Product variety and manufacturing complexity in assembly systems and supply chains

This paper explores the implementation of a kanban system within a multi-echelon pharmaceutical supply chain (MEPSC). The article tackles the multi-echelon supply chain problem and the role of collaboration to enhance its performance specifically while using a pull system. The study provides also insights on the different existing Kanban systems that were created to overcome the difficulties and challenges faced by the original kanban system. This paper presents how a kanban system could manage pharmaceuticals inventory and enhance the information sharing through a multi-echelon supply chain. There are two primary findings: a) that the implementation of Kanban system provides a strategic benefit and improves the quality of services and information sharing through a multi-echelon supply chain; b) It also provides a foundation to leverage innovation in pharmaceutical supply chains; it gives the opportunity to the organisation to move away from the current push delivery and logistics systems towards improved logistics strategy models to empower front-line caregivers.

PurposeEasily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.Design/methodology/approachIn total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.FindingsA scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.Originality/valueThe authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

Business executives have recently indicated that supply chain disruptions represent a major threat to their firm’s profitability and revenue growth (Smyrlis, 2006; Hendricks and Singhal, 2009). In the literature, a number of conceptual studies have suggested that supply chain complexity is a main driver of supply chain disruptions (Choi & Krause, 2006; Christopher & Lee, 2004; Juettner et al., 2003). Other studies have suggested that the degree of coupling in the supply chain is a major determinant of supply chain disruptions (Agrawal and Nahimias, 1997; Ahuja, 2000). Building on the theoretical foundation of Normal Accident Theory (NAT), this paper proposes a nuanced relationship between supply chain complexity and coupling and their collective impact on supply chain disruptions. Through an empirical examination, we seek to validate the theoretical proposition that complex, tightly coupled systems are inherently prone to failures or accidents. This study contributes significantly to the supply chain management discourse, providing a detailed analysis of the mechanisms underlying supply chain vulnerabilities and offering practical guidance for developing more resilient and responsive supply chain systems. Our findings invite future research to further explore the intricacies of supply chain disruptions, complexity, and coupling, aiming to refine and expand upon the mitigation strategies discussed herein.

PurposeThe purpose of this paper is to present methods for assessing and mapping the complexity of products and their assembly. In cases of complexity of assembly it is important to consider and model at the product design stages when only data about individual parts/products and their assembly attributes are known. Assessing the complexity of assembly systems, based on the attributes of their components, is an essential step towards designing them for the least complexity.Design/methodology/approachThis paper presents a mapping method between the complexity of products and their variants and complexity of the system needed to assemble them. A method has also been developed to assess and compare the complexity of assembly systems based on the characteristics of their physical components for comparison and re‐design to reduce complexity.FindingsThe complexity dependency matrix estimates the average assembly equipment complexity for a certain product based on the interactions between parts handling, insertion and assembly attributes and assembly system functions. An automobile engine piston, domestic appliance drive, car fan motor and a three‐pin electric power plug products were used to demonstrate the application of the developed methodology.Originality/valueThe developed methods can be used by products and assembly systems designers to identify and alleviate major sources of complexity.

PurposeThe purpose of this paper is to discuss complexity assessment in supply chains, to describe a methodology for measuring supply chain complexity in distributive trade and to illustrate the measurement of supply chain complexity and mechanisms to cope with supply chain complexity in distributive trade.Design/methodology/approachThe paper discusses literature on measuring supply chain complexity and uses five case studies among wholesale companies to illustrate a methodology to measure supply chain complexity and to discuss strategies to cope with supply chain complexity.FindingsThe study confirms the multifaceted nature of supply chain complexity. The paper identifies eight drivers of supply chain complexity and uses these to illustrate the measurement of supply chain complexity in a wholesale environment. The paper identifies six strategies used by wholesalers for coping with supply chain complexity and identifies interrelations between supply chain complexity drivers and these strategies.Research limitations/implicationsThe research is based on case studies in wholesale companies; future research may include survey research, including other sectors to analyze industry differences but may also focus on other parts of the supply chain.Practical implicationsThe study provides insights into how to identify and measure complexity in a supply chain and what can be done to manage supply chain complexity.Originality/valueThe measurement approach is new to supply chain management and is based on multiple drivers of supply chain complexity. The research is focused on wholesalers, which is a segment that has received limited academic attention in supply chain research to date.

