BUSI730_Module6_Literature Review Assignment_FINAL

docx

School

Embry-Riddle Aeronautical University *

*We aren’t endorsed by this school

Course

730

Subject

Business

Date

May 23, 2024

Type

docx

Pages

Uploaded by wendyliealix

1 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING Literature Review Product Lifecycle and Strategic Cost Planning School of Business, Liberty University BUSI730: Strategic Allocation of Financial Resources (B03) February 26, 2023

2 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING Abstract A literature review presents and summarizes published information in specific subject areas and often presents timely and relevant information. It synthesizes materials about a particular topic and can influence the readers’ interpretations of the literature. This submission is a literature review on product lifecycle and strategic cost planning. It will summarize the importance of effective product lifecycle management and strategic cost planning in general business practices and their application in cybersecurity. Keywords: literature review, cost methodologies, cost planning, cost strategy, cost optimization, computer-aided technologies, digital twins, AI, cybersecurity

3 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING Introduction: Product Lifecycle and Cost Planning Product lifecycle data is essential in informing timely, advantageous, and strategic decisions for organizations (Bednarek & Parkes, 2021; Wang et al., 2021). Product lifecycles are more unpredictable as the market, and consumer demands evolve. Most products delivered in today’s modern society have a short life span, and the consumer expects new products in shorter periods (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Because of this, the need for increased confidence in product lifecycle data and cost management methodology is increasingly becoming important (Niu et al., 2022; Wang et al., 2021). Understanding the cost drivers in each stage of the product lifecycle generates knowledge that can be used to implement strategic financial initiatives driving toward cost reductions and increased profitability (Corallo et al., 2022). Strategic positioning of the organization’s resources can lead to a competitive advantage that can increase a company’s overall profitability (Bednarek & Parkes, 2021; Corallo et al., 2022). Further, implementing strategic cost planning for the product lifecycle can save time and money significantly (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Product Lifecycle Data and Cost Data Product lifecycle is often described as the cycle in which a product goes through various stages, including (1) introduction, (2) growth, (3) maturity, and (4) decline (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Every step of the product lifecycle involves cost elements such as raw materials, direct labor, advertising and marketing, sales commissions, and various indirect costs (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Understanding the cost drivers for each component is essential to develop a strong understanding of the product lifecycle (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Such knowledge is critical to executing multiple business functions and activities needed to support

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

4 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING every stage in the product lifecycle. Further, an organization can leverage an effective cost management strategy to withstand uncontrolled macroeconomic factors by continuously tracking and improving the cost strategy approach in product lifecycle planning (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Cost Planning Methodologies in Product Lifecycle Management The relationship between product lifecycle and cost management is often closely tracked to maximize product profitability (Corallo et al., 2022; Zheng et al., 2020). Corallo et al. (2022) identified that improved accuracy and organization of cost data could be implemented through technology and a practical model-based enterprise (MBE) approach, resulting in cost and time savings. The MBE cost planning methodology applies modeling technologies to integrate the technical and business activities involved in designing, manufacturing, supporting, and retiring products and services (Corallo et al., 2022). Corallo et al. (2022) quantified that an estimated 45% of time savings can be realized in innovation, development, and manufacturing. An additional 50% of time savings can be realized when MBE methodology is adopted in the support function (Corallo et al., 2022). Bednarek & Parkes (2021) focused on the relevance of Fordism as a cost strategy in today’s economy. Fordism is the mass production and consumption of products and has been described as the transition from an agricultural to an industrial economy (Bednarek & Parkes, 2021). The concept of cost reduction through mass production has been known since the early 18 th century (Bednarek & Parkes, 2021). It is an essential concept because it transformed economies in the past and continues to exist and shape modern civilization (Bednarek & Parkes, 2021). While Fordism continues in modern civilization, the social appetite for this strategy is changing as social concerns about pollution, waste, and global warming remain critical issues in

