Network Analysis and Optimization Leads to More Efficient Operations

Challenge

A food company, managing operations across two manufacturing sites and four third-party distribution centers, faced the task of optimizing their logistics network. The company dealt with over 100 different stock-keeping units (SKUs), and management sought to assess whether their existing network of production and distribution was operating efficiently. Key considerations included the potential addition of a fifth distribution center and the possibility of closing underutilized ones. This assessment came with a set of unique challenges:

• Highly perishable products: The nature of their products, many of which had short shelf lives, required precise and timely logistics to avoid spoilage.
• Complexity from a large number of SKUs: The wide range of SKUs added to the complexity of inventory management and distribution planning.
• Order variability and short lead times: Given the short shelf life of products and fluctuating customer demand, the company had to continuously review its distribution network to ensure it remained effective.
• Factory-specific SKUs: Certain products were produced at only one manufacturing site, which complicated fulfillment and transport planning.
• Transport costs influenced by order size and weight: The varying sizes and weights of products significantly impacted transportation costs, making efficient logistics planning even more critical.

Solution

To address these challenges, the company sought an advanced modeling solution that could handle the complexity of their logistics network. Initially, they considered using a spreadsheet-based approach but quickly realized that the scale and intricacy of the problem exceeded the capabilities of traditional tools. Instead, a specialized optimization tool was chosen to build a comprehensive model of the company’s supply chain.

Within just two weeks, a detailed model was constructed to evaluate the company’s logistics network. This model enabled the team to explore a range of questions central to their optimization efforts, including:

• Fulfillment strategy optimization: Could changing the way orders were fulfilled reduce overall logistics costs?
• Cost savings through network adjustments: Would the addition of a new distribution center result in significant savings, or would closing a poorly-performing one be more cost-effective?
• Scenario analysis: The tool allowed the team to conduct side-by-side comparisons of different fulfillment and distribution scenarios to identify trade-offs and quantify potential cost savings.

The model was robust enough to handle multiple variables simultaneously, including transport costs, production constraints, and inventory management. By incorporating real-time data, it provided management with the flexibility to simulate different strategies and immediately understand their impact on costs and efficiency.

Results

The company’s use of the optimization model led to a detailed analysis of over 20 different logistics scenarios. This “What-If” analysis revealed several potential strategies for reducing costs, with estimated savings ranging between 10-25%. These savings, amounting to millions of dollars annually, were achieved by:

• Optimizing the number and location of distribution centers: The model helped the company identify the most cost-effective network structure, either by adding new centers or shutting down underperforming ones.
• Improving transportation routes and fulfillment processes: The company was able to reduce transportation costs by streamlining routes and aligning production schedules more closely with distribution needs.
• Increasing overall operational efficiency: The improved logistics network also enabled the company to reduce product spoilage and better manage inventory levels across multiple sites.

The significant cost reductions enabled the company to reinvest in other critical areas of the business, particularly research and development. With the additional resources, the company was able to introduce new product lines, expanding their market presence and improving their competitive position.

Conclusion

The assessment of the company’s logistics network using advanced modeling tools not only helped to uncover inefficiencies but also enabled them to identify actionable strategies for substantial cost savings. The ability to conduct comprehensive scenario analysis and adjust fulfillment strategies in real time proved to be invaluable. This approach allowed the company to remain agile, adapt to market demands, and make informed decisions about their future logistics network, ultimately driving both cost efficiency and growth. The case illustrates the importance of using data-driven solutions to tackle complex supply chain challenges, particularly for companies managing perishable goods and a broad range of SKUs. By investing in advanced analytics, the company not only improved its logistics operations but also positioned itself for sustained success and innovation in a competitive market.