Apple’s Planning Under Environmental Uncertainty – 2723 Words

Introduction

Innovation is the basis for the development of any successful business enterprise. However, the constant challenges of external factors, and rapid technological development, form a highly dynamic environment in which competitiveness becomes a complex and constantly transforming task (Honig and Samuelsson, 2021, Longva, Strand, and Pasquine, 2020). At the same time, R&D departments in companies are becoming mandatory even where the technological presence was not previously required or implemented (Handiwibowo et al., 2020). As a result, various business planning strategies have emerged to consider all new challenges and long-term dynamics of change, including ERP enterprise resource planning or a focus on innovative development trajectories (Aldossari and Mokhtar, 2020, Menon, 2019). In fact, every day, an environment of uncertainty is formed, where more and more factors are beyond the control of organizations. As a result, there was a need to create new methods and mechanisms for decision-making and business planning under conditions of uncertainty.

For this, many scientific approaches and technologies are integrated that can find patterns in these uncertain conditions. First, working with big data is one of the potential sources of non-obvious dependencies for large businesses (Ajah and Nweke, 2019, Nobanee, 2020). Secondly, intelligent systems based on AI or neural networks, which can learn over time and produce more stable results, provide a broad scope for modeling a wide variety of events (Abiodun et al., 2018, Jiang et al., 2019, Li et al., 2018). Therefore, competitive business planning is completely technology-driven and currently requires innovative approaches.

Research Question and Objectives

The development of such a method is planned in this work, where the determination of the statistical significance of relationships between non-obvious factors in the production of Apple products and external environmental determinants in the context of constant dynamics, complexity, and uncertainty is the leading research problem. Consequently, the research question will be: Is there a correlation between water availability, coal mining, and air pollution in Taiwan, where most semiconductors are made, and the amount of Apple equipment produced or its financial performance? The variables chosen for evaluation are not random and are described in more detail in the literature review section.

The specific objectives of this dissertation are as follows. First, it is necessary to find official operating reports on these environmental indicators and financial and manufacturing reports from Apple. Secondly, based on these data, design a model or find similar works in the scientific field, where specific trends can be predicted for the near future. Thirdly, using statistical analysis tools, determine whether there is a relationship between various indicators, how strong they are, if any, and whether they are statistically significant. As a result, a preliminary prediction model of one of the key factors in Apple’s supply and financial performance can be obtained, partly shedding light on uncertainty in business planning. Apple’s example is illustrative and easy to evaluate due to the information availability. The best control of the reliability of the results will be time, and further research can diversify the approach for another category of factors.

Literature Review

The comprehensive nature of uncertainty cannot be fully considered, nor can it be modeled. However, differentiation of the influence of external factors makes it possible to obtain individual components, for example, environmental ones: there are many forecasts and models of the potential development of events on the planet concerning climate, non-renewable resources, and other things (Jiang et al., 2021, Liu et al., 2022). Against this backdrop, various institutional and political pressures emerge, including legislation and long-term government programs that organizations must comply with (Lee and Woo, 2020, Tagliapietra and Veugelers, 2020). Consequently, various tools that can account for at least one class of external factors will be most in demand in various business planning approaches. In addition, adapting such tools to the most promising technologies, such as neural networks, artificial intelligence, or extensive data analysis, can provide a robust system that detects non-obvious dependencies over long periods (Ferrero Bermejo et al., 2019, Stone et al., 2020). Thus, a preliminary search for such environmental factors through authoritative studies about business planning models can provide better forecasts for organizations that are related to current environmental issues. Apple, for example, has been the largest consumer of semiconductor products in recent years, but the extraction of these resources depends on many political and environmental factors (Lazonic and Hopkins, 2021). As a result, it is possible to consider such non-obvious dependencies using the example of a large company to adapt the approach for universal practical implementation in such areas.

