Inventory Optimization Part 2

As outlined earlier, the building blocks of robust Inventory Optimization lies in efficient Stock-to-Service models, at the SKU-Location levels. From these models, dynamic differentiation of service levels within defined limits are ensured through Mix Optimization, which can automatically distribute service levels among competing SKU’s.

Doing Mix Optimization with a single echelon view is inefficient. Through Stage Optimization, even intricate and extensive networks can be efficiently evaluated and optimized to maintain stocks upstream for deployment when needed or to establish greater buffers closer to the market. Achieving this upstream-downstream dynamic balancing, demands careful consideration of various tradeoffs at the network level, and the solution should be capable of recommending the optimal Service Level to maintain at each node or tier within the network.

Additionally, Lot Size Optimization should empower to flag items with excessively high lot sizes in use, facilitating discussions and negotiations with relevant stakeholders. This optimization capability calculates the optimal batch to be moved and can be triggered within the automatic run, facilitating the handling of economically advantageous quantities, with the flexibility to assign minimum quantities as needed.

Postponement strategies play a crucial role in sizing and positioning inventory buffers within Bill of Material chains, regardless of whether they consist of simple Component and Finished Product arrangements or more complex configurations involving multiple Semi-Finished products.

All the above mentioned capabilities are integral part of true optimization, enabling businesses to facilitate informed decision-making and foster collaboration among Sales, Supply Chain, and Finance teams. By leveraging shared information, these teams can establish Inventory Policies and seamlessly implement agreed-upon working capital strategies into the model, translating high-level information into specific Service Levels and Safety Stocks for every Item at each Location.

Such translations from Strategic to Tactical and Operational levels require robust statistical modeling capabilities. It is important to note that this multi-functional and top-to-bottom alignment should not be confused with the standard process enablement tools like S&OP and their outputs, which typically remain at the tactical level and lack efficient translations of strategic or tactical intent into granular outputs.

In summary, inventory optimization has evolved from being perceived as an art to a well-established science supported by statistical modeling. Previously, it was considered more of an art due to the lack of proper analytical tools. Traditional probability functions such as Normal, Log-Normal, Poisson, or Exponential are not continuous in their recommendations. They often suggest higher inventory norms at higher service levels, extremely low norms at lower ranges, and notably, no norms within certain service level ranges.

Furthermore, these constrained models and algorithms typically function at a single location (Single Echelon) level and employ complex formulas that require extensive pre-processing to calculate inventory norms accordingly. However, deploying such methods independently across the distribution network often yields impractical inventory recommendations. This is mainly due to the inability to account for the interdependencies and tradeoffs among various node locations in single-echelon calculations, underscoring the importance of Mix, Stage, and Lot Optimization capabilities. The disparities in recommendations between single-echelon and multi-echelon inventory strategies can be significant, sometimes exceeding even 60%. All these limitations have contributed to the perception that Inventory Optimization is more of an art than a science.

True inventory optimization enables highly automated management of vast supply chains encompassing numerous SKU-Location combinations, ensuring consistent service levels with optimal stock investment throughout the network and also along the Bills of Material.

In today's dynamic business environment, inventory optimization must be a proactive and recurring practice rather than a static annual activity. With robust software solutions, conducting ongoing optimizations is now easily achievable. With a reliable tool, businesses can streamline this process, performing optimizations in just an hour or two. This agility ensures supply chains remain responsive, predictive, and service-driven to meet the ever-changing demands of the market.