A new rolling forecasting framework using Microsoft Power BI for data visualization: A case study in a pharmaceutical industry.

CONTEXT AND OBJECTIVES: Demand forecasting is a vital step for production planning and consequently, for supply chain efficiency, especially for the pharmaceutical (pharma) supply chain due to its unique characteristics. Numerous models and techniques that are proposed in the literature but little in concrete and generic framework to forecasting process, mainly for pharmaceutical supply chain. Unlike studies in the literature, this study not only perfectly predict the sales of a pharma manufacturer, but also visualize the results via a developed dashboard using modern information technology and business intelligence. MATERIAL AND METHODS: In this research, a rolling forecasting framework comprising of different steps and specialized tools is proposed that can assist supply chain managers to perform an accurate sales forecasting and consequently a better performance and specifically patient satisfaction. The proposed generic framework combines the use of Visual studio C++ software to extract optimal forecasting and the Power BI software to monitor the accuracy of the obtained sales forecasts. Three exponential smoothing methods are integrated in the proposed framework, which is open to adding more new forecasting methods. RESULTS: The proposed framework is tested for many data sets from a pharmaceutical manufacturer company, and the results obtained show superior performance, especially a clear decline in both forecast errors, which can reach 75% and a drop of stock level to 50%. Therefore, the company is currently using it and a future integration with their ERP is being carried out. CONCLUSION: The proposed rolling forecasting framework contributes to insightful decision-making through the visualization of accurate future sales and turnover, and consequently, an efficient stock management and effective production planning.

Integrating Quality Tools and Methods to Analyze and Improve a Hospital Sterilization Process.

Healthcare facilities are facing major issues and challenges. Hospitals continuously search approaches to improve operations quality, optimize performance, and minimize costs. Specifically, an efficient hospital sterilization process (HSP) allows reusable medical devices (RMDs) to be more quickly available for healthcare activities. In this context, this paper describes an integrated approach developed to analyze HSP and to identify the most critical improvement actions. This proposed approach integrates four quality tools and techniques. Firstly, a structured analysis and design technique (SADT) methodology is applied to describe HSP as a hierarchy of activities and functions. Secondly, the failure modes and effects analysis (FMEA) method is used as a risk assessment step to determine which activity processes need careful attention. Thirdly, a cause-effect analysis technique is used as a tool to help identify all the possible improvement actions. Finally, priority improvement actions are proposed using the quality function deployment (QFD) method. To validate the proposed approach, a real sterilization process used at the maternity services of Hedi-Cheker Hospital in the governorate of Sfax, Tunisia, was fully studied. For this specific HSP, the proposed approach results showed that the two most critical activities were (1) improving the coordination between the sterilization service and the surgery block and (2) minimizing the average duration of the sterilization process to ensure the availability of RMDs in time.

Assessment of Seawater Intrusion in Coastal Aquifers Using Multivariate Statistical Analyses and Hydrochemical Facies Evolution-Based Model.

Groundwater (GW) studies have been conducted worldwide with regard to several pressures, including climate change, seawater intrusion, and water overexploitation. GW quality is a very important sector for several countries in the world, in particular for Tunisia. The shallow coastal aquifer of Sfax (located in Tunisia) was found to be under the combined conditions of continuous drop in GW and further deterioration of the groundwater quality (GWQ). This study was conducted to identify the processes that control GWQ mainly in relation to mineralization sources in the shallow Sfax coastal aquifer. To perform this task, 37 wells are considered. Data include 10 physico-chemical properties of groundwater analyzed in water samples: pH, EC, calcium (Ca), sodium (Na), magnesium (Mg), potassium (K), chloride (Cl), sulfate (SO4), bicarbonate (HCO3), and nitrate (NO3), i.e., investigation was based on a database of 370 observations. Principal component analysis (PCA) and hydrochemical facies evolution (HFE) were conducted to extract the main factors affecting GW chemistry. The results obtained using the PCA model show that GWQ is mainly controlled by either natural factors (rock-water interactions) or anthropogenic ones (agricultural and domestic activities). Indeed, the GW overexploitation generated not only the GWQ degradation but also the SWI. The inverse distance weighted (IDW) method, integrated in a geographic information system (GIS), is employed to achieve spatial mapping of seawater intrusion locations. Hydrochemical facies evolution (HFE) results corroborate the seawater intrusion and its spatial distribution. Furthermore, the mixing ratio showed that Jebeniana and Chaffar-Mahares localities are characterized by high SWI hazard. This research should be done to better manage GW resources and help to develop a suitable plan for the exploitation and protection of water resources.