The first mentioned problem, dependent on environmental factors, is the shortage of semiconductors widely used to produce many electronic equipments. Apple is the world leader in the consumption of this resource, which puts the company in a dependent position on suppliers. Preliminary analysis shows that semiconductor chip production, which is in demand in many areas, including actively developing ones, requires a lot of non-environmentally friendly energy, namely coal and water resources, to reduce heat (Shukalov, Zharinov, and Zharinov, 2020). The reorientation of the most famous industries is almost impossible in a short time, and given the current disruptions in supply chains due to the pandemic and other global challenges, the problem is becoming a critical environmental one. In addition to the impact on global warming and irreplaceable water and coal resources, many chemicals are released during production, which can dissolve in the air or even settle on the surface (Shukalov, Zharinov, and Zharinov, 2020). Therefore, environmental projections for Taiwan’s coal mining, water availability in mining and semiconductor manufacturing regions, and chemical air pollution rates can determine Apple’s unit production, revenue, gross profit, and other key variables. This proposal is considered as only one of the possible hypotheses; in addition to the semiconductor issue, it is possible to include other environmental issues, adapting research forecasts and finding an appropriate relationship to extrapolate predictions to Apple’s operational functionality.

In modern literature, more and more often began to pay attention to climatic and environmental problems. The forecasts of scientists are complex, and the modeling of situations takes place based on a considerable amount of input data processed by advanced technologies. Increasingly, forecasts began to rely not only on long-term climatic factors but also to include human activity as determinants, which increased the value of forecasts here and now (Dietze et al., 2018). Thus, so-called iterative short-term forecasts are gaining wide popularity in the scientific community, which allows for updating environmental problems and issues (White et al., 2019). In this situation, as a rule, one or two processes in a particular region are analyzed depending either on each other or in light of a potential disaster or problem (Bai et al., 2018, Tutak and Brodny, 2019). Often, for this modeling, the fuzzy logic technique is used (Zeinalnezhad et al., 2020). For example, a team of scientists is modeling a forecast of coal production relative to the availability and carrying capacity of water resources (WRCC), which is planned to be used as a variable in this paper (Chi et al., 2021). The H – J – B algorithm makes it possible to build a decision system for the scale of coal production under the WRCC constraint (Chi et al., 2021). In addition, water resources are often differentiated through the lens of fauna and flora, which also has environmental implications that affect specific production in these regions (Hobday et al., 2019, Kearney et al., 2018). Often, the dynamics of the chemical composition of water or air are assessed in the context of the longevity of the availability of these resources (Nemitz et al., 2020, Page et al., 2018, Shamshirband et al., 2019). These models often incorporate conditions of uncertainty, typically dictated by human activity and global factors.

The diversification of available methods narrows as the focus is on a specific region, issue, or association with an impact on specific companies. Typically, a specification in one country makes forecasts more extensive in terms of environmental choice, focusing on global issues: for example, Taiwan is studied in terms of wind power availability, carbon dioxide emissions, or the cumulative impact of human activity, called ecological footprint (Lee and Lin, 2020, Tsai and Chen, 2020, Wu et al., 2018). The impact on a specific material mining industry, particularly semiconductors, is studied in the context of environmental potential, and rarely are any mathematical models provided for relatively accurate or approximate predictions (Lin et al., 2018, Stock et al., 2018). The emphasis on material-dependent production is more often seen concerning the availability of electricity resources, innovation in sustainable development, or through the model of ecological footprint (Hazarika and Zhang, 2019, Li et al., 2019, Miśkiewicz, 2020). The weak side of such studies in the context of this work is the lack of quantitative data for analysis and forecasting; as a rule, systematic solutions are offered. As a result, uncertainty estimation is deprived of the possibility of statistical implementation for process automation. Moreover, many innovative ideas do not yet find a proper response in specific industries, which creates a gap in the practical application of such scientific works.

Methodology

The diversity of the identified studies provides a basis for the initial construction of a potential model of the relationship between non-obvious factors of production and financial performance at Apple and dynamic environmental phenomena. Forecasting based on specific decision-making systems is currently not a priority in large companies, but it can provide critical information for more accurate predictions in the face of uncertainty. The search for correlation between similar phenomena best describes the linear Pearson coefficient, which determines the joint variation of variables (Afyouni, Smith, and Nichols, 2019). At the same time, one should consider the diversification of more local determinants that can describe such variables as the availability of resources, and environmental pollution, which depends on human activity and stable natural processes. The most promising are algorithmization systems that consider the totality of these factors in the approximation and give potential decision-making options within the framework of applied problems (Chi et al., 2021). Scientific novelty in this situation is manifested as the transformation of the qualitative characteristics of such forecasts into quantitative data, which opens up a vast potential for statistical processing. In addition, qualitative studies highlight the ecological growth potential over the economic one in the Taiwan region (Lin et al., 2018). Accordingly, in this work, it is necessary to adapt promising models for assessing environmental performance to existing system developments.

