A house of quality (HOQ) chart is a product planning matrix that is used to show how customer requirements relate directly to the ways and methods companies can use to achieve those requirements. HOQ charts are part of the quality function deployment (QFD) method, which helps to ensure quality in product development and service delivery. HOQ charts use a design that resembles the outline of a house, with different sections representing different aspects of the product or service1. One of the functions that a HOQ chart aligns is competitive analysis with product design. Competitive analysis is the process of evaluating the strengths and weaknesses of the competitors in the market, and identifying the opportunities and threats they pose to the company.Product design is the process of creating the features, functions, and specifications of the product or service that meet the customer needs and expectations. A HOQ chart aligns these two functions by comparing the company's product design with the competitors' product design, and showing how well the company's product design satisfies the customer requirements. This helps the company to identify the areas of improvement, differentiation, and innovation in the product design, and to create a competitive advantage in the market23. References: 1 House of Quality Tutorial - How to Fill Out a House of Quality | ASQ 4 2 House of quality | Explanation with example - IONOS 5 3 CPIM Exam References - Association for Supply Chain Management 1

Forecast error is the difference between the actual demand and the forecasted demand for a given period.
Forecast error can be caused by various factors, such as changes in customer preferences, market conditions, competitor actions, or random variation. Forecast error can be measured using different methods, such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), or tracking signal. Forecast error typically triggers forecast revision when it is continually increasing, which indicates that the forecast model is not capturing the underlying demand pattern or trend. A continually increasing forecast error can lead to poor customer service, excess or obsolete inventory, or lost sales opportunities. Therefore, it is important to monitor the forecast error and revise the forecast when necessary to improve the forecast accuracy and reliability.
Forecast error does not trigger forecast revision when it is used in computing the tracking signal, associated with the introduction stage of the product life cycle, or caused by random variation. These are not valid reasons for revising the forecast, as they do not indicate a systematic or persistent deviation from the actual demand. References:
CPIM Part 1 Study Guide, Chapter 4: Demand Management, Section 4.2: Forecasting Techniques and Performance Measurement CPIM Part 2 Study Guide, Chapter 3: Demand Management, Section 3.1: Demand Planning A Critical Look at Measuring and Calculating Forecast Bias, Section: What Is Forecast Bias?
How Can Forecast Error be Calculated?, Section: Introduction