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Created at 6am, Apr 19
SplinterMedicine
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Enhancing Pharmaceutical Cold Supply Chain: Integrating Medication Synchronization and Diverse Delivery Modes
Contract ID
IMUTpZTD79UxWPx2OKVDNzeim4oUZSq41VCJdUeR1To
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PDF
Entry Count
138
Embed. Model
jina_embeddings_v2_base_en
Index Type
hnsw

The significance of last-mile logistics in the healthcare supply chain is growing steadily, especially in pharmacies where the growing prevalence of medication delivery to patients' homes is remarkable. This paper proposes a novel mathematical model for the last-mile logistics of the pharmaceutical supply chain and optimizes a pharmacy's logistical financial outcome while considering medication synchronization, different delivery modes, and temperature requirements of medicines. We propose a mathematical formulation of the problem using Mixed Integer Linear Programming (MILP) evolved from the actual problem of an outpatient pharmacy of a Dutch hospital. We create a case study by gathering, preparing, processing, and analyzing the associated data. We find the optimal solution, using Python MIP package and the Gurobi solver, which indicates the number of order batches, the composition of these batches, and the number of staff related to the preparation of the order batches. Our results show that our optimal solution increases the pharmacy's logistical financial outcome by 34 percent. Moreover, we propose other model variations and perform extensive scenario analysis to provide managerial insights applicable to other pharmacies and distributors in the last step of cold supply chains. Based on our scenario analysis, we conclude that improving medication synchronization can significantly enhance the pharmacy's logistical financial outcome.

In the realistic improvement scenario, we collaborate with pharmacy management to assess feasible synchronization enhancements. For instance, a patient who currently orders three times in the time horizon may reduce their orders to two times. The average synchronization level for this realistic scenario is 87%. In the ideal improvement scenario, all patients order only once throughout the time horizon, resulting in 100% synchronization. To assess these adaptations, we adjust the parameter p based on the specified synchronization level.
id: 77f335b6ea104269b750eb4efdc26f70 - page: 12
Thirdly, we consider an adaptation in the patient types served by the pharmacy. Currently, the MA serves all patients from the SMK or related hospitals who wish to order medication at the MA. However, the hospital is exploring a scenario where they only cater to patients with medication type k0, which refers to medication without a prescription line fee. These are medications that cannot be obtained from other pharmacies, meaning patients are required to order them exclusively from the MA. Patients with other medication types (k1), which include medications with a prescription line fee, have the freedom to visit their local pharmacy if they prefer. This scenario has an impact on parameter p as patients who do not have any medications of type k0 in their prescription are excluded, resulting in their p value being set to 0. Consequently, this adaptation decreases the total number of patients, leading to a reduction in the sum of p values, denoted as (cid:80) The final adaptation involves
id: abb85e7a21576dc8a6f533a0b36f2e02 - page: 12
Presently, the MA allows patients to order their medication for a maximum period of 4 months. However, there is an expectation that costs will decrease if the time horizon is extended to 6 months. This adaptation does not have any impact on the input parameters but rather expands the number of elements in set W from 4 to 6.
id: a5ec9e56c58480bc8183fced233726ed - page: 12
A total of 24 scenarios are formed by combining these four adaptations: two options for the number of patients, three options for the synchronization level, two options for the patient types, and two options for the order period. Table 13 provides a summary of the scenarios, including the total annual LFO for each scenario. Scenario 0 represents the base case analyzed in Section 3.2.
id: 66ad6b7e0a94055f8a50daf1f2c7f5fe - page: 12
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