ISM and fuzzy logic approach to model and analyze the variables in downstream supply chain for perfect order fulfillment

Abstract

PurposeAs we move up in the supply chain (SC) from retailer to supplier, amplification in the fluctuation of order increased. To minimize this amplification, understanding of key decision variables which affects the SC is essential. So, in the present work the authors developed a novel approach to examine the structural dependencies among variables responsible for perfect order fulfillment (POF).Design/methodology/approachInterpretive structural modeling approach has been used to model the structural relationship among the key SC variables. Further, to study the driver-dependence dynamics among variables MICMAC analysis has been used. In the second phase, the influence of driver variables on the POF is investigated by using fuzzy logic.FindingsFrom the results, it is observed that the variables’ delivery time, number of echelons, data accuracy and information sharing have high driving power which may help the organizations to meet challenges offered by POF. The results showed that for POF is said to be at optimum level when the number of echelons should be low and data accuracy should be high, and information sharing among all partners should also be very high.Research limitations/implicationsResearch on SC is classified into three categories, i.e. operational, design and strategic. In the present study authors discussed strategic variables responsible for POF which is the main limitation of the study. The work can be extended by including operational and design variables.Practical implicationsPOF in SC network is affected by various variables. The in-depth understanding of contextual association among the variables helps the managers to improve the efficiency of the SC and reduce the bullwhip effect across the downstream SC network.Originality/valueThe study presents a hybrid approach to analyze the key POF dimensions, i.e. forecasting, number of echelons, information sharing, cycle time and delivery time, critical to POF in downstream SC network by developing various case settings.

Proposing a framework for analyzing the effect of lean and agile innovation on lean and agile supply chain

“…As the complexity increases it makes it more difficult for the management to monitor and control the performance of their supply chains. This requires the introduction of incremental steps to improve the efficiency of the supply chain (Sharma, 2021). According to literature review indicates that there has been much work done in the area of lean manufacturing, but Lean and agile innovation and supply chain less on lean supply chain (Sharma et al, 2022).…”

Modelling the abstract knots in supply chains using interpretive structural modelling (ISM) approaches: a review-based comprehensive toolkit

“…However, certain researchers neither reported the number of experts engaged nor the type of the group decision process or both (e.g. Wuni and Shen, 2019; Sindhu, 2020; Sharma, 2021 etc.). In order to use the FISM, Joshi et al (2009) thoroughly reviewed the literature, distributed the relevant literature to the experts, carried out the semi-structured interviews of 14 experts from industry and academia and refined the list of initially selected 24 variables into 13 most relevant variables.…”

“…Selected ISM applications in the supply chain management domain include sustainable supplier selection criteria (Chauhan et al , 2020), critical success factors of 5S implementation (Attri et al , 2020), additive manufacturing implementation factors (Sonar et al , 2020), barriers to the implementation of mass customization (Ullah and Narain, 2020), barriers to six sigma implementation (Ali et al , 2020), variables in the downstream supply chain for perfect order fulfilment (Sharma, 2021), supply chain management (SCM) flexibility (Singh et al , 2020a, b), SCM risks (Babu et al , 2020), factors for green supply chain (Goel and Khurana, 2020), green lean practices (Singh et al , 2020a, b), employee turnover in humanitarian logistics (Khan et al , 2020), barriers to the adoption of blockchain technology in supply chains (Mathivathanan et al , 2021; Kouhizadeh et al , 2021), supply chain sustainability (Wan et al , 2021), critical risk factors in sustainable telecom services (Chen et al , 2021), contextual and organizational factors in sustainable supply chain decision-making (Yang et al , 2020) and barriers in reverse logistics (Tiwari et al , 2020).…”

Lean Manufacturing Model to improve the order fulfillment in a company in the wine sector in Peru

“…As stated by [32], "for order fulfillment based on perfect order, it begins through the whole process of consumer acquisition where the factors of on-time delivery, out-of-stock and among other factors are involved". In order to achieve the objectives of a good delivery, we link both technology and information management for the fulfillment of different factors such as time, quantity, quality, and documentation [6] [38]. In order to achieve order fulfillment, strategic planning must be carried out where the capabilities and limitations of the company allow the necessary performance to ensure the requirements of production, transportation, storage, distribution, and marketing to ensure order fulfillment by the interaction of the chain and the behavior of the market in which it must obtain the loyalty needed to ensure a stable demand value [39].…”

“…In order to achieve such order fulfillment correctly, the flow of information must be optimally carried out to begin with, since it will control and inform about the capacity of the facilities, production rate and the quantity that is being handled in the system. This will allow cost reduction and profit improvement if added value is achieved in the process chain [5] [6]. In the second case, the use of Value Stream Mapping (VSM) is proposed, which through the support of other tools such as A3, metrics and/or other methods, like time reduction, higher added value and finally improved order fulfillment are achieved [7] [8].…”