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SWOT Analysis in Increasing the Competitiveness of Local Fashion Products in the Digital Era at XYZ Company Setiyowati, Anggi
Siber Journal of Transportation and Logistics Vol. 3 No. 1 (2025): (SJTL) Siber Journal of Transportation and Logistics (April - June 2025)
Publisher : Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/sjtl.v3i1.448

Abstract

The SWOT analysis method is employed to pinpoint a company's strengths, weaknesses, opportunities, and threats that affect its overall success. The data related to opportunities and threats is gathered from various channels. Many organizations utilize monitoring services to gather information and assess trends. SWOT analysis examines both external elements and internal factors, which are documented in the IFAS and EFAS matrices. Maintaining competitiveness is vital for ongoing business operations, particularly in fiercely competitive local markets. Aspects like product quality, operational efficiency, innovation, and leadership play a significant role in determining competitiveness. Local businesses must implement effective management strategies. Additionally, a strong online marketing strategy is essential for sustaining revenue, even though some entrepreneurs might face challenges in developing effective plans. The growth of the domestic fashion industry in Indonesia is driving brands to adapt to new trends. This article discusses ways to improve the competitiveness of local fashion products, highlighting the factors that affect competitiveness, the role of innovation, and how businesses in the area adjust accordingly. An examination of company XYZ reveals both its strengths and weaknesses, along with available opportunities and threats. The optimal approach emphasizes using strengths to take advantage of opportunities while innovating in product development.
Determinasi Kepemimpinan Adaptif: Manajemen Risiko, Transformasi Digital dan Adaptif Perusahaan Setiyowati, Anggi
Jurnal Pendidikan Siber Nusantara Vol. 3 No. 1 (2025): Jurnal Pendidikan Siber Nusantara (Januari - Maret 2025)
Publisher : Siber Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jpsn.v3i1.342

Abstract

Artikel ilmiah berjudul "Determinasi Kepemimpinan Adaptif: Analisis Manajemen Risiko, Transformasi Digital, dan Adaptivitas Perusahaan" bertujuan untuk mengeksplorasi serta mengembangkan hipotesis mengenai pengaruh berbagai variabel yang saling berkaitan dengan kepemimpinan adaptif, manajemen risiko, transformasi digital, dan adaptivitas perusahaan. Dalam studi pustaka ini, penulis mengacu pada beragam sumber yang memberikan wawasan lebih mendalam terkait tema-tema tersebut, yang akan menjadi dasar bagi penelitian lebih lanjut. Objek penelitian meliputi pustaka online yang diakses dari platform akademis terpercaya seperti Google Scholar, Mendeley, dan berbagai media akademik online lainnya yang relevan. Hasil artikel ini: 1) Manajemen Risiko berpengaruh terhadap Kepemimpinan Adaptif; 2) Transformasi Digitalberpengaruh terhadap Kepemimpinan Adaptif; dan 3) Adaptif Perusahaan berpengaruh terhadap Kepemimpinan Adaptif.
Pengaruh Data Mining, Strategi Perusahaan, Terhadap Laporan Kinerja Perusahaan Haryanti, Mayang Fadillah; Fauzi, Achmad; Jelita, Alvina Arum; Setiyowati, Anggi; Octarina, Azzahra; Edina, Edo Putra; Aulia, Reva Zahra; Fitriana, Sela
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 3 No. 1 (2024): Evaluasi dan Kinerja Perusahaan
Publisher : Prisani Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70704/jpjmb.v3i1.285

Abstract

The rapid development of business in the digital era intensifies competition among companies. To survive, businesses must develop effective sales strategies based on accurate information. This study explores the impact of strategic datamining on company performance. Data mining techniques, such as classification and forecasting, help analyze sales data to predict trends, enabling production adjustments and sales optimization. Implementing data mining accelerates decision-making, increases sales, and reduces losses, making companies more competitive. Data mining is the process of extracting valuable information from large datasets, crucial for business improvement. It transforms transactional data into actionable insights, enabling automated and efficient analysis. This study shows that data mining enhances operational efficiency, data-driven decision-making, and predictive capabilities. Challenges like technical complexity and data quality must be addressed. In conclusion, data mining plays a vital role in improving company performance by identifying patterns, optimizing operations, and predicting future trends. This allows companies to enhance efficiency and competitive