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Hi, I'm Alfred Becker, a product manager at SAP, and I'm responsible for all SAP solutions relating to forest products, paper and packaging industries. I'd like to share some thoughts on digital transformation in the paper & packaging industry, for example the current state of the industry, the applicability of new technologies, how thought leading companies are using innovation to becoming intelligent enterprises and much more. Let's start today with an introduction to:

Using Big Data to power Benchmarking in Production 

(Tired of reading? Listen to this 6 minute podcast, first published on Paperitalo)

Benchmarking has always been one of the key ways companies can get better – by comparing and improving process based on who has a best practice. Today many paper mills have some solution in place to do benchmarking and to track KPIs at the mill level. But they lack the full visibility and integration of all relevant data sources to compare fully within the mill and even with other mills. The same applies to other levels of insights, for example shifts or product groups. Thus, they don’t really help the business as much as they could

Technology can make their life easier by having an integrated way to automatically collect the data and share it with the appropriate people both within the mill and at headquarters. That means production processes and approaches that happen locally at a mill can be automatically collected and shared with others (as authorized) who can then leverage that data to learn how to improve.

Furthermore, companies can get more innovative. In the past benchmarking was based on basic statistics like tonnage, for example: how many tons in that shift - and you were king if you could do 400 tons. But what if you created all the product, but are not then able to sell it?

Today, many companies are looking very differently at what or how to benchmark. For example, contribution or profit margin could be your most relevant KPI. To understand profit, companies need to know sales order related information, like the price a customer would pay for a particular product at that time. By integrating the enterprise level to the production level, enterprise information can be used to do benchmarking with sense!

Figure 1 shows an example of a dashboard with key production KPIs
SAP Manufacturing Insights showing standard KPIs on tiles, tiles can be adapted to individual needs.

One of Europe's leading corrugated packaging companies has shifts that compete for the best run based on the money they make. But they cannot only strive for the golden batch (perfect quality at lowest cost), because that does not take into consideration if the products actually ended up selling well at a good price. At several of their mills they are now tracking success in production by using revenue and margin. Of course, they still continue to track production loss, energy consumption, amount of rework, etc. but in the end, they want to know which teams made money and how much. Showing those respective KPIs on flat screens to their production staff, they have kicked-off some internal “friendly” competition based on profitability. They confirmed that this has increased productivity in the manufacturing areas as well.

In my daily work, I hear from a lot of our customers, both executive management and mill managers, that transparency to data can be a good thing assuming it is well managed and people in the mill are bought into the process.

The best way to achieve both transparency and one source of the truth is through a fully integrated approach. This gives companies a real understanding of their own processes that can allow them to make changes to improve production. So, coming back to the golden batch example: production managers would review the numbers and identify the best recipe for a certain product. The recipe master data would then be automatically updated in the SAP system to reflect what should be used to plan the next run.

Making life easier for the mill staff

Paper and packaging mills create a lot of data, such as uptime / downtime / output levels and much of that data is created in an autonomous way by many devices or systems. This data is stored in many, many different places, e.g. in historians, or different technology platforms.

Figure 2 shows a view of different organizational levels in a company, each relating to certain data. Furthermore, data can be collected from all steps of a business process usually. The holistic view on all available data will help in optimizing almost all steps of the design-to-operate process.
Different levels of information in a plant, which can be re-used when integrated.

Mill operators normally spend a lot of time collecting data and reporting to their managers and supervisors and the enterprise that wants to know what they are doing.  But reporting ends up being much more truthful and easier with an integrated data approach. You can skip double work, just aggregate based on the requirements of reporting levels. We often hear: “No one wants to spend days in excel”. Thus, integration of all data onto one single (analytics) platform can easily get you the buy-in of production staff because it will leave them with more time to do what they are intended to do: producing paper.

Finally, this analytical insight leads to the ability to predict problems ahead of time. Based on what you learned, you could produce in a different, maybe more profitable way... but that will be topic of one of the next blogs. Stay tuned!