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Cost prediction can provide a pharma supply chain industry with completing their projects on schedule and within budget. This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its importance, and we applied the Feature Weighting approach to analyze their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a more substantial influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

A pharmaceutical supply chain (PSC) is a system of processes, operations, and organisations for drug delivery. This paper provides a new PSC mathematical cost model, which includes Blockchain technology (BT), that can improve the safety, performance, and transparency of medical information sharing in a healthcare system. We aim to estimate the costs of the BT-based PSC model, select algorithms with minimum prediction errors, and determine the cost components of the model. After the data generation, we applied four Supervised Learning algorithms (k-nearest neighbour, decision tree, support vector machine, and naive Bayes) combined with two Evolutionary Computation algorithms (ant colony optimization and the firefly algorithm). We also used the Feature Weighting approach to assign appropriate weights to all cost model components, revealing their importance. Four performance metrics were used to evaluate the cost model, and the total ranking score (TRS) was used to determine the most reliable predictive algorithms. Our findings show that the ACO-NB and FA-NB algorithms perform better than the other six algorithms in estimating the costs of the model with lower errors, whereas ACO-DT and FA-DT show the worst performance. The findings also indicate that the shortage cost, holding cost, and expired medication cost more strongly influence the cost model than other cost components.

PurposeThe purpose of this paper is to design a model in order that government hears citizen voices and welcomes people’s suggestions to improve its performance, it may be regarded as Management By People’s Suggestions.Design/methodology/approachAfter designing a conceptual model, collected data through the responses of 451 questionnaires from nine areas of Iran Territory Development Plan were analyzed using the structural equation model and an ANOVA.FindingsThe results indicate three directions are initiatives for the action of using citizens’ opinions to improve government performance. The directions are as follows: the direction of government strategies moving toward citizens’ needs; the direction of developing a seamless, integrated, coordinated, relationship between citizens and authorities; and the direction of centralizing citizens’ suggestions and criticisms.Practical implicationsThis study suggests an approach using citizen feedback to improve government performance in Iran where it appears to be signs of a gap between people and government.Originality/valueThis paper reveals that, before any effort for citizen relationship management, investigations into effective directions which affect citizen–government relationship seems essential.

Purpose The purpose of this paper is to design a model in order that government hears citizen voices and welcomes people’s suggestions to improve its performance, it may be regarded as Management By People’s Suggestions. Design/methodology/approach After designing a conceptual model, collected data through the responses of 451 questionnaires from nine areas of Iran Territory Development Plan were analyzed using the structural equation model and an ANOVA. Findings The results indicate three directions are initiatives for the action of using citizens’ opinions to improve government performance. The directions are as follows: the direction of government strategies moving toward citizens’ needs; the direction of developing a seamless, integrated, coordinated, relationship between citizens and authorities; and the direction of centralizing citizens’ suggestions and criticisms. Practical implications This study suggests an approach using citizen feedback to improve government performance in Iran where it appears to be signs of a gap between people and government. Originality/value This paper reveals that, before any effort for citizen relationship management, investigations into effective directions which affect citizen–government relationship seems essential.