5 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING today’s economy (Bednarek & Parkes, 2021). As a response to the growing social concerns about global warming, Bednarek & Parkes (2021) suggested changes to incorporate the Fordism methodology. By improving post-purchase satisfaction, consumers can extend the product life through longer warranties and service support on many products (Bednarek & Parkes, 2021). Bednarek & Parkes (2021) believed that by improving customer satisfaction post-purchase, firms invest in people and create workplaces that lead to more sustainable economies. Further, Bednarek & Parkes (2021) suggested opportunities to increase profitability by improving existing models and driving towards a circular economy (CE) or the recovery, reuse, and recycling of materials produced. Another cost methodology emerging in today’s economic environment is product morphology (Liu et al., 2021). The product lifecycle has three forms: (a) conceptual products, (b) digital products, and (c) physical products (Liu et al., 2021). By integrating the three product forms into one through global object mapping, a unified data model can be developed, resulting in a streamlined source of product lifecycle data that can be utilized for strategic cost management planning (Liu et al., 2021). An essential aspect of product morphology is its information modeling architecture (Liu et al., 2021). Ensuring all product data at all stages of its lifecycle is integrated with high consistency, integrity, and security levels is essential (Liu et al., 2021). Data redundancy is eliminated through the product morphology approach, and increased confidence in data integrity and consistency can be attained (Liu et al., 2021). Product Lifecycle Management and Technological Advancements Organizations rely on technological advancements to improve cost-planning strategies and product lifecycle management (Evrard et al., 2021; Rojek et al., 2020; Wang et al., 2021). New technology can develop more sustainable products with extended life spans (Evrard et al.,

6 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING 2021). Engineers and manufacturers can design new products that adopt a circular loop product lifecycle, suggesting sustainability through the reuse and recycling of goods produced (Evrard et al., 2021). To create a sustainable production environment, engineers and designers must develop an entire product lifecycle of immortal goods, consisting of numerous loops and repurposing products and services (Evrard et al., 2021). Using technology, designers can drive toward two approaches: (1) design for recycling and (2) design for integrity (Evrard et al., 2021). The foundation of immortal products must prioritize product integrity in the long term because it will redefine the practice of design, manufacturing, and supporting new goods produced (Evrard et al., 2021). Further, it requires changing production, consumption, business operations, and manufacturing (Evrard et al., 2021). Immortal products require three elements in their design process: (1) business strategy, (2) product lifecycle scenario, and (3) environmental solutions profile (Evrard et al., 2021). It encourages organizations to constantly review and track the above elements to ensure adaptability in the dynamic economic environment (Evrard et al., 2021). Another perspective on the importance of product lifecycle sustainability was presented by Saxena et al. (2020). The long-term benefits of intelligent manufacturing and incorporating sustainability as a deciding element in the product design is essential to maximize opportunities for increased profitability margin (Saxena et al., 2020). Smart manufacturing can improve key metrics such as quality, flexibility, and time involved in the manufacturing process (Saxena et al., 2020). Using computer-aided technologies (CAx), manufacturing firms can learn more about the multiple elements influencing quality, flexibility, and time involved in manufacturing (Saxena et al., 2020). Firms can focus on improving product performance over its lifetime, creating a circular economy product, and reducing cost through extended product life. (Saxena et al., 2020).

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

7 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING In addition to computer-aided technologies, Digital twins and artificial intelligence are two emerging technologies that can significantly improve product lifecycle data management and sustainability (Rojek et al., 2020; Wang et al., 2021). A digital twin is a dynamic, digital duplicate of a technical object (Rojek et al., 2020). The technical object can be a device, machine, physical system, or production process (Rojek et al., 2020). Changes to the technical object are detected by the digital duplicate or its digital twin and provide an in-depth analysis of the processes in the technical object (Rojek et al., 2020). As the digital twin collects information, the ability to predict events can result in effective remote management, early detection of maintenance needs, and pre-identification of potential malfunctions (Rojek et al., 2020). Such information is helpful for managers to take a proactive approach to prevent downtimes resulting in unexpected expenses and lost revenues or sales (Rojek et al., 2020). Adopting a digital twin is easy to implement because it can be integrated into new and already-established solutions (Rojek et al., 2020). Further, digital twin technology is flexible; businesses can use it to test new products before committing to a full product manufacturing process (Rojek et al., 2020). This enables businesses to continue developing, evolving, and improving product designs without significant cost commitments during the product design stage (Rojek et al., 2020). To implement digital twin technology, a virtual recreation of the physical world with real-time access to its data is necessary because it needs a complete spectrum for data collection (Rojek et al., 2020). This solution has been proven possible with the emergence of product sensors and the Internet of Things (IoT), offering the efficient collection of large volumes of data (Rojek et al., 2020). Leveraging data and machine learning for timely analytics has been instrumental in using these data for strategic decision-making and cost planning (Rojek et al., 2020).

