Shorter Product Life Cycles Research Articles

Crystallization and Aging Behavior of Polyetheretherketone PEEK within Rapid Tooling and Rubber Molding

In times of short product life cycles, additive manufacturing and rapid tooling are important methods to make tool development and manufacturing more efficient. High-performance polymers are the key to mold production for prototypes and small series. However, the high temperatures during vulcanization injection molding cause thermal aging and can impair service life. The extent to which the thermal stress over the entire process chain stresses the material and whether it leads to irreversible material aging is evaluated. To this end, a mold made of PEEK is fabricated using fused filament fabrication and examined for its potential application. The mold is heated to 200 °C, filled with rubber, and cured. A differential scanning calorimetry analysis of each process step illustrates the crystallization behavior and first indicates the material resistance. It shows distinct cold crystallization regions at a build chamber temperature of 90 °C. At an ambient temperature above Tg, crystallization of 30% is achieved, and cold crystallization no longer occurs. Additional tensile tests show a decrease in tensile strength after ten days of thermal aging. The steady decrease in recrystallization temperature indicates degradation of the additives. However, the tensile tests reveal steady embrittlement of the material due to increasing crosslinking.

Product allocation and network configuration in global production networks

Driven by an increasing demand for individualized products and shorter product life-cycles, companies continuously extend their product portfolio. Simultaneously, companies expand into new markets to reach customers and to exploit varying location factors to reduce costs. Global production networks (GPNs) have to be adapted constantly to react to new circumstances and changes in the demand of products. To remain competitive, product allocation and production network configuration are essential. At the same time, companies face an increasing complexity while handling these tasks. This poses a challenge particularly for small and medium sized companies, which have limited planning capacities and management resources. Current literature describes optimization-based approaches for the integrated product allocation and network configuration of production networks. Yet, multi-objective models lack transparency of results and user friendliness. Therefore, this paper presents a multi-objective optimization model that incorporates flexibility and reconfiguration aspects to determine an optimal product allocation and network configuration of a GPN over a given planning horizon. The preemptive goal programming approach is used to identify Pareto-optimal solutions and to increase user friendliness. The subsequent verification, validation and post-optimality analysis combined in a structured process enables a wide range of companies to apply the approach. The model is successfully applied in the GPN of a special machine manufacturer, which produces high precision metrology machines. Due to its transparent approach for complex planning problems, the developed method provides a solid base for well-founded, objective decisions. Hence, the risk of costly errors in the planning phase is reduced.

Multi-Period EOQ Model for Multi-Generation Technology Products With Short Product Life Cycles

In this paper, the multi-period EOQ model is developed for the technology products that have multiple generations co-existing in the market, with each of them having a very short product life cycle. The paper first develops the framework for computation of inventory-related costs and then minimizes the total replenishment costs using random search technique and approximating the non-linear expressions while using Simpson’s Rule for integration. The paper also provides numerical illustrations and establishes a few important theorems that relate the EOQ to the innovation of diffusions. It is found that the total replenishment cost curve, drawn on the EOQ axis in the case of technology generations is convex to the origin. Since the objective function is highly non-linear, the genetic algorithm has been used to find the solution to the problem. The study also suggests that the faster diffusion of the next generations has a conflicting effect on the EOQ of the first generation in the case of pooled and non-pooled logistics.

Product Life Cycle Lengths and Real Activities Management

We examine how short product life cycle lengths constrain real activities management among a group of manufacturing firms. Companies with short-cycle products face fast product obsolescence and constant pressure to develop and market new products. Fast product obsolescence increases the cost of overproduction, while constant pressure for new product development increases the cost of opportunistic spending cuts. We predict and find evidence that in general, short-cycle firms do not beat earnings thresholds through overproduction and discretionary spending cuts as extensively as long-cycle firms do. However, since the Sarbanes-Oxley Act came into effect, short-cycle firms have increasingly overproduced, and there is no longer a difference in overproduction between short- and long-cycle firms. Moreover, the presence of major customers attenuates the relationship between product life cycle lengths and real activities management, while a large market share and high fixed-assets intensity both accentuate the relationship. Finally, short-cycle firms are also less likely to use LIFO (last-in, first out) liquidation to inflate earnings than long-cycle firms.