Consequently, this paper proposes adapting qualitative data to the Hamilton-Jacobi-Bellman equations, which take into account the non-linear nature of the input data and give a value function of the price at the output to optimize decision-making. This function is then analyzed using the Pearson Ratio for correlation with Apple’s financial and operating performance. Considering this approach in more detail, the following advantages can be seen. First, the deterministic control function has a system state vector, which will be determined using the available environmental forecasts related to environmental dynamics variables. Secondly, the function of the Bellman value in solving these equations carries the goal of cost optimization, which has the applied character of improving productivity for the company. Thirdly, the problem of deterministic control is solved in a specific period, which makes it possible to introduce short-term forecasts lacking in the environmental analysis field. To obtain quantitative characteristics of the state of the environmental system in the Taiwan region, it is necessary to identify the key factors that indirectly affect the mining and production of semiconductors. For example, coal mining is determined by the carrying capacity of water resources, which in turn have ecological limitations due to the consequences in the reproduction of flora and fauna. Moreover, the chemical composition of air and water changes over time, affecting the scale of mining.

An important feature that limits the accuracy of semiconductor production estimates is worth noting, which also depends on political and logistical factors. However, specialists usually lay down accounting for these determinants in a complex business system when forecasting demand and production, including taking into account social, demographic, and partly environmental factors (Kelly, 2019, Sodhi and Tang, 2019). Therefore, this work will focus on building a model specifically in the context of environmental determinants, which need to be taken into account in a fully integrated way at the stage of working with production and financial indicators.

The adaptation of quantitative research methods to the transformation of qualitative characteristics has been studied in detail in the scientific community. This question has become relevant just as the growing uncertainty of the context of many works, especially within the global functioning of a business. Quantitative methods are much easier to statistically process and calculate statistical significance, which was considered the only possible way to prove specific hypotheses (Creswell, 2014). However, systems analysis has proven itself quite well in many areas, which has increased the popularity of mixed methods. In this work, it is a quantitative analysis will be used, but the experience successfully used in funny methods will be consistently implemented when it is necessary to take into account the variety of influencing factors. Therefore, the transformation of the data collected as a result of the literature analysis into the decision-making framework at this stage will be approximate due to the experimental nature of the methodology. Environmental factors concerning, for example, water resources, count as a diversified human influence, including related areas even in a particular region. Therefore, the cumulative assessment will be taken from the works on the conscience and faith of previous researchers. Even in narrow environmental forecasting, building a comprehensive model is currently impossible, or it requires significant technological, time, and financial resources, which, for several reasons, cannot be provided.

However, the prospect of complex use of the HJB system of equations with a further search for covariance can provide one of the complete methods for the practical implementation of the impact of environmental factors on Apple’s production and financial performance. Despite the narrow nature of the model, the resulting output will provide an approximation of the nature of the dependence on such uncertainty conditions. This step can bring the company’s policy to the proactive nature of predicting many of the essential characteristics of the already existing reactive adaptability. The transition from a “black box” to a “grey box,” when decision-making is formed solely based on already existing aspects of the dynamics of ecology, will allow the creation of promising models in business planning. The company does not influence each of the variables of this study. In other words, Apple cannot directly influence the carrying capacity of water resources, coal mining in Taiwan, or air pollution. However, this approach opens up the possibility of considering indirect influence through cooperation with specific stakeholders, including semiconductor companies, Taiwanese government structures, or global environmental associations. This information will be valuable not only for Apple but also for business sustainability in general, researchers in the field of environmental modeling and forecasting, and for understanding global natural processes.

