Chapter 3: Trend Analysis

Trend analysis is arguably the most frequently used analytical method in business analytics. Trend analysis is intrinsically tied to the time dimension. For example, comparing values from a historical period with those from the current period yields actionable business insights. This chapter covers several common trend analysis methods, including seasonal year-on-year (YoY) analysis, year-to-date (YTD) YoY analysis, month-end (or week-end) value comparison analysis, and analysis of changes in aggregated values. 3.1 Seasonal Year-on-Year Analysis Business scenario: Fast-moving consumer goods (FMCG) are often subject to seasonal influences—for instance, water-sports apparel. When analyzing such products, attention must be paid to seasonal YoY changes. Visualization: Cyclical chart; in Power BI, the "small multiple line chart". Data source: Example—Supermarket.xls New measures: Sales Amount 1 = SUM('Orders'[Sales Amount]) Number of Distinct Years = CALCULATE(DISTINCTCOUNT('Date Table'[Year]), ALL('Orders')) // i.e., 4 years Monthly Average Sales Amount = CALCULATE([Sales Amount 1], ALL('Date Table'[Year])) / [Number of Distinct Years] 3.2 Non-seasonal Period-on-Period Analysis Business scenario: Industries whose sales are not significantly affected by seasonality—for example, IT. Case study: Comparing Apple Inc.’s average stock price for the current quarter against its average stock price for Q1 of the same fiscal year. Visualization: Clustered column and line chart (combo chart) Data source: Apple stock data New columns (fields): Year = YEAR([Date]) Quarter = FORMAT([Date], "Q") Growth vs. Q1 = [Average Price] - [Average Stock Price for Q1 of Current Year] New measures: Average Price = AVERAGE(AAPL[close]) Average Stock Price for Q1 of Current Year = CALCULATE('AAPL'[Average Price], AAPL[Quarter] = 1) 3.4 Average vs. End-of-Period Value Analysis Business scenario: Data such as inventory levels, employee headcount, and transaction prices are semi-additive—that is, they cannot be meaningfully aggregated across time. For example, summing daily bank account balances yields no meaningful insight. For such data, analysis may instead rely on averaging or on end-of-period values (e.g., month-end or week-end values) for mid-term performance evaluation. In this case, summing monthly stock price changes is meaningless; however, calculating the monthly average stock price and the month-end closing price is highly informative. Visualization: Line chart Data source: Apple stock data New measures: Average Price = AVERAGE(AAPL[close]) Last Selected Date = LASTDATE('AAPL'[Date]) Month-End Closing Price = CALCULATE([Average Price], FILTER('AAPL', 'AAPL'[Date] = DATE(YEAR([Last Selected Date]), MONTH([Last Selected Date]), DAY(MAXA('AAPL'[Date])))))

Transitioning from Bioethanol to Food and Feed: Green Plains Innovatively Expands Corn’s Multiple Uses

Maximizing the value of every single kernel of corn is the mission of Green Plains (NASDAQ: GPRE), headquartered in Omaha, Nebraska. As an emerging leader in the ethanol industry, the company is innovating its production processes to develop food and feed ingredients—including protein, yeast, fiber, and corn oil. Currently, Green Plains operates 11 biorefineries, processing 260–300 million bushels of corn annually to produce 750 million–1 billion gallons of ethanol—ranking fourth in the United States and globally. The company’s market capitalization stands near $2 billion, with annual revenue of $2.5–$3 billion. As this “ethanol producer” expands into food and feed markets, Green Plains has pursued strategic acquisitions and partnerships. It now operates three innovation centers—located in Omaha and York, Nebraska, and Shenandoah, Iowa—dedicated to developing fish feed and other “ultra-high” protein and corn yeast products. The flagship Omaha Innovation Center opened in 2022 and features a demonstration center, laboratories, and a production facility. It also houses an advanced, commercial-scale aquaculture feed plant—Fluid Quip Technologies’ lab space—as well as dedicated lab space reserved for future university collaborations. In 2021, Green Plains acquired a controlling stake in Fluid Quip Technologies, along with its Maximized Stillage Co-products (MSC™) technology and trademarks. This acquisition became a key accelerator of Green Plains’ transformation. MSC™ provides the core technological engine enabling Green Plains to produce novel high-protein feed ingredients: a backend mechanical “fractionation process” that allows dry-mill ethanol plants to extract 50%-purity protein from residual corn fiber, carbohydrates, and oil left over after ethanol distillation. This capability is central to expanding corn’s utility. Green Plains incorporates these novel high-protein feed ingredients into the complete feed formulations supplied by its joint venture, Optimal Aquafeed—delivering growth and health benefits far exceeding those of conventional protein-based feeds. Green Plains acquired Optimal Aquafeed in 2020, gaining control of both Optimal Aquafeed (pet fish food) and Optimal Fish Food (aquaculture farm feed) businesses. Fish feed is growing increasingly vital. Population growth drives rising demand for protein—including fish. Half of all fish consumed by humans today comes from aquaculture. Herbivorous species such as carp and tilapia thrive on plant-rich diets; carnivorous species like trout, bass, and salmon require high-protein feed; and salmon feed must contain over 20% fat. Optimal Aquafeed formulates its feeds using whole wheat, soybean meal, and dried distillers grains, supplemented with poultry by-products, fishmeal, wheat middlings, and proprietary ingredients produced by Green Plains.

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Green Plains Renewable Energy Inc. stock seasonal returns chart covers 2016 to 2026. Shares closed at $13.62 on October 29, reflecting a 0.73% decline.

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