Trend 1: MES in the cloud

The cloud has arrived in production and is becoming more important every day - and not just as a "data reservoir" for analytics, but to integrate production-related processes even more closely into the company-wide supply chain. More and more companies will operate their production management completely in the cloud via the Manufacturing Execution System (MES) and increasingly rely on solutions such as the SAP Digital Manufacturing Cloud (DMC).

Trend 2: Integration of SAP and Microsoft

Stronger integration of SAP processes with Microsoft 365 apps such as Teams, Power Apps or Power Automate for workflows will make work easier for workers. Employees, for example from maintenance, who do not regularly use SAP, can plan and control orders in the familiar MS environment and thus trigger the necessary SAP processes in the backend.

Trend 3: Agile project approaches

Agile and dynamic project approaches will also increasingly replace traditional top-down organizational structures in production in 2022 and replace rigid process structures with small, effective teams in short iteration cycles. Hundreds of pages of specification documents will be a thing of the past. Companies need to involve end users much earlier and pick them up when explaining complex issues, for example via visual representations.

Trend 4: Digital supply chain

The author: Björn Bartheidel is Vice President Intelligent Industry Services at Syntax.

© Syntax

The pandemic has shown many manufacturing companies how important a well-managed digital supply chain is. However, the efficient management of value-adding processes with the help of digital solutions is also becoming increasingly important, regardless of crises. By keeping an eye on your processes at all times, you can react to potential bottlenecks in good time and prevent machines from coming to a standstill due to a lack of material, for example.

Trend 5: Predictive analytics

Pure KPI dashboarding is a thing of the past: thanks to artificial intelligence and machine learning-supported data analysis, manufacturing companies combine recorded process data with order and quality data - and benefit from new, important insights: In addition to predictive maintenance, predictive quality can also improve process stability and achieve higher product quality with fewer rejects.