Conclusion

This dissertation proposes a study of the impact of environmental factors on Apple’s production and financial performance. The literature review showed the presence of certain blind spots in the assessment and forecasting of environmental aspects under conditions of uncertainty. Weaknesses at the moment are manifested in the absence of effective quantitative methods and decision-making models concerning these factors, as well as their implementation for specific business tasks from areas not directly related to natural processes. In addition, current approaches are just beginning to move from long-term to short-term forecasts. Nevertheless, certain developments already exist, which opens up the scope for their application in order to build forecasts that are closer to reality. Among these, the approach of the Hamilton-Jacobi-Bellman system of equations stands out, which considers the input data’s non-linear nature, the environment’s state, dynamics over time, and potential use for production optimization.

The application of this technique has yet to be applied in the designated area, which emphasizes the relevance and scientific novelty of the study. The output is a function that, in addition to optimization, can be subjected to correlation analysis with the necessary indicators of the company. A differentiated consideration approach can provide information on non-obvious dependencies with environmental factors for more detailed and accurate business planning. Due to the significant dependence of manufacturers of smart technology on the production of semiconductors, this work is of great practical importance for many organizations and the business sector as a whole.

Reference List

Abiodun, O. I., et al. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), p. e00938. Web.

Afyouni, S., Smith, S. M., and Nichols, T. E. (2019) ‘Effective degrees of freedom of the Pearson’s correlation coefficient under autocorrelation’, NeuroImage, 199, pp. 609-625. Web.

Ajah, I. A., and Nweke, H. F. (2019) ‘Big data and business analytics: Trends, platforms, success factors and applications’, Big Data and Cognitive Computing, 3(2), p. 32. Web.

Aldossari, S., and Mokhtar, U. A. (2020) ‘A model to adopt enterprise resource planning (ERP) and business intelligence (BI) among Saudi SMEs’, International Journal of Innovation, 8(2), pp. 305-347. Web.

Bai, L., et al. (2018) ‘Air pollution forecasts: An overview’, International Journal of Environmental Research and Public Health, 15(4), p. 780. Web.

Chi, M., et al. (2021) ‘Determining the scale of coal mining in an ecologically fragile mining area under the constraint of water resources carrying capacity’, Journal of Environmental Management, 279, p. 111621. Web.

Creswell, J. W. (2014) Qualitative, quantitative and mixed methods approaches. London, Uk: Sage Publications.

Dietze, M. C., et al. (2018) ‘Iterative near-term ecological forecasting: Needs, opportunities, and challenges’, Proceedings of the National Academy of Sciences, 115(7), pp. 1424-1432. Web.

Handiwibowo, G., et al. (2020) ‘Strategic fit implication of technological innovation capabilities for SMEs with new product development’, Management Science Letters, 10(12), pp. 2875-2882. Web.

Hazarika, N., and Zhang, X. (2019) ‘Evolving theories of eco-innovation: A systematic review’, Sustainable Production and Consumption, 19, pp. 64-78. Web.

Hobday, A. J., et al. (2019) ‘Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources’, ICES Journal of Marine Science, 76(5), pp. 1244-1256. Web.

Honig, B., and Samuelsson, M. (2021) ‘Business planning by intrapreneurs and entrepreneurs under environmental uncertainty and institutional pressure’, Technovation, 99, p. 102124. Web.

Jiang, L., et al. (2021) ‘COD Optimization prediction model based on CAWOA-ELM in water ecological environment’, Journal of Chemistry, 2021. Web.

Jiang, S., et al. (2019) ‘Prediction of ecological pressure on resource-based cities based on an RBF neural network optimized by an improved ABC algorithm’, IEEE Access, 7, pp. 47423-47436. Web.

Kearney, M. R., et al. (2018) ‘Field tests of a general ectotherm niche model show how water can limit lizard activity and distribution’, Ecological Monographs, 88(4), pp. 672-693. Web.

Kelly, A. (2019) ‘Apple and Google named in US lawsuit over Congolese child cobalt mining deaths’, The Guardian, 16. Web.

Lazonick, W., and Hopkins, M. (2021) ‘Why the CHIPS are down: Stock buybacks and subsidies in the US semiconductor industry’, Institute for New Economic Thinking Working Paper Series, 165. Web.

