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The purpose of this paper is to identify the accurate cause and effect relationships among strategic objectives and also to demonstrate how decision makers can be guided in the process of defining quantitative strategic target values in the framework of balanced scorecard (BSC) and performance measurement system.

Based on the proposed method in this research, after determining strategic objectives and developing an initial strategy map according to decision makers’ opinions, simultaneous equations system (SES) was used to determine the significance of the relationships among strategic objectives in higher perspectives of the BSC and corresponding strategic objectives in lower perspectives. Afterward, desirable values for performance measures were determined based on the equations and relationships obtained through SES and were optimized by goal programming method.

By applying the proposed method, a clearer picture of the associations among strategic objectives is obtained and the influence of strategic objectives on one another is determined. Afterward, optimal values for strategic objectives are determined to achieve the organization’s goals.

This paper proposes a framework for constructing a strategy map and setting quantitative targets in the framework of BSC. Indeed, this paper presents a case study to demonstrate the applicability and effectiveness of the proposed approach. However, SES technique requires a greater amount of data to generate more accurate results. Although the advent of the Information Age has forced organizations’ decision makers to provide sufficient information and data for business analysis, the data requirements are met.

The presented quantitative approach is a supporting approach for improving decision makers’ opinions and enabling them to reach a more accurate picture of the relationships, valuing strategic objectives and achieving strategic goals. This research also presents a case study to demonstrate the applicability of the proposed approach. The application and implication of the proposed method in banking services show that the contributions of the paper are not only theoretical, but also practical.

The proposed method provides a novel approach for determining the most appropriate targets and applies a comprehensive and scientific model together with decision makers’ opinions and experiences and has two main contributions: first, the associations among strategic objectives are investigated and obtained in an effective way by conducting the SES for the first time in the framework of BSC. Second, quantitative targets have been determined to help in achieving the long-term goals. This task has been accomplished through a combination of SES, the three-stage least squares regression analysis and optimization by using weighted goal programming method.

This study addresses resilient mixed supply chain network design (SCND) and aims to minimize the expected total cost of the supply chain (SC) considering disruptions. The capacity of facilities is considered uncertain. In order to get closer to real-world situations, competition between SCs is considered.

A two-stage stochastic programming model is developed for designing the SC network. The location of facilities and selection of suppliers are considered first-stage decisions, and the determination of materials and products flows are second-stage decisions. Some resilience strategies are applied to mitigate the negative impacts of disruptions.

The results indicate that considering resilience and applying the related strategies are vitally important, and resilience strategies can significantly improve the SC objective and maintain market share. Also, it is confirmed that unrealistic decisions will be made without considering the competition.

This study contributes to the literature by proposing a novel mathematical model for the resilient mixed SCND problem. The other contribution is considering the chain-to-chain competition in collecting returned products and selling recycled products to other SCs in a mixed SC under disruptions. Also, a novel hybrid metaheuristic is developed to cope with the complexity of the model.

The underlying purpose of this paper is to propose a comprehensive framework evaluating the performance of business units of an organization with a process perspective, identifying the most influential performance indicators, enabling managers to make more informed decisions based on data recording every day in their operational information systems.

For proposing the conceptual framework of performance evaluation a synchronized analysis of selected process' data, obtained from an integrated information system of an Iranian chain store, was performed.

The superiority of the proposed framework results is demonstrated in comparison to applying the process mining solely; principal component analysis was identified as an efficient link between process mining and data envelopment analysis. Also, based on the final data analytics, the units' throughput times and the variety of brands and suppliers had the most impact on their performances.

The data of abundant business units and performance indicators, which would have allowed adding data prediction and other data analytics techniques for more insight, was not able to be accessed.

Organizations' managers can use the framework to evaluate their business units' current status and then prioritize their resources based on the most influential performance indicators for overall improvement.

The study contributes to the research on performance management and process mining by presenting a comprehensive framework with two levels of data analytics. It stresses discovering what is happening in business units, and how to prioritize their improvement opportunities learning the significant correlations between performance indicators and units' performance.

The current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.

The constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.

The proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.

Introducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

The purpose of this paper is to propose a quantitative methodology for setting targets in the framework of Balanced Scorecard (BSC) in order to achieve vision and goals.

Response Surface Methodology is proposed to find the significant relationships that should be included in the strategy map and the optimal values of performance measures are assessed by using the desirability function-based approach of RSM. The proposed method was created by reviewing the existing literature, modeling the problem, and applying it in an oil company. In fact, RSM is used to execute the design matrix, analyze the collected data, extract models, analyze the results, and optimize the procedures that generate multiple responses.

By applying this methodological design, a clearer picture of the relationships between strategic objectives is obtained and the influence of strategic objectives on one another is determined. Afterward, optimal values for performance measures are determined.

This paper proposes a framework for constructing a strategy map and setting quantitative targets to translate the goals and strategies into corresponding performance measures and targets. Also, this paper presents a case study to demonstrate the applicability and effectiveness of the proposed approach. However, RSM-based techniques require a greater amount of data to generate more accurate results. Although the advent of the Information Age has forced organizations’ decision makers to provide sufficient information and data for business analysis, the data requirements of RSM-based techniques are met.

In practice, the process of setting targets for performance measures can be challenging in terms of reaching a consensus between managers and decision makers. The findings of this paper can offer a new approach for performance evaluation based on the BSC which allows the organization’s decision makers to reach a more accurate picture of the relationship model between organization goals and those objectives within the BSC. It also demonstrates how decision makers can be guided in the process of defining performance target values in the BSC method.

Reviewing the literature on setting quantitative targets within the framework of the BSC showed no prior study in which RSM is used. This approach has two main contributions: the associations among strategic objectives are investigated and obtained in an effective way which analytically identifies the direction and degree of the relations among the performance measures. Considering the performance evaluation structure based on the BSC, quantitative targets have been determined to help in achieving the long-term goals of the organization. The application of the proposed method in a company showed that the contributions of this research are not only theoretical, but practical as well.

Supply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs a resilient retail supply chain network for perishable foods under the dynamic competition to maximize retailer's profits.

A two-stage mixed-integer non-linear model is presented for designing the supply chain network. In the first stage, an equilibrium model that considers the characteristics of perishable foods is developed. In the second stage, a mixed integer non-linear programming model is presented to deal with the strategic decisions. Finally, an efficient memetic algorithm is designed to deal with large-scale problems.

The optimal the selection of suppliers, distribution centers and the order allocation are found among the supply chain entities. Considering the perishability of agri-food products, the equilibrium retail price and selling quantity are determined. Through a numerical example, the optimal inventory period under different maximum shelf life and the impact of three resilient strategies on retailer's profit, selling price and selling quantity are analyzed.

As for future research, the research can be extended in a number of directions. First, this paper studies the retail supply chain network design problem under competition among retailers. It can be an interesting direction to consider retailers competing with suppliers. Second, the authors can try to linearize the non-linear model and solve the large-scale integer programming problem by exact algorithm. Finally, the freshness of perishable foods gradually declines linearly to zero as the maximum shelf life approaches, and it would be a meaningful attempt to consider the freshness of perishable foods declines exponentially.

This paper innovatively designs the resilient supply chain network for perishable foods under dynamic competition. The retailer's dynamic competition and resilient strategies are considered simultaneously when designing supply chain network for perishable foods. In addition, this paper gives insights into how to obtain the optimal inventory period and compare the retailer's resilient strategies.