balance due date compliance with resource utilization.
The combination of all these techniques - plant automation, production tracking and APS - enables companies to reduce lead times and better meet promised deadlines and respond to fluctuations in demand.
Traceability – Verify the History and Location of Your Product
Traceability is the ability to verify the history and location of an item, including its quality requirements and manufacturing history. A service-oriented business model must collect a large data set and make it available in real time to track the quality of the production process and ensure that it meets customer requirements.
In order to trace a product, it is necessary to know how the production order targets were calculated and how the product was physically produced including its material genealogy. The tools necessary to generate this large data set and collect this information are dynamic product design and a production execution system capable of tracking all the details that describe a metal product and its entire history, from its mother piece to the last piece delivered to the customer.
In addition, collecting traceability information and using it to train Machine Learning algorithms has become a valuable asset. Machine Learning algorithms are able to detect hidden patterns and can be used to support production decisions such as predictive maintenance or production route selection.
The sole reason we are in business is to create value for our clients. By combining dynamic product design, advanced planning and scheduling, and production execution tools, it is possible for a metals manufacturer to create a streamline of unpredictable metal products to provide customers the products they need and when they need them in an uncertain world (Figure 4).
The metal producer receives the product requests from the customers and delivers the product in a short time, providing an accurate estimate of the delivery date and tracking ID when accepting the order. The result is not only a better customer experience, but also more efficient use of resources. The resulting customer experience is a seamless streamlining of products that is aligned with today's customer expectations.
Do you think your business is ready to take a step forward?
Our business consultants can help you unlock your asset potential. If you are interested, please contact us to discuss further how you can apply this strategy to your business.
Business Consultant PSI Metals GmbH
Electronics engineer with a PMD from ESADE Business School. Started his career in R&D applying Machine Learning to planning problems until he joined PSI Metals where he applied IT models to optimize metal production. His global experience in the metals industry, both in IT and business, has given him the strategic perspective and detailed view to create and implement operational solutions. Eric's interests go from science & technology to its social impact.