Inventory management is one of the cornerstones of supply chain management as inventory consists of a key contributor in all supply chains. In the supply chain sector, many inventory management-related problems have been broadly investigated and discussed over the years. It is of great importance for the supply chain of a company to make proper decisions when it comes to plan the order quantity or the time of placing an order, the safety stock that should be keep, the optimal location for its warehouse or other related decisions. Further, it is understood that defining “how much to stock” is closely correlated with defining “how much to order”. Hence, each firm follows a specific replenishment policy according to its needs. One categorization among inventory replenishment policies is made with respect to the inventory review interval (IRI). The IRI refers to the frequency of reviewing the inventory to determine when orders must be placed for replenishment. However, the IRIs differs among policies and companies. According to this categorization inventory replenishment policies can be either continuous or periodic. In the continuous review process, the inventory levels are continuously reviewed, and as soon as the stocks fall below a predetermined level (known as the reorder point or reorder level), a replenishment order is placed. This research is conducted from the scope of a specific company’s supply chain, company X that is an international book seller. Company X is currently one of the many clients of Accenture. Company X is currently using an inventory replenishment policy similar to the (s,S) policy. However, company X is in a transition phase aiming to change this current inventory replenishment policy to the so called Min/Max replenishment policy. The Min/Max inventory replenishment policy is based on the EOQ calculation. At this project conducted by Accenture, Macomi, a business consulting firm, provides the simulation capabilities. A discrete event simulation model was built for providing company X with the optimal EOQs of company X’s suppliers for the new replenishment policy by Macomi with the author’s contribution. However, it is unknown how the IRI of the current or the new inventory replenishment policy can influence the supply chain performance of Company X. The author of this thesis built with Macomi a simulation model and adapted it afterwards in order to conduct this research. The author contributed in building the simulation model with Macomi and parametrized it afterwards along with collected data of company X provided by Accenture for her research purposes. From the aforementioned, the aim of this research is to explore the relationship between the IRI and supply chain performance of company X. Hence, the main research question that needs to be answered is: “What are the effects of review intervals on the supply chain performance of company X?” The following sub questions are intended to be answered in order to answer to the main research question presented above. Those are formulated below: 1. Why it is important to focus on the IRI and what theories are relevant regarding the IRI? 2. Which inventory replenishment policies and IRI ranges are relevant to company X’s case? 3. How can supply chain performance be defined and measured, in the case of company X? 4. How can one test the effect of IRIs on supply chain performance of company X? 5. How do supply chain performance metrics behave under different IRIs for company X? First, in order to understand the purpose of inventory management, theoretical drivers of inventory were gathered. This includes the analysis of the currently used inventory model. Also, measures were 13 selected to rate the performance of inventory management practices, the KPIs. This step is achieved by conducting first a literature review. A top down approach is applied for that purpose starting from general notions such as supply chain management and narrowing down step by step in a systemic way to the IRI that is the key concept of this research. Second, an embedded case study strategy was used, specifically company X ‘s case, in order to scope the research. A discrete event simulation model was built for providing company X with the optimal EOQs of company X’s suppliers for the new inventory replenishment policies by Macomi with the author’s contribution. After the model was finished, the author built another version of the model to use it in order to answer the relevant research questions. The simulation model’s main outputs are inventories and service levels expressed in Key performance indicators (KPIs). The author’s model is based on Macomi’s model and is used to conduct this research to identify the impact of the IRI on supply chain performance. Hence, first the conceptual design of the simulation was developed using IDEF0. The conceptual model was perceived as a system. After defining the inputs, controls mechanisms and outputs as seen in the following figure, the system that was perceived as a black box was opened up using the DEMO methodology. DEMO was selected as a suitable methodology in order to map the business processes that are relevant to the inventory management and inventory control of company X. Figure 1: The conceptual design of the simulation presented as a system diagram. Afterwards, the model is implemented in the S3N interface. Before using the simulation model to run the tests, the author verified and validated the model. Subsequently, the design and the execution of experiments followed. After having executed all the experiments, the author proceeded in exporting the relevant KPIs for obtaining the results and for being able to analyze them afterwards, for all the 57 products that were selected from the product list. There are two relevant KPIs: “Inventory final product” and “% Delivered on time versus Requested. As expected, the results showed that both KPI values decline as we move from smaller to bigger IRIs: both the finished inventories and the service levels decrease as we move from smaller to bigger IRIs. However, if one looks at the scale, it is not that strong. Further, looking at both the two different KPI behaviors, they are consistent in their decrease. However, it is noticed that the decrease is not that intense, and hence, the influence of the IRIs is not that big. Moreover, it was observed that as long as 14 the IRIs are fluctuating from 1 day to 1 month, the KPI values do not decrease that significantly. The KPI values drop faster as we move the IRI beyond one month. Hence, valuable recommendations are made from a business perspective and from a scientific perspective as well. For instance, from a business perspective, a recommendation for company X would be to estimate if the Min/Max policy that is proposed to be “a real time” policy is indeed beneficial: Based on the results of this research, it could be stated that the Min/Max policy works efficiently with the low levels of inventory that are kept, but still, the review interval of the inventory does not seem to influence the service level and the inventory KPI. Thus, paying for the real time policy implementation, from the IRIs perspective does not seem beneficial enough since during one month period it is not needed to have a real time observation on what is happening to the inventories. More specifically, the results showed that the influence of the IRIs is not that important when they are less than one month. In other words, there is no need for investing on expensive software that helps company X managing and controlling the replenishment of products in real time because both inventories and service levels decrease with a low rate. From a scientific perspective, a knowledge gap was tackled as there was not much literature found regarding the impact of the IRIs on supply chain performance. Further, this research was scoped for a specific company; company X, in order to answer to the research question “What are the effects of review intervals on the supply chain performance of company X?” Thus, from a scientific perspective, it was not clear how supply chain performance could be influenced by different IRIs. Hence, the author selected a case in order to narrow down the scope of the research. However, limitations that occurred are presented and discussed. Hence, based on the result interpretations and the limitations that are observed, someone in the future could perform another case study to test this model for another company with different supply chain. Nevertheless, one could go further and perform a more fundamental study and not take just one case of company, but perform a more controlled experiment in which he is going to turn all the variables that have been thoroughly discussed during the implementation of the experiments.