Lee, J. H., and Woo, J. (2020) ‘Green new deal policy of South Korea: Policy innovation for a sustainability transition’, Sustainability, 12(23), p. 10191. Web.

Lee, Y. J., and Lin, S. Y. (2020) ‘Vulnerability and ecological footprint: a comparison between urban Taipei and rural Yunlin, Taiwan’, Environmental Science and Pollution Research, 27(28), pp. 34624-34637. Web.

Li, J. X., et al. (2019) ‘Evaluation and analysis of ecological security in arid areas of Central Asia based on the emergy ecological footprint (EEF) model’, Journal of Cleaner Production, 235, pp. 664-677. Web.

Li, Y., et al. (2018) ‘On neural networks and learning systems for business computing’, Neurocomputing, 275, p. 1150-1159. Web.

Lin, F., et al. (2018) ‘Sustainability assessment of Taiwan’s semiconductor industry: A new hybrid model using combined analytic hierarchy process and two-stage additive network data envelopment analysis’, Sustainability, 10(11), p. 4070. Web.

Liu, Y., et al. (2022) ‘A long short‐term memory‐based model for greenhouse climate prediction’, International Journal of Intelligent Systems, 37(1), pp. 135-151. Web.

Longva, K.K., Strand, Ø. and Pasquine, M. (2020) ‘Entrepreneurship education as an arena for career reflection: the shift of students’ career preferences after a business planning course’, Education + Training, 62(7/8), pp. 877-896. Web.

Menon, S. (2019) ‘Critical challenges in enterprise resource planning (ERP) implementation’, International Journal of Business and Management, 14(7). Web.

Miśkiewicz, R. (2020) ‘Efficiency of electricity production technology from post-process gas heat: Ecological, economic and social benefits’, Energies, 13(22), p. 6106. Web.

Nemitz, E., et al. (2020) ‘Potential and limitation of air pollution mitigation by vegetation and uncertainties of deposition-based evaluations’, Philosophical Transactions of the Royal Society, 378(2183), p. 20190320. Web.

Nobanee, H. (2020) ‘Big data in business: A bibliometric analysis of relevant literature’, Big Data, 8(6), pp. 459-463. Web.

Page, T., et al. (2018) ‘Adaptive forecasting of phytoplankton communities’, Water Research, 134, pp. 74-85. Web.

Shamshirband, S., et al. (2019) ‘Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters’, Engineering Applications of Computational Fluid Mechanics, 13(1), pp. 91-101. Web.

Shukalov, A. V., Zharinov, I. O., and Zharinov, O. O. (2020) ‘Green cyber-production of semiconductor electronic’, IOP Conference Series: Earth and Environmental Science, 548(5), p. 052047. Web.

Sodhi, M. S., and Tang, C. S. (2019) ‘Research opportunities in supply chain transparency’, Production and Operations Management, 28(12), pp. 2946-2959. Web.

Stock, T., et al. (2018) ‘Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential’, Process Safety and Environmental Protection, 118, pp. 254-267. Web.

Stone, M., et al. (2020) ‘Artificial intelligence (AI) in strategic marketing decision-making: a research agenda’, The Bottom Line, 33(2), pp. 183-200. Web.

Tagliapietra, S., and Veugelers, R. (2020) ‘A green industrial policy for Europe’, Bruegel Blueprint, 31. Web.

Tsai, B. H., and Chen, C. C. (2020) ‘Forecasting interactive relationships and long-term equilibrium of CO2 emissions in industrial interdependent regions’, Carbon Management, 11(1), pp. 57-67. Web.

Tutak, M., and Brodny, J. (2019) ‘Forecasting methane emissions from hard coal mines including the methane drainage process’, Energies, 12(20), p. 3840. Web.

White, E. P., et al. (2019) ‘Developing an automated iterative near‐term forecasting system for an ecological study’, Methods in Ecology and Evolution, 10(3), pp. 332-344. Web.

Wu, Y. K., et al. (2018) ‘Probabilistic wind-power forecasting using weather ensemble models’, IEEE Transactions on Industry Applications, 54(6), pp. 5609-5620. Web.

Zeinalnezhad, M., et al. (2020) ‘Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System’, Journal of Cleaner Production, 261, p. 121218. Web.