Consumer demand is one of the key factors of a company’s continuous success. The relationship between both the consumer and enterprise which is based on trust, total risk, and benefit-sharing awareness, achieving competitive advantage besides creating greater performance revolves around consumer demand (Zӓpfel, 2010). According to Babakus, Yavas and Ashill (2015), two factors of measuring consumer demand is consumer satisfaction and consumer loyalty. Internal and external risks such as competition, technology failure, labour unrest, inflation, recession as well as change in government laws are several condition risks and uncertainties that companies need to face consistently. Companies could determine the demand for its services through demand forecasting process to lessen the adverse risks that the company may face.
Value for Demand
The process of finding values for demand in future time periods is defined as demand forecasting (Tiemessen, 2017). Consumer demand for services is necessary to determine the capacity, facility planning, capital investments, departmental budgets, marketing plans and human resource activities. In fact, turnover, profit margins, capital expenditure, risk assessment and mitigation plans are dependent on demand forecasting (Pratsini, 2017). It is imperative for companies to prioritise demand forecasting as there are multiple variables involved in this industry such as workforce, training requirements and workforce efficiency.
Demand forecasting is important for companies in terms of huge profitability as it helps to reduce uncertainties and business risk in the future. Besides that, demand estimation enables companies to undertake critical business decisions. Capital arrangement and manpower planning decision could be easier to determine if companies are aware of the demand expected to further increase its services. The company could also alter its business and marketing strategies to satisfy the expected demand by consumers (Schaver Michael, 2015). Furthermore, demand forecasting serves as a good aid in devising pricing strategies. Moreover, the company could budget accordingly besides assessing its performance in comparing actual demand with the management’s expectations. Demand forecasting acts as a check and balance in this way. Companies could make adjustments to its business model in identifying how far its demand forecast is off from the actual demand (Azorin, 2015). This method also allows companies to produce pro forma financial statements in terms of planning and tracking the company’s overall performance. In short, demand forecasting would help companies to efficiently prepare beyond the current period.
Each scope has expanded and become a critical part of a company’s strategic planning with the proliferation of technical tools, machine learning and automation that are available today. The impact of digitalization on future consumer demand forecasting and production planning could be observed through three mediums which are data transparency, processing large data sets and data visualization. Data transparency comprising data integration across the company provides visibility of data within various departments which is the keystone of many digital transformation initiatives (Pertusa-Ortega, 2015). Hence, forecasts of data are more comprehensive and accurate with the evolvement of digitalization which could help companies to have a broader understanding of operations that impact the overall financial forecasts.
Finance teams of a company would have to process large data sets in forecasting when big data initiatives are included in the company’s transformation efforts (Cortes, 2015). In addition, data from across the company could be married with other data platforms such as historical and environmental industry data and perhaps social media too with machine learning and predictive contributing to the company’s growth. In terms of data visualization, decisions could be made across the company through data visualization tools and financial dashboards which simplifies the job (Mudipi, 2015). Therefore, everyone involved could have a clear understanding of the critical data collected which further improves the forecasting process besides enhancing the growth of the company.
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- Stuart Bermon, Sarah Jean Hood, 2016, “Capability Optimization Planning System (CAPS),” Interfaces, Vol. 29, No. 5, pp. 31-50.
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