Inventory management optimization
Proposal for a data-based replenishment concept
In the course of deregulating the pharmacy market, a proposal for optimizing inventory management was developed for a large Scandinavian pharmacy chain. Increasing competitive pressure from private providers led to declining margins and made it necessary to redesign the merchandise planning process.
The aim of the project was to design a robust replenishment concept that reduces inventories while ensuring high delivery capability to end customers. Based on real demand and inventory data, existing scheduling practices were analyzed, alternative approaches developed, and validated using simulations.
initial situation
The starting point for the project was the liberalization of the pharmacy market in Sweden, which abolished the state monopoly on pharmacies and allowed private providers to enter the market. This led to new competitors entering the market, which resulted in increased price pressure and declining margins for established providers. Against this backdrop, optimizing internal cost structures became a key area of focus, particularly in merchandise planning.
The focus was on inventory management, as it has a significant impact on both capital commitment and the ability to deliver to end customers. Real historical inventory and demand data from a large Scandinavian pharmacy chain was available as a data basis, enabling a quantitative analysis of existing inventory management practices.
The project was carried out as part of a university consulting role-play. The underlying problem, the database, and the structure of a request for proposal were based on a real project from the retail and distribution sector. The aim was to develop a well-founded proposal for a data-based replenishment concept under these conditions and to quantitatively examine its impact.
Procedures and methods
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The project was initiated as part of a university consulting role-playing game that simulated a realistic tendering and competitive environment. The students worked in groups as independent consulting firms competing for a project contract within the framework of a structured RFP.
At the outset, the client presented the initial situation, including key targets such as the desired level of service, perceived cost drivers, and existing scheduling practices. In addition, there was an opportunity to clarify operational, organizational, and economic conditions in a structured Q&A session, covering topics such as demand behavior, branch structure, personnel costs, and existing ordering processes.
This phase primarily served to precisely define the problem, clarify assumptions, and determine the focus of the project, and formed the methodological basis for the subsequent development of a robust solution proposal.
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Based on the findings from the RFP phase, a data-driven replenishment concept was developed and elaborated in the form of a structured proposal. First, relevant literature approaches to inventory management, ordering policy, and service level control were analyzed and reviewed for their applicability in the given context.
Based on this, a proprietary replenishment concept was developed that is based on data-driven determination of order points and order quantities and provides for automated daily order recommendations for each store. The goal was to replace the existing manual scheduling process, reduce personnel costs, and at the same time systematically address the conflict of objectives between inventory levels and delivery capability.
In addition to the conceptual approach, the proposal also included a quantitative assessment of the expected effects, a structured presentation of the operational implementation, and an economic evaluation. It thus provided a sound basis for decision-making regarding the selection and further pursuit of the concept.
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After submitting the proposal, the developed replenishment concept was validated in a simulation-based proof of concept (PoC). The aim of this phase was to quantitatively verify the effectiveness of the concept and compare it with historical scheduling practices.
To this end, a simulation environment was implemented in Python, which reproduced the real past using historical demand and inventory data. The developed replenishment concept and the historical scheduling logic were subjected to identical demand curves, enabling a consistent A-B comparison.
The simulation enabled a systematic analysis of key performance indicators such as service level, inventory levels, and ordering behavior. The results of the PoC formed the basis for evaluating the concept and were finally presented and discussed as part of a role-play exercise.
solution concept
The solution concept describes the design of a data-based replenishment service to support operational merchandise planning. The goal was to develop a standardized, scalable concept that could be integrated into existing ordering processes and contribute to the long-term improvement of inventory levels and delivery capabilities.
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The replenishment concept is based on an ABC categorization of the product range. Appropriate ordering strategies are used for the different product segments in order to reduce inventories while ensuring high delivery capability.
Since each branch has its own warehouse, scheduling is done individually for each branch. The underlying logic is identical for all branches, while location-specific parameters such as demand profiles or delivery costs vary.
This separation of logic and parameters allows the concept to be scaled consistently across a large branch network without the need for branch-specific special solutions.
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The introduction of the replenishment concept was designed as a step-by-step process in order to minimize risks and verify its effectiveness in a controlled manner. The first step was a proof-of-concept phase, in which the concept was validated using simulations based on historical data.
Based on this, a pilot phase was planned in selected branches to test practical feasibility and make operational adjustments. Following a successful pilot, the concept was to be rolled out gradually across the entire branch network.
Thanks to standardized scheduling logic and parameterized configuration for each branch, the rollout is scalable and can be implemented with manageable organizational effort.
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The pricing and revenue model followed a value-based approach. The decisive factor was not the implementation costs, but rather the economic benefits achieved through reduced inventories, lower process costs, and improved delivery capabilities.
The initial project output was designed so that the investment would pay for itself within a year. Subsequent support and further development of the replenishment service was covered by ongoing license and support agreements.
The model thus combines short-term economic efficiency with long-term stability, enabling low-risk implementation and sustainable operation of the concept.
Results and added value
As part of the proof of concept, the developed replenishment concept was compared with historical scheduling practices using simulations. Both scenarios were based on identical demand and article data and differed only in the scheduling logic applied. The developed concept showed the following effects:
Stabilization of service levels upon achievement of defined business objectives
Reduction in average inventory levels
Lower inventory volatility due to more consistent order quantities
Reduced operational personnel costs through automated scheduling logic
The results show that a data-based replenishment concept can achieve significant economic effects while significantly reducing the operational effort involved in scheduling. The PoC thus not only provided quantitative proof of effectiveness, but also a reliable basis for management and investment decisions. The project shows how a clearly formulated problem and real operating data can be used to derive a coherent proposal with measurable economic benefits.