{"id":39678,"date":"2022-01-18T03:57:02","date_gmt":"2022-01-18T03:57:02","guid":{"rendered":"https:\/\/www.sisinternational.com\/?page_id=39678"},"modified":"2025-09-23T23:46:06","modified_gmt":"2025-09-24T03:46:06","slug":"marktforschung-im-fertigungsbereich","status":"publish","type":"page","link":"https:\/\/www.sisinternational.com\/de\/losungen\/qualitative-quantitative-forschungslosungen\/marktforschung-im-fertigungsbereich\/","title":{"rendered":"Marktforschung im verarbeitenden Gewerbe"},"content":{"rendered":"

Marktforschung im verarbeitenden Gewerbe<\/h1>\n\n\n\n
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Emerging from the Pandemic amid big changes<\/h2>\n\n\n\n

The Global Manufacturing Market<\/a> emerged from the COVID pandemic, challenged by supply chain instability (headlined by a large-scale semiconductor shortage), rising freight and raw material costs, a workforce shortage, and an ever-expanding need for digital integrations and accessibility across all sectors. Furthermore, with US manufacturers reporting phishing or ransomware security incidents in the past 12 months, Cybersecurity has become a bigger need in the sector.<\/p>\n\n\n\n

Manufacturers are building back fast despite these significant headwinds with rising demand, and are expected to return to pre-pandemic production within several years.<\/p>\n\n\n\n

Die Pandemie hat Hersteller dazu veranlasst, ihren Omnichannel-Ansatz anzupassen und ansprechende Erlebnisse mit ihren Kunden zu schaffen. Ein proaktiver Fokus auf Effizienz, Prozessverbesserungen und die Integration neuer Technologien treibt die besten Segmente voran.<\/p>\n\n\n\n

KI, IIoT und vernetzte Technologien<\/h2>\n\n\n\n

Artificial Intelligence and the Industrial Internet of Things (IoT) have graduated from buzzwords to a prioritized focus, driving post-pandemic manufacturing improvement. Improved sensor data and investment in using that data through IoT, machine learning algorithms, predictive maintenance, and customer experience optimization tools have been a large driver in manufacturing\u2019s improved efficiency. For many industries like healthcare, retail, automotive, and transport, the IIoT will significantly improve reliability, production, and customer satisfaction. Acceleration in digital technology adoption will bring operational efficiencies to scale.<\/p>\n\n\n\n

Artificial Intelligence and the Industrial Internet of Things (IoT) have truly graduated from buzzwords to a prioritized focus, driving post-pandemic manufacturing improvement. Improved sensor data and investment in using that data through IoT, machine learning algorithms, predictive maintenance, and customer experience optimization tools have been a large driver in manufacturing\u2019s improved efficiency. For many industries like healthcare, retail, automotive, and transport, the IIoT will significantly improve reliability, production, and customer satisfaction.<\/p>\n\n\n\n

Daten f\u00fcr eine verbesserte Betriebseffizienz<\/h2>\n\n\n\n
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Multi-pronged supply chain strategies will help protect companies from the vast challenges and rising costs of goods. Leveraging existing and recently enhanced data \u201cfabrics\u201d to connect the dots and work through complex scenarios will find the most efficient outcome in supply chain-related problems. Advancements in so-called \u201cdigital supply networks\u201d and analytics systems help plan, provide additional reserve materials, and give manufacturers more isolation from the recent supply chain instability.<\/p>\n\n\n\n

Furthermore, existing and additional new staffing data can help understand concerns, improve retention, and improve overall work efficiencies as remote work becomes a desired alternative, even in the manufacturing world.<\/p>\n\n\n\n

Marktforschungsl\u00f6sungen f\u00fcr die Fertigungsindustrie<\/h2>\n\n\n\n

SIS International Forschung<\/a> has over 40+ years of experience in Market Research and Strategy Consulting in the Manufacturing industry.  We conduct research with suppliers, manufacturers, wholesalers, retail partners, and other stakeholders.<\/p>\n\n\n\n

Wir bieten an:<\/p>\n\n\n\n