8 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING Artificial intelligence (AI) is recently gaining extensive attention in the manufacturing industry and is becoming involved in the various stages of product lifecycle management as companies push for smart manufacturing (Rojek et al., 2021; Wang et al., 2021). Three notable schools of thought on AI include (1) symbolicism, (2) connectionism, and (3) actionism (Wang et al., 2021). The introduction of AI through symbolicism represents knowledge, cognition, and reasoning, which is the center of its existence (Wang et al., 2021). The connectionism concept was invented based on the fundamental structure of biological neural networks (Wang et al., 2021). Finally, actionism focuses on the cybernetic systems resulting in self-adaptation, self- optimization, self-regulation, self-organization, and self-learning (Wang et al., 2021). AI’s application in immersive technologies can redefine human interaction with the physical world (Wang et al., 2021). AI can significantly improve data analysis, automating data- driven tasks (Wang et al., 2021). Current limitations in the use of AI are rooted in its computation capability (Wang et al., 2021). Thus, studies on accelerating chips are essential to AI’s long-term existence and prosperity (Wang et al., 2021). Evidence of AI’s use in real-world business applications can be seen in products such as natural language processing, computer vision, and machine learning (Wang et al., 2021). Future applications of AI are possible in market analysis based on data mining (Wang et al., 2021). This can lead to determining target customers and learning customer requirements that can be transformed into product features (Wang et al., 2021). Timely development of conceptual design is possible with AI through case library, yielding increased design efficiency and using previous designs for relevant adjustments in new designs (Wang et al., 2021). Lastly, AI is used in augmented reality to create a personalized, collaborative design, an immersive and interactive way to develop advanced modeling techniques (Wang et al., 2021).

9 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING Product Lifecycle data and Cost Planning in Cybersecurity During the Covid-19 pandemic, the world was forced to adapt to a restricted society where limited human interactions became necessary. The workforce shifted from being physically in the office to a remote or hybrid work model, which increased the need for reliable cybersecurity products. As such, the cybersecurity industry has seen increased sales growth during this time. Thus, the ability to produce goods to match consumer demands was needed and required cost-planning strategy adjustments. Strategic cost planning becomes critical as the market shift because the cybersecurity industry requires accurate cost estimation driven by the demand for more sophisticated systems as new cybersecurity threats, viruses, and hackers increase daily (Kamariotou & Kitsios, 2023). Confidence in the product lifecycle data is essential to develop the right products consumers need to secure digital systems (Kamariotou & Kitsios, 2023; Turk et al., 2022). Quickly producing cybersecurity solutions is necessary because new viruses, malware, and threats emerge daily (Kamariotou & Kitsios, 2023; Turk et al., 2022). This pressures the cybersecurity industry to keep up with its products and deliver cybersecurity solutions promptly. By accurately understanding product lifecycle data and incorporating technological advancements, organizations can respond to evolving market demands helping managers control costs to continue driving toward increased competitive advantage (Bednarek & Parkes, 2021; Niu et al., 2022; Wang et al., 2021). Conclusion The cost planning methodologies presented above have different approaches to delivering value to businesses. Each method presents solutions dependent on their applicability to a particular business model or industry. While they present differences, one thing they all have in

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

10 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING common is that they all agree that an accurate and robust understanding of product lifecycle data leads to an effective cost-planning strategy (Bednarek & Parkes, 2021; Corallo et al., 2022; Liu et al., 2021). Product lifecycle management and strategic cost planning are integral to an organization’s operations. They are the foundation for developing sustainable products that optimize cost, resulting in long-term operational success and increased profitability opportunities. Understanding the information driven by the product and cost data can facilitate strategy development and implementation, target costing and pricing, practical analysis and evaluation of product and business performance, and planning and decision-making (Asadabadi & Zwikael, 2021; Kadir et al., 2020; Kasie & Bright, 2021). Further, modern society is learning to adapt and maximize technological advancements to their benefit. Technologies such as CAx, digital twins, and AI can increase efficiency, enhance productivity, optimize processes, improve data integrity, and strengthen security (Rojek et al., 2021; Wang et al., 2021; Saxena et al., 2020). As new technologies emerge, new opportunities can be leveraged to improve product lifecycle management and strategic cost planning. Because of the evolving nature of the economic environment, it is essential to develop business strategies that are dynamic and adaptable. Continuously being aware of the ongoing shift in market demand and macroeconomic events and taking a proactive approach to respond to these changes is critical for survival and competitive advantage (Rojek et al., 2021; Wang et al., 2021; Saxena et al., 2020). Recommendation for Future Research Product lifecycle management and strategic cost-planning are certainly challenging tasks. Even with technology, collecting data accurately and identifying its relevance and applicability in today’s economy is challenging. Rojek et al. (2021) recommended that future research on this

