Digital Inventory Optimization: A Practitioner's Guide to Transform Your Organization

Digital Inventory Optimization: A Practitioner's Guide to Transform Your Organization

Andreas Werner (KPMG LLP, USA)
DOI: 10.4018/978-1-5225-7700-3.ch010


This chapter provides an overview of select inventory optimization (IO) techniques for single and multi-echelon optimization. The main goal is to familiarize the reader with various IO models by providing a clearly structured approach, improving the reader's understanding of the mathematical concepts, and by providing an ample number of examples. Furthermore, the guaranteed service model for a three stage serial supply chain is introduced to show the effects of keeping inventory at different echelons in the supply chain in regards to total cost. Lastly an inventory planning maturity model is presented to show actionable next steps to the practitioner.
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Inventory reduction remains one of the key targets of companies to reduce working capital and its associated costs which leads to improved cash flow. At the same time customers are demanding higher service levels and increased convenience for example shorter lead times. To achieve these seemingly conflicting objectives is a great challenge for many companies on their digital journey, however, inventory optimization (IO) has proven that it can achieve an increase in customer service while reducing safety stock (Farasyn et al., 2011). Surprisingly, most companies are not yet taking advantage of inventory optimization, especially of multi-echelon inventory optimization (MEIO). Only 21% of consumer goods companies surveyed reported that they perform multi-echelon inventory optimization to some extent (Romanow, 2014). Another study found that a MEIO implementation can increase service levels by 3% while reducing the cash-to-cash cycle by 15% (Aberdeen group, 2012).

The main value proposition of the following chapter is providing the reader and supply chain practitioner an overview of the IO basics including mathematical concepts which enables them to understand important details for a digital transformation journey. MEIO is the focus of the main chapter since the author feels that the greatest opportunity for many companies exists in this area. The target audience for this chapter is knowledgeable in supply chain management and undergraduate level statistics but not an expert in inventory optimization. Most existing literature can be categorized into either of the following.

  • Academic articles focused on the mathematical aspect of inventory optimization, e.g. operations research

  • Articles focusing on the qualitative aspects of inventory optimization, e.g. defining the approaches, best practices and laying out the observed benefits of an inventory optimization implementation

This chapter seeks to strike a balance between both worlds and is structured in the following way. Firstly, select literature is reviewed that provides the foundation to understand the further sections. After that, the main section explains the mathematical background of single- and multi-echelon inventory optimization. The goal of which is to provide the mathematical basics and an understanding of the general IO concepts and common formulas. After this, the context of inventory optimization within the overall supply chain planning function is explained to help the reader understand the big picture and the fit of IO within their organization’s supply chain functions. Lastly, a maturity model for inventory planning is introduced to enable the reader to articulate the gaps and opportunities within their organization and help with the next steps.



The following will provide a literature review on select articles that the author deems important to understand the main section of this chapter. Table 1 shows an overview of the sources reviewed in the literature review alongside for their reasoning of being included in the background section.

Table 1.
Literature reviewed
Literature reviewedCategoryReasoning
Farasyn et al. (2011) Case study of inventory optimizationQualitative study to showcase results of single- and multi-echelon inventory optimization.
Graves and Willems (2003) Mathematical modeling of MEIOComparison of MEIO techniques: GSM and SSM. Includes details of GSM that are used later in the chapter. Also explains solving the supply chain configuration problem which simultaneously makes sourcing and inventory decisions
Klosterhalfen, Minner and Willems (2014) Mathematical modeling of MEIOExample of an extension to the GSM model (dual supply) and its benefits
Eruguz et al. (2016) Mathematical modeling of MEIOOverview of various GSM models

Key Terms in this Chapter

MAD: Mean absolute deviation.

OTIF: On time in full. It represents a KPI for delivery performance and is typically calculated by dividing the number of deliveries delivered on time in full by the total number of deliveries.

Stage/Node: A stage in the supply chain. It can represent a manufacturing operation or a warehousing process.

GSM: Guaranteed service model.

MEIO: Multi-echelon inventory optimization.

RCCP: Rough-cut capacity planning.

CpG: Consumer packaged goods.

S&OP: Sales and operations planning.

IO: Inventory optimization.

Arc: Connection between stages/nodes. It can, for example, represent a relationship defined in a bill of material or a transportation network.

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