Question 1 of 7: Trend Analysis, Forecasting Models and Technology Assessment

Question 1: Trend Analysis, Forecasting Models and Technology Assessment (20 marks)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

(i) Purpose of Trend Analysis and the Forces That Influence Trends

Trend analysis is the examination of historical data over time to identify the underlying direction and pattern of change, so that the future can be anticipated and planned for. Its purpose is to support forecasting and strategic decision making: by revealing whether demand, cost, technology performance, or market share is rising, falling, or cycling, it lets managers anticipate future conditions, allocate resources, set targets, and act proactively rather than reactively. It also helps detect turning points and emerging opportunities or threats early. The major forces that influence trends are the broad external factors captured in environmental scanning: economic forces (growth, interest rates, inflation, employment); technological forces (innovation, automation, obsolescence); social and demographic forces (population change, values, lifestyles, education); political and legal/regulatory forces (legislation, trade policy, taxation); competitive and market forces (rivalry, new entrants, customer preferences); and environmental/ecological forces (resource availability, sustainability expectations). A credible trend analysis interprets historical data in the light of these forces, since it is they that will sustain, accelerate, or reverse an observed trend.

(ii) Characteristics of Three Forecasting Models

(a) Time-series models forecast a variable purely from its own past values ordered in time, on the assumption that the historical pattern will continue. They decompose the series into components—an underlying trend, seasonal variation, cyclical movement, and random irregular noise—and project these forward. They are objective and data-driven but assume the past pattern persists and cannot anticipate structural change. (b) The moving-average model smooths a series by averaging the most recent n observations, dropping the oldest as each new period is added; it dampens random fluctuation to reveal the underlying level. A larger n gives more smoothing but responds more slowly to genuine change, while a smaller n is more responsive but noisier; it weights all included periods equally and lags behind trends. (c) Exponential smoothing also averages past data but assigns exponentially declining weights to older observations through a smoothing constant α (0 < α < 1): the new forecast is the old forecast plus α times the latest forecast error. It therefore reacts more to recent data, requires little data storage, and, with a high α, responds quickly to change while a low α gives heavy smoothing. It is a practical, widely used method, extendable (Holt–Winters) to handle trend and seasonality.

(iii) Questions Asked in a Technology Assessment or Audit

A technology assessment or audit systematically evaluates an organization's technologies against its needs and the competitive environment, and it is guided by a set of searching questions. What technologies do we currently possess and use, and in what state are they? establishes the baseline. How do our technologies compare with the best available and with competitors'? benchmarks the gap. Where does each technology sit on its life cycle—emerging, growing, mature, or obsolete? judges its remaining value. Which technologies are core to our competitive advantage, and which are peripheral? sets priorities. What emerging technologies could threaten or benefit us, and when? looks outward and forward. What are the costs, risks, and benefits of adopting, upgrading, or divesting a technology? informs investment. Do we have the skills, capacity, and infrastructure to exploit our technologies? tests readiness. How well do our technologies align with customer needs and strategic goals? confirms fit. Answering these questions reveals technological strengths and gaps and guides decisions on research, acquisition, and retirement of technology.

Practical Application

A firm forecasting demand for a product line would use time-series decomposition on several years of sales, applying exponential smoothing with a moderate α to track a rising trend responsively while filtering noise, and interpreting the result against economic and technological forces. In parallel, a technology audit—asking where each production technology sits on its life cycle and how it compares with competitors'—would flag an ageing process nearing obsolescence, prompting a timely reinvestment decision.