By investing in data-driven insights, CPG companies can significantly reduce costs and clear out inefficiencies associated with their supply chain networks, leading to enhanced profitability across the value chain.
Managing and operating supply chains, particularly in the consumer-packaged goods (CGP) industry, can be a challenging task. The increasing costs of sourcing, inventory, warehousing, and dispatch can have a significant impact on the operating profits of companies. Additionally, the dynamic choices and evolving consumer behaviour further add to the complexity of the supply chains of CPG companies.
To overcome these multidimensional issues, data-driven solutions can provide valuable insights to enhance the fragmented supply chains of CPG companies. These data insights can help optimize demand projections, inventory management, warehouse efficiency, and cost optimization, leading to improved overall results for CPG companies.Learn more about how data-driven insights can aid organizations in reducing the cost of their supply chains and enhancing their profitability.
1) Demand forecasting:
Forecasting demand is one of the critical factors in keeping supply chain costs in check. When companies overestimate demand, they end up with excess inventory that costs
money to store and manage. On the other hand, when companies underestimate demand, they risk running out of stock, leading to lost sales and disappointed customers. By using
insights derived from data analytics and machine learning algorithms, CPG firms can better predict demand levels and achieve higher levels of profitability and customer
satisfaction while keeping the costs well within the limits of tolerance.
2) Optimising logistics:
Data-driven insights can prove crucial in optimising the cost associated with transportation. As logistics accounts for a major portion of the total supply chain costs, cost
optimisation in this area can yield significant benefits for the companies. AI and ML-driven algorithms can prove instrumental in identifying ideal routes, building warehouses,
locating distribution centres, and analysing patterns to offer a range of optimization techniques associated with logistics. All these insights help CPG companies to identify the
inadequacies and optimize the logistics cost to realize better efficiency and effectiveness in transport operations.
3) Inventory management:
Managing inventory figures is one of the key tasks for improving both the bottom-line and top line of CPG firms. Having an excess of inventory ties up the valuable resources of
organisations as they must incur extra costs on storage, handling, and insurance operations of the additional stocks. Data-driven insights leveraging Machine learning and AI
models can help organizations better manage their inventory levels. These evaluate important parameters on demand, lead time, and stock levels and by keeping the inventory at
its optimum can lead to the improvement in the cash flow and profitability of companies
4) Supplier performance:
Advanced data analysis techniques are very helpful in collecting, analysing, and controlling the performance of suppliers on a range of parameters including quality, delivery time,
and cost among others. The data obtained on these critical aspects can be analyzed to evaluate the suppliers' performance and identify areas that need improvement in the value
chain. By implementing corrective measures, the suppliers' overall performance can be enhanced, leading to improved product quality and performance thus enabling companies
to achieve their goals while optimizing costs
5) Procurement and prime spend:
AI and ML-powered data analysis models aid in optimizing procurement costs through various means. These data insights can help organizations improve their spending
management by reconfiguring their supply network and procurement processes. Analytics can enable better visibility and control over raw material and packaging expenses,
helping clients identify the brands or SKUs most affected by cost increases. This information can provide valuable insight into pricing decisions
Conclusion:
The potential for data-driven insights to revolutionize the supply chain networks of CPG firms is significant. The application of AI and ML in data analysis techniques has further
improved the effectiveness of these models. Leveraging AI-driven data insights, CPG companies can significantly reduce their supply chain costs, ultimately leading to enhanced
profitability for the organization.
The article was originally published in Dataquest
