Supply Chain in Local Food Businesses: Basis for Business Model Development
This study aimed to assess the impact of supply chain disruptions and inefficiencies on local food businesses in Daet, Camarines Norte. It specifically investigated common supply chain management practices—focusing on inventory management, sourcing and procurement, and logistics and transportation—and examined the relationship between these practices and the supply chain issues experienced by these businesses. A quantitative research design was employed, using a structured survey questionnaire administered to 138 respondents from 46 local food businesses. Respondents included business owners, managers, and key personnel involved in supply chain operations. The survey measured the extent of supply chain practices and the level of disruptions using a five-point Likert scale. Data was analyzed using descriptive statistics (Weighted Mean) and Pearson Product-Moment Correlation to test for significant relationships. Results indicated that local businesses emphasized inventory availability and supplier relationships, though many still relied on manual inventory tracking. Adaptability in transportation was evident, yet fuel efficiency was less prioritized. Internally, communication breakdowns were the most frequent disruption, while extreme weather events were the leading external factor. Statistical analysis showed no significant relationship between supply chain practices and the disruptions experienced, suggesting that external, uncontrollable factors had a greater impact. Despite strong supply chain practices, local food businesses remain vulnerable to external disruptions. The study recommends transitioning to digital inventory tools, diversifying suppliers, adopting fuel- efficient logistics, and improving communication systems. A proposed business model aims to enhance internal coordination, resilience to external shocks, and overall operational efficiency
Institutions
- Saint Louis College La Union (PH)
Publication Details
- Journal
- Knowledge Commons (Lakehead University)
- Published
- 2025-08-31
- DOI
- https://doi.org/10.17613/7pwwj-a7j30
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00