7 Key Factors for the Success of Inventory Optimization Initiative

From the most basic to the most advanced organization, Inventory Optimization is a critical goal and one which underpins the effectiveness of the supply chain operations management. Inventory Optimization requires an organization to sustain and maintain the right mix of cost versus service.

In Global organizations, successful inventory management is a complex science that requires modeling across an entire network to determine interdependencies between locations and the calculation of the ideal inventory targets from raw materials through work-in-progress to finished goods; no small undertaking.

Multi-Echelon Inventory Management requires analysis across the full end-to-end (e2e) supply chain to determine what inventory is needed, where it should be held and in what format; balancing at all times commercial requirements with supply. Getting the mix right is difficult and like any plan it requires constant fine-tuning to take account of the many variables that impact the stock. In this article, we will examine inventory optimization processes as part of a structured sequential journey and examine seven of the key factors in delivering the optimal mix of stock.

The start-point for any Inventory Optimization Project is Visibility. The task of determining the correct stock holding relies on the capability to analyze a number of key e2e factors in an integrated way:

- Demand Characteristics: Volume, Value, Volatility, Service Targets

- Supply Characteristics: Supply Reliability, Cadence, Constraints, Interdependencies, Network

- Stock Characteristics: Current SS levels, Strategic Stock Requirements, Storage Constraints, Volume Constraints

This information is critical to enable an understanding of the current and future modeled state and to enable the identification of the proposed inventory model aligned with the theoretical risks and impacts that it will deliver. Getting this information is not always as easy as you might expect. While on the surface it is true to say that in most organizations inventory is a highly visible dimension of Supply Chain performance impacting company accounts and a key reportable metric of business performance. At the granular level, the details of the inventory held and the analytical tools required to determine the optimal stock are often not so advanced.

In complex supply chains with international business activities and processes dispersed geographically and data held in multiple systems, sometimes at different levels of aggregation with no easy way to cross-reference, the challenge of instigating an effective analysis of the current status and future target state is hard to navigate. Inventory is financial and operational, and often the two parts of the organizational systems architecture hold different data that isn’t always easy to link together. Operational systems will tend to hold SKU-level data on quantities but don’t always link to the financial systems. Linking the financial priorities of the business in terms of sales value with the complexity of managing the items logistically is critical to moving into the next step of the process. Data is critical to the process as it underpins key questions crucial to the inventory optimization endeavor such as:

- How critical is the item to the sales performance of the company?

- How easy is the item to forecast and plan?

- How critical is the item to a customer?

Segmentation guides and informs decisions on what level of risk is acceptable to the business, how much safety stock is needed and in what form and where should this be held as well as guiding decisions on operational cycle stock and factory efficiency. At a simplistic level, this may extend merely to an ABC of SKU value, where a more sophisticated classification will take in Volatility, Volume, margin, sales channel characteristics, and other factors, which help to determine stock strategy. Accurate data is the foundation of the analysis and without this, any initiative will flounder as organizations traverse the maturity continuum to support both current and projected stock holding patterns.

In many organizations, the challenge of multiple systems and disparate data sources is overcome through a combination of custom reporting tools, data warehouses, and dashboards. The task is enabling an integrated view of the inventory picture to support the modeling of an optimal future state and the flexibility to use data from multiple systems and platforms and integrate that is key. Digitization and Big Data analytics provide an opportunity to both drive greater automation of this process overcoming some of the sluggishness underpinned by manual/ semi-manual processes. Best-in-class organizations such as Amazon has transformed the paradigm through their use of analytics and predictive modeling, but it should not be overlooked that the foundations of their success are rooted in excellent data management.

Modeling the right profile of inventory to maximize service and minimize cost takes the foundation of data visibility and looks to determine the best balance across the e2e chain. While the optimal model will differ depending on the type of products sold the considerations are similar. The aim is to meet segmented customer service strategies with the right mix of inventory to meet targeted on-shelf availability. Modeling looks at the balance of cost versus service to define the right profile of inventory across the chain and needs to answer a number of key questions:

- What is the optimal on-shelf target availability for each item

- What level of SS is needed to meet the on-shelf availability target taking into consideration the volatility of demand and supply

- What is the correct form (bulk, finished pack, etc) and location for SS across the e2e chain to optimize the cost/service strategy targeted by the organization

- Where should inventory be held and replenished (push/pull boundaries) 

- What is the optimal delivery frequency needed to balance cycle stock holding with factory efficiency

These questions are by no means all of the considerations and other factors will come into play such as shelf life and storage restrictions of the product, lead time, criticality to the end-users (a key factor in Pharma), sourcing strategy, growth profile of the products, potential synergies with other brands, life-cycle stage, etc.

