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

Question 1: Trend Analysis, Forecasting Models, and Technology Assessment (20 marks: 7/7/6)

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 Shape Trends

Trend analysis is the systematic study of historical data to identify the underlying direction and rate of change in a variable over time, so that management can anticipate future conditions rather than merely react to them. Its purpose is to support planning decisions—capacity, staffing, product development, and investment—by distinguishing genuine long-run movement from short-term noise and seasonal fluctuation. In engineering management it informs when a technology will mature, when demand will justify new capacity, and when a product will require replacement. The major forces that influence trends are economic (growth cycles, interest rates, disposable income), technological (innovation, obsolescence, automation), social and demographic (population change, values, lifestyle), political and regulatory (legislation, trade policy, environmental rules), and competitive (rival strategies, market entry and exit). A sound analysis recognizes that these forces interact, so a trend is rarely explained by a single cause.

(ii) Characteristics of the Three Forecasting Models

(a) Time-series models forecast a variable purely from the historical pattern of its own past values, decomposing the series into trend, seasonal, cyclical, and irregular components. They assume the past pattern continues, which makes them powerful for stable processes but weak at anticipating structural breaks. (b) The moving-average model smooths a series by averaging the most recent n observations, dropping the oldest as each new one arrives; a longer window smooths more heavily and responds more slowly, a shorter window is more responsive but noisier. It is simple and transparent but lags at turning points and weights all included periods equally. (c) Exponential smoothing applies exponentially decreasing weights to older observations through a smoothing constant α between 0 and 1: the new forecast equals α times the latest actual plus (1−α) times the previous forecast. A high α is responsive to recent change, a low α is stable; extensions (Holt, Holt–Winters) add trend and seasonal terms.

(iii) Questions Asked in a Technology Assessment or Audit

A technology assessment audits the organization's technological position and the impact of adopting or retaining a technology. The questions typically asked are: What technologies do we currently use, and how do they compare with best available practice? Where is each technology on its life cycle (emerging, growth, mature, declining)? What is its impact on cost, quality, and capability? What are the risks—obsolescence, single-supplier dependence, safety, regulatory exposure? What competitive advantage does it confer, and are competitors ahead? What investment is required to upgrade or replace it, and what is the return? And what are the wider environmental, social, and legal consequences of the technology?

Practical Application

A manufacturer of industrial pumps would use time-series decomposition on ten years of order data to separate a modest upward trend from strong seasonality, apply Holt–Winters exponential smoothing with a moderate α to forecast the coming year, and feed that into capacity planning. In parallel, a technology audit would reveal that its casting process is mature and energy-intensive relative to a competitor's near-net-shape method, prompting a business case for re-investment.