Green in-store operations and sustainability performance: the moderating role of organization size

PurposeBusinesses are increasingly striving to become sustainable in terms of economic, environmental and social aspects. However, in the fresh food retail supply chains (SCs), achieving environmental objectives can be challenging because of the unique characteristics of products such as perishability, bulkiness, short product lifecycle and the requirement for cold chain infrastructure. The retail industry is the face of a SC. Therefore, its role in achieving sustainable objectives is pivotal. This study examines the effect of green in-store operations on sustainability performance indicators of fresh food retail and examines the moderating role of organization size in this context.Design/methodology/approachData are collected through surveys using self-administered questionnaires from 70 retail stores with 188 completed responses. Data are analyzed using structural equation modeling.FindingsResults show a positive relationship between green in-store operation with environmental social and economic performance. Furthermore, these relationships are moderated by the organization size such that the positive green in-store operation and performance relationships are stronger in the case of environmental and social performance only and for larger retail stores. No moderation is seen for economic performance.Originality/valueThe study broadens the understanding of green SC management’s effect on sustainability performance in the retail industry. It shows how the positive implications of a green SC are contingent on organization size and have prominence for environmental and social performance.

ASSOCIATING PRODUCT DESIGN TO SUPPLY CHAIN MANAGEMENT FOR BUSINESS RESPONSIVENESS

The economy is advancing and developing at animus pace, the entrepreneurs are progressing by shifting the concept of mass production to quality production and then providing individual touch to products and services. The consumer has now-a-days elated expectations like product variations, shortened product lifecycles. Due to increase of pressure from market, consumers towards quality improvements, the supplier has to cater to the demand of the customer by providing the quality delivery, on time and with the personalised attributes. The product delivery is provided to the customers through firm’s supply chain management network, when the network is intact, it will make the supply effective and easy. The way to deal with the increased demand is designing and developing the product on the basis of the supply networks. The competitiveness among the business tycoons are moving to another level as these businesses are no more competing among themselves but competing between the product supply chains. The current study has discussed that how the problems of supply chain management can be solved and made more responsive by associating it with the product design at very initial stage with the help of multiple case study approach.

Method for improving the speed and pattern quality of a DMD maskless lithography system using a pulse exposure method.

Maskless lithography based on a digital micromirror device (DMD) has the advantages of high process flexibility and a low production cost. However, due to the trade-off relationship between the pixel size and exposure area, it is challenging to achieve high resolutions and high patterning speeds at the same time, which hinders the wider application of this technology in micro- and nano-fabrication processes. In addition, micromirrors in DMDs create pixelated edges that limit the pattern quality. In this paper, we propose a novel DMD maskless lithography method to improve the pattern quality during high-speed continuous patterning by means of pulse exposure and oblique scanning processes. A unique criterion, the pixel occupancy, was devised to determine the parameters related to the pulse exposure and oblique scanning optimally. We also studied how the duty cycle of the pulse exposure affects the pattern quality. As a result, we were able to increase the scanning speed up to the speed limit considering the damage threshold of the DMD and improve the pattern quality by resolving the pixelation problem. We anticipate that this method can be used in various microfabrication fields with short product life cycles or in those that require custom designs, such as the manufacturing of PCBs, MEMS devices, and micro-optics devices, among others.

Competitive Advantage as an Outcome of Strategic Intent: A Review of Literature

Organizations throughout the world are confronted with issues coming from a high degree of competitiveness and pressure to fulfill strategic objectives in accordance with their mission and vision statements, yet they lack the capacity to reach their full potential over time. The global marketplace is characterized with reduced trade barrier, intensified competition, shortened product life cycle, and deepening industrial segmentation. In this fast-paced business environment, companies fiercely seek to secure core competence to obtain and sustain their competitive advantages. The attribute of strategic intent will help the firm to achieve performance in terms of growth since organizations know themselves and will operate with direction. Strategic intent help in determining resource allocation and competency in an organization, as organizations' resources and capabilities are necessary for a future position. Identifying the desire for the company to succeed and stay ahead of the competition as the primary incentive for strategic thinking is one of the fundamental drivers of strategic intent. Although empirical data supports the existence of performance difficulties in organizations, little efforts have been made to study how the execution of strategy intent influences the competitiveness of organizations. Consequently, the study has systematically reviewed extant literature in the field of strategic intent to develop an encompassing definition of the construct, identify the processes of strategic intent, the perspectives and measures of these perspectives. Ultimately, the review of literature has led to the development of a theoretical and empirical literature that can be used in future empirical studies. This study sought to review the development existing literature to underline the knowledge gaps appropriate to form a basis for future research work. A conceptual model was developed consisting of two constructs: strategic intent and competitive advantage as a guide for future research work in the field of strategic management.

