Case Study of Empirical Big Data Analytic Operations for Small Trading Company

Abstract

Data driven supply chain management is an optimized system of supply chain data which
improves inventory levels, lower costs, and reduced risks. Supply chain optimization is driven by the
demand for efficient use of resources to maximize delivery while minimizing excess and obsolete
inventories. By improving demand forecasting systems and integrating supply chain data, risks and
costs are lowered. The research will explore the big data analytic method applied in the supply chain
management of small trading company in Taiwan. The volume data of case company consists of
inventory, sales, cost, logistic cost, product changing, and so on. The main findings are explored and
policies are made. Findings:1. Logistic cost is at least 11%. 2. Product change will be higher margin.
3. More sales in Taiwan & ASEAN countries. 4. Sales cost and labor cost is keeping stable even
service business. 5. The business direction meets the government policy. 6.Outsourcing strategy is
the right policy. From the findings we made our policies as the follows: 1. Investing IT system in
SCM is more important issue. 2. Gross - margin is at least 30% and up. 3. In the small trading
company, labor cost should be lower than 5%. 4. Managed service is higher margin. 5. USA,
ASEAN countries are the main market in coming years.