Supply Chain Cost Optimization

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Supply chain costs account for a significant percentage of the final product cost.  Besides raw material costs, logistics can account for anything between 5% and 50% of the total product cost, depending on where products are sourced, their value, and the shipping methods. 

Reducing these costs while improving operational outcomes has become a primary mechanism for increasing profitability.  Simply reducing supply chain costs often introduces other problems, and the global pandemic of 2020 has demonstrated that the lowest-cost supply chains are not the most resilient.  A better approach is implementing a unified and collaborative optimization software that reduces cost and determines the optimal combination of expense, resilience, and quality. 


What is Supply Chain Cost Optimization 

Supply chain cost optimization refers to managing supply chain costs effectively to achieve the best possible return on investment while ensuring reliable supply. Cost optimization includes: 

  • Raw material 
  • Transportation methods 
  • Procurement 
  • Inventory policies 
  • Distribution costs 
  • Forecasting and demand planning 
  • Customer service levels 

From sourcing the raw material to getting the finished product into the customer’s hands, every supply chain stage is part of the supply chain and a target for cost optimization. 

In their 2020 Hype Cycle for Supply Chain Strategy, Gartner states that cost optimization is achieved by determining the total cost of ownership instead of the lowest supply chain costs.  Through this process, suppliers quantify costs and evaluate service tradeoffs related to the complexity of supplying goods to customers. 

Successful cost optimization uses advanced supply chain solutions such as sales and operations planning (S&OP) and integrated business planning (IBP) supported by advanced supply chain analytics. 

Why is Supply Chain Cost Optimization Important? 

In a perfect world, an organization’s supply chain system automatically sources the exact quantity of raw materials at the right time and, optimally, ships those materials to the appropriate processing facilities.  Once processed, the finished goods are sent to the correct distribution center for prompt delivery to their customers. 

In reality, supply chains are complex and rife with uncertainty; materials are unavailable, deliveries are late, manufacturing has holdups, and sales forecasts are inaccurate. 

In an increasingly complicated supply chain ecosystem, companies struggle to obtain a coordinated view of the entire process, both from a high-level, strategic perspective, as well as a detailed, granular viewpoint for execution.  According to McKinsey, poorly managed logistics and distribution centers hide nearly half of all supply chain costs.  What is needed are tools and information systems that enable organizations to determine what is going on so the right decisions can improve supply chain cost optimization. 

Advanced Analytics in Supply Chain Cost Optimization 

Regardless of the current state, companies seeking to reduce supply chain costs effectively need to gather, measure, and evaluate historical, present, and future performances using advanced supply chain analytics

Each form of analytics is essential: 

  • Descriptive analytics inform you of what happened. 
  • Diagnostic analytics provide the reasons why. 
  • Predictive analytics allow you to determine what may happen in the future. 
  • Prescriptive analytics help determine the optimal way to respond to future trends 

While it is vital to access historic descriptive and diagnostic analytics, their primary value is to provide historical data to inform predictive and prescriptive analytics. 

Successful cost optimization benefits most from predicting what will happen in the future, allowing companies to adjust cost optimization when needed.  For example, predictive analytics detects changes in demand, demand forecasting, inventory management to determine distribution requirements. 

While these factors alert you to changes needed, they are silent regarding what actions you should take.  Prescriptive analytics offers data-driven guidance as to which of several scenarios best optimize supply chain costs.  Vanguard’s advanced analytics applies an exhaustive range of time-series and model-based methods that capture and extrapolate historical patterns and simulate and compare innumerable outcomes from unprecedented events and circumstances. 

Conclusion 

Recent events, such as COVID-19, trade disputes, and natural disasters, have only served to increase supply chain complexity.  While it may not be possible to predict these events’ outcomes, it is possible to factor them into your scenario planning to create robust yet agile supply chain cost optimization. 

Vanguard Predictive Planning™ (VPP) takes a unique approach to help organizations optimize costs and maximize profits across the enterprise.  VPP enables companies to optimize their end-to-end supply chain process with complete support for unlimited what-if scenarios, collaborate internally and externally with clients and suppliers, and leverage AI in many areas of the platform.  Thus, companies can more realistically optimize decisions and balance their supply chain’s complexities that impact cost, resilience, revenue, service levels, and more.