Launch Strategies for Newly Developed Short Life Cycle Products

Nowadays, due to the rapid pace of technology enhancement, growing consumer expectations, and market competitiveness, life cycle of products is shortening faster than it used to be. In such situations, where new generations of products lead to the obsolescence of those currently available, the simultaneous pricing of both newly developed and available products is a challenging task. In this paper, using game theory approaches, we investigate various possible conditions in which two firms may introduce new generations of products with short life cycles. Optimum price of newly developed and planned obsolescence products are determined by the proposed method. The effectiveness of the proposed method is verified using various numerical calculations and sensitivity analyses. A real case study from textile industry illustrates the application of the proposed approach in industry.

Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review

With unorganized, unplanned and improper use of limited raw materials, an abundant amount of waste is being produced, which is harmful to our environment and ecosystem. While traditional linear production lines fail to address far-reaching issues like waste production and a shorter product life cycle, a prospective concept, namely circular economy (CE), has shown promising prospects to be adopted at industrial and governmental levels. CE aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing. Innovative technologies like artificial intelligence (AI) and machine learning(ML) provide vital assistance in effectively adopting and implementing CE in real-world practices. This study explores the adoption and integration of applied AI techniques in CE. First, we conducted bibliometric analysis on a collection of 104 SCOPUS indexed documents exploring the critical research criteria in AI and CE. Forty papers were picked to conduct a systematic literature review from these documents. The selected documents were further divided into six categories: sustainable development, reverse logistics, waste management, supply chain management, recycle & reuse, and manufacturing development. Comprehensive research insights and trends have been extracted and delineated. Finally, the research gap needing further attention has been identified and the future research directions have also been discussed.

Grouping Maching using Genetic Algorithm for Dynamic Cell Layout Design

Changes in the manufacturing sector due to changes in shorter product life cycles, market demands and also use of the latest technology in the company, this will result in changes in process flow and also change the layout in the production section, causing dynamic layout problems. Dynamic layout problems can be solved by using a manufacturing cell formation method which has a high degree of flexibility. In this study, algorithms used for grouping machines into manufacturing cells are Direct Clustering Algorithm and Rank Order Clustering. Then it will be improved by using Genetic Algorithm. The Dynamic Modified Spanning Tree Algorithm is also used to sort machines into a layout with a single-row structure and determine the length of the planning time window in the future. The goals of this research is to get the best solution from the two methods of grouping machines/parts into manufacturing cells and to obtain improvement results using Genetic Algorithms. For the design industry, the resulting dynamic cell layout is expected to reduce production costs, save material handling, be efficient in material flow, which in turn will be able to compete globally.

Microplastics in intertidal water of South Australia and the mussel Mytilus spp.; the contrasting effect of population on concentration

Microplastics, plastic particles <5 mm in size, are of global concern as human-caused pollutants in marine and fresh waters, and yet little is known of their distribution, behaviour and ecological impact in the intertidal environment of South Australia. This study confirms for the first time, the presence of microplastic in the South Australian intertidal ecosystem by quantifying the abundance of particles in intertidal water and in the keystone species, the blue mussel, Mytilus spp., an important fisheries species, at ten and six locations respectively, along the South Australian coastline. For a remote region known for its pristine environment, microplastic concentration in intertidal water was found to be low to moderate (mean = 8.21 particles l−1 ± 4.91) relative to global levels and microplastic abundance in mussels (mean = 3.58 ± 8.18 particles individual−1) was within the range also reported globally. Microplastic particles were ubiquitous across sites and bioavailable by size in water (mean = 906.36 μm) and in mussel (mean = 983.29 μm) raising concerns for the health of South Australia's unique coastal ecosystems and for the human food chain. Furthermore, a positive correlation was found between human coastal population size and microplastic concentration in intertidal water, irrespective of influences from industry - tourism, fishing and shipping ports. FTIR analysis determined plastic type to include polyamide (PA), polyethylene (PE), polypropylene (PP), acrylic resin, polyethyleneterephthalate (PET) and cellulose, suggesting synthetic and semi-synthetic particles from single-use, short-life cycle products, fabrics, ropes and cordage. Our findings shed light on the urgent need to establish the local sources of microplastic pollution in order to assist the community, industry and government to reduce the impact of microplastic on the fragile marine systems within South Australian intertidal waters and on the organisms associated with the human food chain.