Multi-echelon supply chain coordination: Contract sequence and cut-off policies

Today’s greater product variety, shorter product life cycle, and lower production costs are pushing companies to look beyond their own boundaries, thereby, creating complexity in the management of the supply chain. To manage such complexity, it is imperative that the management understand the associated complexity drivers and their interrelationships. This study identified twenty-three drivers responsible for supply chain complexity and classified them by using various criteria. In addition, the study presents a structural model using interpretive structural modelling (ISM) methodology to understand the inter-relationships between one driver to another. The research findings showed that drivers such as customer need, competitor action, and government regulation are beyond the control of supply chain partners, and have found the highest dominance with respect to supply chain complexity. Conversely, drivers related to tactical issues such as production planning and control, logistics and transportation, forecasting error, and marketing and sales are found to be the dependent drivers. Remaining drivers, such as company culture, number of suppliers, product variety, and organisational structure fall between the former two classifications. These drivers are related to strategic issues and require action from the upper level of the management hierarchy.

As the supply chains are growing and becoming more interdependent, the vulnerability and the chances of supply chain failure also increases. The supply chain industry is severely affected due to the COVID-19 outbreak and industry practitioners are focusing on minimizing the ripple effect of the disruption made to the economy. Considering the unprecedented situation, the research is motivated to analyse the ripple effect in a multi-echelon supply chain and investigate the performance at various nodes to understand the capability of the supply chain to withstand the disruptions at different levels. Using discrete event simulation, this study analyses the ripple effect in the copper industry by an agent-based simulation software anyLogistix, considering various key performance indexes (KPIs) to gauge the magnitude. From the results of the simulation, it is evident that the lack of safety stocks and multi-sourcing of copper facilitate as major causes for the disruptions. The simulation helps to understand the disruption levels and make the supply chain more resilient and robust for any future disruption. Further, the study proposes resilient project management solutions to recover from the cascading ripple effect in the copper supply chain. The scientific contribution of the research is to provide supply chain managers with simulation techniques to understand the ripple effect on the copper supply chain. It helps the stakeholders to understand the importance of project management tools to reduce the cascading ripple effect in a copper supply chain. Further, the findings of this study will support contemporary managers, supply chain allies, project managers, and stakeholders to formulate strategies for recovering from the supply chain disruptions caused due to natural disasters, pandemics such as COVID-19.

Accomplishing veritable demand in a timely manner is a true challenge in present business circumstances especially for the fashion products where the demand is to get more varieties in short interval. Managing demand and supply is not that naïve as the supply chain is complicated by the outsourcing trend. This study aims to assess how brands in the fashion industry are managing their demand-supply chains (DSCs) by considering both trendy and regular product. This paper is based on the literature review of demand chain management (DCM) and also supply chain management (SCM) of the fashion industry. Also the research on demand-supply chain management (DSCM) is studied as a scope of demand-supply management in fashion industry. Textile and apparel processing stages are also studied to understand the manufacturing and supply chain complexity. The review identified that the degree of fashion sensitivity adopted by the retail brands influences their supply chain strategy. Fast fashion retailers urge to respond quickly and they need a flexible and responsive supply chain and contrariwise, others do need fast response but more efficiency focus with economy of scale. chains. The demand side aspects and supply side alignment will contribute insights on DSC organization in textile-apparel supply chain. Also textile and apparel manufacturers may have a clearer picture regarding the structure of retailers’ DSCs. The findings may also prove useful for them who are not aligned with the fast track concept, but yet supplying product in timely manner is their top priority. The co-ordination of complex and multidimensional textile-apparel supply chain with individual interest is still a concern and yet to be resolved. The objective of the study is to add knowledge for perceiving the importance of the co-ordination for mutual benefit.

Precisely positioning pallets in multi-station assembly systems

The interrelationships between system components are critical to improving the performance of a complex supply chain system. Thus, any improvement or development can be carried out systemically and comprehensively. The complexity of coordination grows as the number of echelons in a supply chain increases. In practice, coordination becomes more difficult to implement in a supply chain with more echelons. Through demand information sharing, this research attempts to figure out how coordination can have implications for complex multi-echelon supply chains with a modeling approach. The Aggregate Andesite Stone Supply Chain is used as an empirical model with four echelons. Changes in dimensions and values per ton of product in each echelon displacement add complexity. Total holding cost is not the only consideration. The timely completion of projects downstream is also a priority. So the system's behavior that runs and changes over time also needs to be observed. To accommodate this complexity, a system dynamics modeling approach is used. This modeling technique could capture fluctuations in volatile conditions that change in time sequences. The pattern of model behavior shows that demand information sharing in the andesite aggregate supply chain is faint, and the "bullwhip effect" occurs. The demand information sharing can eliminate this effect, reduce up to 73.5 % of total supply chain holding costs, and increase the percentage of project completion on time downstream of the supply chain. These results provide a scientific and practical understanding that although there are many obstacles, demand information sharing can significantly improve performance in multi-echelon complex supply chains and be worthwhile applied