11 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING topic point toward the applicability of other new technologies to generate increased confidence in accurate product lifecycle data. Integrating systems as intelligent modules can verify assumptions presented by the abovementioned methodologies by using an actual set of manufacturing data and its application to existing business architecture and business processes. Corallo et al. (2022) believe that the next step to further expand knowledge in product lifecycle management is to develop an empirical approach using the model-based enterprise (MBE) as a framework. Meanwhile, Evrard et al. (2021) recommended future research to identify the social concerns beyond the technical approach presented in developing and designing immortal products.

12 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING References Asadabadi, M. R., & Zwikael, O. (2021). Integrating risk into estimating project activities' time and cost: A stratified approach. European Journal of Operational Research, 291 (2), 482- 490. https://doi.org/10.1016/j.ejor.2019.11.018 Bednarek, M., & Parkes, A. (2021). Legacy of Fordism and product life cycle management in the modern economy. Management and Production Engineering Review, 12 (1), 61. https://doi.org/10.24425/mper.2021.136872 Corallo, A., Del Vecchio, V., Lezzi, M., & Luperto, A. (2022). Model-based enterprise approach in the product lifecycle management: State-of-the-art and future research directions. Sustainability (Basel, Switzerland), 14 (3), 1370. https://doi.org/10.3390/su14031370 Evrard, D., Ben Rejeb, H., Zwolinski, P., & Brissaud, D. (2021). Designing immortal products: A lifecycle scenario-based approach. Sustainability (Basel, Switzerland), 13 (6), 3574. https://doi.org/10.3390/su13063574 Kadir, A. Z. A., Yusof, Y., & Wahab, M. S. (2020). Additive manufacturing cost estimation models—a classification review. International Journal of Advanced Manufacturing Technology, 107 (9-10), 4033-4053. https://doi.org/10.1007/s00170-020-05262-5 Kamariotou, M., & Kitsios, F. (2023). Information systems strategy and security policy: A conceptual framework. Electronics (Basel), 12 (2), 382. https://doi.org/10.3390/electronics12020382 Kasie, F. M., & Bright, G. (2021). Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process. Journal of Modelling in Management, 16 (3), 825-847. https://doi.org/10.1108/JM2-05-2020-0123

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

13 PRODUCT LIFECYCLE AND STRATEGIC COST PLANNING Liu, G., Man, R., & Wang, Y. (2021). A data management approach based on product morphology in product lifecycle management. Processes, 9 (7), 1235. https://doi.org/10.3390/pr9071235 Saxena, P., Stavropoulos, P., Kechagias, J., & Salonitis, K. (2020). Sustainability assessment for manufacturing operations. Energies (Basel), 13 (11), 2730. https://doi.org/10.3390/en13112730 Turk, Ž., Sonkor, M. S., & Klinc, R. (2022). Cybersecurity assessment of bim/cde design environment using cyber assessment framework. Journal of Civil Engineering and Management, 28 (5), 349-364. https://doi.org/10.3846/jcem.2022.16682 Wang, L., Liu, Z., Liu, A., & Tao, F. (2021). Artificial intelligence in product lifecycle management. International Journal of Advanced Manufacturing Technology, 114 (3-4), 771-796. https://doi.org/10.1007/s00170-021-06882-1 Zheng, P., Li, X., Peng, T., Wang, Y., & Zhang, G. (2020). Industrial smart product‐service system development for lifecycle sustainability concerns. IET Collaborative Intelligent Manufacturing, 2 (4), 197–201. https://doi.org/10.1049/iet-cim.2020.0056

BUSI730_Module6_Literature Review Assignment_FINAL

Related Documents