Application of system dynamics to reveal the suitable CPFR mode and unsold goods return policy for deteriorating items

With the intensification of global competition and rapid changes in customer needs, most companies focus on rapid customer response. This has caused problems such as shortened product lifecycles, diversified and small volume demands, and shortened lead time order. As a result, suppliers at all levels in a supply chain produce too many finished products, parts, or components, and increase inventory stock. This is an undesirable situation, particularly for deteriorating items. Increased inventory of deteriorating items does not only increase inventory cost, but also the product cost when the items lose function due to long storage time. In order to solve this problem, many scholars have proposed tools to improve supply chain performance, for example, collaborative planning, forecasting and replenishment (CPFR). Therefore, this research applied system dynamics to reveal the suitable CPFR mode for deteriorating items under two types of unsold goods return policies. The first model prohibits buyer to return unsold goods to seller, while the second model allows buyer to return unsold goods to seller. By using CPFR, buyer and seller can share, change and instantly update forecast information, both on the supply and demand sides. The inventory levels can be minimized and the bullwhip effect in supply chain can be resolved by CPFR to create a win-win situation for both seller and buyer. The results show that for deteriorating items, seller receives more profit than buyer in all four CPFR scenarios under the policy that buyer cannot return goods to seller. However, if buyer can return goods to seller, the seller’s profit is less than the buyer’s but the profit gap is not as wide as the previous model. Therefore, to maintain long-term collaborative relationship, seller should allow buyer to return goods. In both models, total profit is the highest when buyer is responsible for order generation in CPFR. This study applied system dynamics to illustrate the impact of different business models on supply chain performance and then to reveal effective strategy for supply chain partners.

Aftersales Service Factors Determining the 'Would Recommend' Score in Mobile Phone Industry

Customers' recommendations are strongly associated with sales growth and profitability. Satisfied loyal customers tend to recommend, but it doesn't happen always. Furthermore, a customer is exposed to aftersales services throughout the product lifecycle, which significantly impacts customer satisfaction and loyalty. This situation is more complex for the mobile phone industry, where innovative and short-lived products are launched every year. Providing aftersales services for these innovative products worldwide is challenging while maintaining customer satisfaction and loyalty. But, within a short mobile product lifecycle, customers' recommendations are crucial for growth. However, the aftersales service elements influencing customer recommendations are not well studied. This study aims to identify the elements of aftersales services that affect customers' recommendations. Three hundred and two mobile phone users of different brands, identified through the snowball sampling technique, were surveyed through a structured questionnaire. Multiple regression analyses of these data found five aftersales services: service center's support/repair, warranty, online support, upgrades, and delivery are statistically significant in influencing customers' recommendations for mobile phone brands.

Design Crowdsourcing Supply Chain in Short Life Cycle Products

Design crowdsourcing is the largest open innovation model that can create value with potential consumers. It offers an opportunity to quickly respond to the market by obtaining instant designs from the crowd, freelancing designers with fundamental skills. In addition, it can secure globally innovative competitiveness without financial burden, which is more effective to start-up companies and small and medium enterprises (SMEs) in the field of seasonal product industry. Developing standardized design crowdsourcing supply chain processes and mathematical models is essential to respond to market trends and customer needs in the seasonal product industry. This study has been carried out to determine the best mechanism in the design crowdsourcing supply chain and coordinate each supply chain member whose desires meet each other. Thus, we identify contracts under which conditions can coordinate the crowdsourcing supply chain by a newsvendor model with a manufacturer and a retailer with a crowdsourcing platform. To see that, we study the coordination of the crowdsourcing supply chain through the following contracts: wholesale price, buy-back, and revenue sharing contract. For the forecasting, we present a framework of the design crowdsourcing supply chain and compare supply chain performance under crowdsourcing supply chain coordination. The summarized result shows that the wholesale price contract cannot coordinate the crowdsourcing supply chain efficiently. In contrast, buy-back and revenue sharing contracts can coordinate the crowdsourcing supply chain.

Product-Production-CoDesign: An Approach on Integrated Product and Production Engineering Across Generations and Life Cycles

Shorter product life cycles and high product variance nowadays require efficient engineering of products and production systems. Hereby a further challenge is that costs over the entire life cycle of the product and production system are defined early in the process. Existing approaches in literature and practice such as simultaneous engineering and design for manufacturing incorporate aspects of production into product engineering. However, these approaches leave potential for increasing efficiency unused because knowledge from past generations of products, production systems, and business models is not stored and reused in a formalized way and future generations are not considered in the respective current engineering process. This article proposes an approach for integrated product and production engineering across generations and life cycles of products and production systems. This includes the consideration of related business models to successfully establish the products on the market as well as the anticipation of future product and production system characteristics. The presented approach can reduce both development and manufacturing costs as well as time to market and opens the vast technological potential for product design to achieve additional customer benefits. Three case studies elaborate on aspects of the proposed approach and present its benefits.