The complexity of the modeling task is to determine an inventory strategy for the entire e2e value network that encompasses the high-level impact of the stock strategy and through that, the detailed system parameters such as safety stock, make frequency, order size, etc. for each SKU It needs to define not just the levels of inventory but also the form and function of inventory and what that means in terms of business drivers and associated risk profiles.

Utilizing new systems capability to drive continuous fine-tuning of the demand signal and how that impacts supply and inventory decisions can speed response and aid operational control, but the configuration of the network remains largely driven by planners and failure to set the correct parameters means simply speeding up the wrong decisions. Systems help in terms of determining the replenishment plans, enabling an analysis of the slow-moving stock, S&OP available to promise, simulation of scenarios, supporting VMI, and automated reordering amongst others. Dynamic real-time multi-echelon inventory optimization tools, modeling software, and potential future innovations through AI will speed the process but are underpinned by the same disciplines and often the same mathematics as a more manual exercise.

This is a task often best undertaken by a specialist group that can take an overview of the entire chain and has the autonomy to make decisions that whilst optimizing the whole will result in changes that within Silos will seem to drive more inventory or potential costs. It is also a specialist activity, different in nature from an operational task and requiring a level of mathematical capability aligned to business insight; skills that are often not always overly prevalent. Done right the model should define not just the levels of targeted stock but also potentially flag opportunities for policy-related decisions such as where postponement could enable a more flexible and agile supply and replenishment strategy e.g. VMI.

For this reason, Structuring your Inventory optimization initiative is crucial and a key determinant of success. In Global corporations where the supply chain is a multi-stage, an international network of nodes and interdependencies this is not easy. The structure requires that decisions be made that align a multitude of different perspectives on stock holding, from Marketing whose concern is ensuring on-shelf availability to the CFO who wants faster inventory turn and less write-off. Likewise defining the scope of inventory management is crucial. The goal is to determine an optimal inventory plan for the e2e chain but the scope is a complex organizational question. Do we approach by business unit, region, product, brand, type of product, or supply site? These are difficult but critical questions and likewise how do the component parts roll up and who makes the final call on what working capital we ultimately hold to meet the business's service targets. Many supply chains are also a conglomeration of internal and external partners. Thus the structure needs to enable decisions to be made that cross over organizational boundaries ratifying what is held and all the partners need to support this.

Starting with the Customer and working back to determine the most appropriate model is the theoretically correct approach that will enable the modeler to make the appropriate recommendation for the supply chain. The scope and the process for finalizing stock decisions and targets are strategically difficult but critical if the model is to translate into action plans for delivery.

Ultimately no amount of sophisticated modeling and analytical tools will succeed unless the execution at the operational level is effective. Foundational Excellence in People, Process and Systems capability needs to underpin the deployment of the model. This involves getting the buy-in and validation of planners who need to own the operational delivery. Lack of the right buy-in or skills needed to execute the proposals will undermine the endeavor. Ultimately whatever level of sophistication the modeling exercise takes or the tools that support the planning systems parameters will need to be updated and whether that is automated or manual Planners must own and be happy with the data.

Planning excellence is often cited as a major challenge in impacting inventory performance. Undeveloped processes need to be addressed and fixed as part of the ongoing management of stock. Lack of accurate data, inventory recording, role clarity, scheduling tools, and the right measures will likewise quickly disrupt inventory performance as will the lack of key processes such as S&OP which acts as governance for decisions impacting inventory strategy and a monitoring body for the status of stock holding versus targets.

Just as the modeling exercise can be approached from a multitude of dimensions, so too the measurement of performance; ensuring alignment and common buy-in is as critical as designing the right KPIs to monitor progress. Financial KPIs such as Stock Turns, % of revenue and Write-offs need to align with Fill/ Stock out rates, Slow Moving and Obsolete stock, forward days cover, etc. to build an integrated picture of where the organization is and drives targeted interventions when targets are missed/ tolerances breached. Again S&OP is a forum where interrelated business activities such as demand changes or supply issues can be properly assessed in terms of their impact on inventory strategy and the appropriate business response to that.

Inventory Optimization looks to define the right mix of inventory to meet the targeted service goals of the organization. The process needs to build from the detailed level upwards aligning parameter recommendations at the item level with aggregate goals for working capital reduction and improved customer service. Modeling this over a complex network and gaining traction is critical to driving the execution and measurement of the task is not easy but the rewards in terms of improved performance are big. Aligning the steps will be different depending on the organization, however, once the plan is in place and agreed one final key enabler should be the same. Move fast and measure performance aggressively and mobilize the organization to enable the benefits to flow.

About the Contributors:
Joy Taylor-CEO, TayganPoint Consulting Group

David Evans-Director, Greedy Lemon Consulting
About the Author and Editor:
Ben Benjabutr is the author and editor of Supply Chain Opz. He holds an M.Sc. in Logistics Management with 10+ years of experience. You can contact him via e-mail or Twitter.