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

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.

Perfect Order and Its Components: Application for Deliveries of Fast Moving Consumer Goods to Retail Stores

The perfect order concept is traditionally used to measure logistics performance, but the conventional assessment of the perfect order from the perspective of the “in-time, in-full and error-free” trinity is no longer viable today. This paper aims to make a modest contribution to creating a novel understanding of the perfect order concept. Based on the primary survey in 132 Czech retail stores, the authors identify the key components that constitute a perfect order fulfilment of fast moving consumer goods to retail stores, namely reliability, collaboration, extra adaptation, ordering, and promotion. Subsequently, it was discovered that there are the differences in the influence of the identified components depending on the retail store type. Retail stores in the sense of drugstores regard extra adaptation and promotion as being the more important components compared to retail stores specialising in food products. Independent retail stores regard collaboration and promotion as being much less important components compared to retail stores that form part of a retail chain. Ordering is a more important component for small retail stores compared to large retail stores. Retail stores located in a capital city and in towns representing the centres of individual regions regard collaboration and extra adaptation to be the more important components compared to retail stores located in small towns. Research results support perfection in measuring logistics performance and make it possible to improve retail store operation.

The comparative analysis of global pandemics of two respective genre

The ongoing global pandemic that has been demolishing every aspect of humankind is truly unprecedented. The mankind experienced the variety of catastrophe since last few centuries, however, this deadly epidemic is extremely unique. This is not restricted to a particular geographical periphery, more importantly, it is not ethnicity dependent. If we could revert back, the last global epidemic of such proportion that is Spanish Flu (1918 H1N1 influenza pandemic) had plenty of similarities with the ongoing disaster in terms of the prevalence across the globe, epidemiology and associated attributes. Scientific fraternity across the world is trying the heart out to depict the origin of this deadly disaster but to say the least, there has been more argument than settlement. However, the most crucial part that coincidentally blends both the epidemics in a perfect order is the infodemic that without a shadow of doubt is the most staggering obstacle to deal with. In this paper a comprehensive effort has been put forward to illustrate the comparative analysis between the global pandemics of two respective genre. At the same time, the best possible lay outs have been also discussed to overhaul the ongoing crisis.

Perfect order fulfillment in construction supply chain performance

The problem of construction supply chains is the fragmentation of unsustainable projects such as manufacturing. Therefore it is necessary to have supply chain performance indicator standards related to the procurement of materials that can be used as a reference for the performance of a project or contractor. The purpose of this study is to measure the performance of the construction supply chain with the perfect order fulfillment (POF) indicator adopted from the Supply Chain Operations Reference (SCOR) 12.0. The adoption stage starts from the definition in construction to the calculation of indicators in the project. The results showed that suppliers of steel and ready mix concrete at close range from the project had better performance. In the case study, the perfect order fulfillment performance of steel suppliers with a distance of 345 km is worth 89.476% while suppliers with a distance of 540 km are worth 87.535%. The supplier of ready mix concrete within 7.3 km is worth 95.355% and the supplier with a distance of 15.7 km is 91.705%.