Economic efficiency of automated manufacturing systems design

Systems for the automated planning and design of manufacturing systems have been widely studied and are a promising answer to current challenges arising from shorter product life cycles, high labor costs, and increasingly complex production resources. However, implementing these systems entails substantial investment, and their profitability depends on various factors. One of the major difficulties in implementing automated planning systems in an industrial context has been establishing a suitable database of resource models. This article presents an evaluation method to compare the economic efficiency of manual and automated planning, including resource modeling. It also explores different application scenarios to determine under which preconditions automated planning is advantageous and where the limits of their application lie.

Aerodynamic Feeding 4.0: A New Concept for Flexible Part Feeding

In modern production environments, the need for flexible handling systems constantly increases due to increasing uncertainties, shorter product life cycles and higher cost pressure. Part feeding systems are vital to modern handling systems, but conventional solutions are often characterized by low flexibility, high retooling times, and complex design. Therefore, in previous research, multiple approaches towards aerodynamic feeding technology were developed. Using air instead of mechanical chicanes to manipulate workpieces, aerodynamic feeding systems can achieve high feeding rates while at the same time being very flexible and reliable. Still, the complexity of the workpieces that can be oriented relies on the number of aerodynamic actuators used in the system. Previously developed systems either used one nozzle with a constant air jet or one nozzle and an air cushion, allowing a maximum of two orientation changes.This work presents a new concept for an aerodynamic feeding system with higher flexibility (with regard to the workpiece geometry) and drastically reduced retooling times compared to conventional feeding systems. In contrast to previous implementations of aerodynamic feeding systems, using only one air nozzle or an air cushion, the new concept uses multiple, individually controllable air nozzles. Using a simulation-based approach, the orientation process is divided into several basic rotations - from a random initial orientation to the desired end orientation - each performed by a distinct nozzle. An optimization algorithm is then used to determine an optimal layout of the air nozzles, enabling the feeding system to feed any desired workpiece, regardless of the initial orientation. With the proposed concept, high flexibility, low retooling times and relatively low costs are expected, setting up aerodynamic feeding as an enabler for changeable production environments.

Do Supply Chain Capabilities have an Impact on Firm Performance? A Case of the Manufacturing Sector in Gauteng Province

Rapid technological growth, consumer fulfillment, shorter product life cycles, and more penetrating international competition define the business environment nowadays. The new competitive environment has required companies to acquire new ways of effectively pursuing a competitive advantage, since the competitive advantage of a firm now depends on operational efficiency and productivity across its functional areas. The main purpose of this research was to study the relation amongst supply chain capabilities (supply chain network design, supply chain information capabilities, and supply chain integration) and supply chain agility on firm performance in the Gauteng province’s manufacturing sector in South Africa. Structured questionnaires were circulated to different manufacturing firms in the Gauteng province. Data was analysed using SPSS and AMOS 26.0 software. Results showed that the level of firm performance in the manufacturing sector is dependent upon the effectiveness of supply chain capabilities and the existence of a sound long-term relationship between partners in the supply chain. Manufacturing managers and proprietors may be able to enhance the levels of firm performance by making improvements to supply chain technologies and improving supply chain agility by cultivating long-lasting sound relationships with key suppliers and customers.

Model Selection for Predictive Quality in Hydraulic Testing

Manufacturing companies are confronted with enormous challenges such as increasing product complexity, shorter product life cycles and growing product diversity. Politically and socially, increased demands regarding sustainability and resource consumption are streaming into the focus of companies. One solution strategy to increase the productivity of existing production systems while ensuring existing quality standards is the application of data-driven analytical methods such as machine learning.Due to the frequent changes in production conditions, the analysis of real manufacturing data is linked to sophisticated data pre-processing. Changes in production data are manifested in trends and systematic shifts over time. Data pre-processing includes rule-based data cleaning, the application of dimension reduction techniques and the identification of comparable data subsets. Within the used dataset of hydraulic valves by Bosch, the comparability of the same production conditions in the manufacturing of hydraulic valves can be identified within certain periods. Machine learning methods can process large amounts of data, unfavorable row-column ratios and discover dependencies between the input data and the specified target variable as well as evaluate the multidimensional influence of all input variables on the target variable. For use cases in manufacturing, neural networks, support vector machines and tree-based methods have so far proved to be very successful.The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Within this research, machine learning methods with deep and shallow structures are applied to predict the internal leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from end-of-line testing. Moreover, the most suitable methods are selected, and accurate quality predictions are obtained.