There has been a paradigm shift in the functioning of global supply chains, particularly in thepost-pandemic era. This shift is characterized by the convergence of technology. deployment, geopolitical dynamics, and an increased awareness of environmental and social responsibilities. These transformative changes compel businesses to navigate a complex. web of challenges while simultaneously creating many opportunities for global supply chains. This includes delegating responsibilities to the supplier side, empowering suppliers, and promoting digital supply chain networks. Ongoing trade tensions between major economies, such as the United States and China, have resulted in fluctuations in global logistics patterns. Alterations in tariffs and trade agreements impact shipping routes, transportation costs, and the overall flow of goods. Consequently, supply chains must focus on end-to-end supply chains originating from low-cost sources to withstand unforeseen events like black swan occurrences and geopolitical and trade wars. The pandemic has underscored the critical importance of building resilient supply chains. Many firms reevaluate their supply chain strategies, emphasizing diversification to mitigate risk. This involves reassessing suppliers, exploring alternative sourcing options, and increasing safety stock levels. Geopolitical uncertainties and disruptions have prompted reconsidering global sourcing strategies, with reshoring and nearshoring gaining prominence. Companies seek to mitigate risks associated with distant and complex supply chains by bringing production closer to target markets, enhancing responsiveness, reducing lead times, and fortifying supply chain resilience amid uncertain business conditions. From the traditional supply chain model, there is a continuous effort to integrate information technology to transform value streams and increase processes' transparency. Blockchain is playing a significant role, from tracking the provenance of raw materials to reducing transaction fraud. This technology fosters a new level of trust among supply chain partners, safeguarding against potential disruptions and reinforcing the integrity of the entire supply chain ecosystem. These rapid transformations amid uncertain conditions have prompted a reevaluation of supply chain resiliency with a greater focus on risk mitigation strategies. In global logistics, owning ships and manufacturing/leasing containers for extended periods have become the guiding principles. The recent Brexit has also impacted global transport's European and UK legs. The European industry is grappling with significant shortages of resources, including drivers and trained supply chain manpower, affecting last-mile delivery and land transportation in numerous geographies. The growth of e-commerce has significantly influenced supply chains, leading to innovations in last-mile delivery solutions. Companies are investing in warehouse automation, drone deliveries, and other technologies to meet the demands of the rapidly expanding e-commerce market. x Furthermore, a seismic shift toward sustainability reshapes the global supply chain landscape. Businesses recognize the imperative to address environmental, social, and governance (ESG) concerns. Initiatives to reduce carbon footprints, optimize transportation routes, and embrace ethical sourcing practices are becoming integral components of supply chain strategies. Sustainability is no longer merely a corporate buzzword but a critical factor in ensuring long-term viability and meeting the expectations of socially conscious consumers. Overall, global supply chains are undergoing significant transformation driven by technological innovation, environmental consciousness, and a commitment to building resilient networks. Businesses that embrace these trends are not only adapting to the challenges of today but are also positioning themselves to thrive in the dynamic landscape of tomorrow. The key to success is navigating change, leveraging technology, and building sustainable businesses.

In a multi-stage production/inventory system, customer service level is a function of the service levels at individual stages. The objective of a multi-stage system can be defined in terms of determining the stage-specific service levels, which minimize the total cost of the system subject to achieving a desired customer service level. The literature available so far discusses the service level perspective of multi-stage systems under the continuous review and periodic review policy. In this paper, we consider systems operating under the base stock policy, and derive service level relationships in multi-stage serial and assembly systems. First, we develop models for a two-stage serial system, and illustrate the models with the help of a numerical example. Finally, we propose an extension of the models for multi-stage serial and assembly systems.

Mass customisation — an automotive perspective

Large-scale automated assembly systems are widely used in automotive, aerospace and consumer electronics industries to obtain high quality products in less time. However, one disadvantage of these automated systems is that they are composed of too many working parameters. Since it is not possible to monitor all these parameters during the assembly process, an undetected error may propagate and result in a more critical detected error. In this paper, a unique way of detecting and diagnosing these types of failures by using Virtual Factories is discussed. A Virtual Factory was developed by building and linking several software modules to predict and diagnose propagated errors. A multi-station assembly system was modeled and a previously discussed “off-line prediction and recovery” method was applied. The obtained results showed that this method is capable of predicting propagated errors, which are too complex to solve for a human expert.