Explanation
An outlier is a data point that falls outside of the expected range of the data, i.e., it is an unusually large or small data point1. Outliers can have a significant adverse impact on the forecasts, as they can skew the data distribution and distort the statistical analysis2. Therefore, it is important to detect and remove outliers from the demand data before generating forecasts.
One of the techniques that can be used to detect outliers is to use the standard deviation of the data, or the equivalent z-score, to determine the outlier limit3. For example, one approach is to set the lower limit to three standard deviations below the mean, and the upper limit to three standard deviations above the mean. Any data point that falls outside this range is detected as an outlier.
However, detecting outliers is not enough. The most appropriate next step would be to screen the outlier for manual review. This means that the detected outlier should be examined by a humanexpert to determine whether it is a true outlier or not, and whether it should be corrected or not4. This is because not all outliers are erroneous or irrelevant. Some outliers may be valid observations that reflect real changes in demand, such as seasonal peaks, promotional effects, or market trends. In such cases, correcting or removing the outliers may lead to inaccurate or biased forecasts.
Therefore, screening the outlier for manual review can help verify the cause and validity of the outlier, and decide on the best course of action. Some of the possible actions are:
Correcting the outlier: replacing the outlier with a more typical value based on historical data or expert judgment. This can smooth out the data and reduce the noise.
Separating the demand streams: splitting the data into two or more series based on different factors that influence demand, such as product type, customer segment, or distribution channel. This can isolate the outliers and allow different forecasting methods to be applied to each series.
Adjusting the forecasting model: modifying the parameters or assumptions of the forecasting model to account for the outliers, such as using a different smoothing factor, trend component, or error term. This can improve the fit and accuracy of the model.
References: 1: Outlier Definition 1 2: How to Forecast Data Containing Outliers 2 3: How to Detect Outliers in Machine Learning - 4 Methods for Outlier Detection 1 4: How Outlier Detection and Correction Works 4 :
How to Understand What is an Outlier in Forecasting 3

Explanation
A benefit of the ISO 9000 series of specifications is that purchasers may accept ISO 9001 certifications, minimizing additional surveys. ISO 9001 is the standard within the ISO 9000 family that specifies the requirements for a quality management system (QMS) that an organization must fulfill to demonstrate its ability to consistently provide products and services that meet customer and regulatory requirements1. ISO
9001 certification is a third-party verification that an organization has implemented and maintained a QMS that conforms to the ISO 9001 standard2. By obtaining ISO 9001 certification, an organization can provide objective evidence of its quality performance to its customers, suppliers, regulators, and other stakeholders3. This can reduce the need for additional audits or surveys by the purchasers, as they can rely on the ISO 9001 certification as a proof of quality assurance4. This can save time, money, and resources for both the purchasers and the suppliers, as well as improve their trust and confidence in each other5.
References: 1: ISO 9000 Vs. 9001 3 2: ISO 9000 Standard: Benefits, How to Achieve 4 3: The Ultimate Guide to ISO 9000 5 4: ISO 9000 Certification Guide 1 5: ISO - Selection and use of the ISO 9000 family of standards 6

Explanation
In a lean environment, the batch-size decision for planning "A" items would be done by lot-for-lot (L4L). A lean environment is a production system that aims to eliminate waste and maximize value by applying the principles and practices of lean manufacturing1. "A" items are the most important items in an inventory system, based on the Pareto principle or the 80/20 rule, which states that 80%of the effects come from 20% of the causes2. Lot-for-lot (L4L) is an inventory ordering policy that orders exactly the quantity needed to meet the demand for each period3.
The reason why L4L is the preferred batch-size decision for planning "A" items in a lean environment is because it minimizes the inventory holding costs and reduces the risk of obsolescence or deterioration of the items3. L4L also supports the concept of pull production, which is a key element of lean manufacturing. Pull production is a method of controlling the flow of materials and information by producing only what is requested by the downstream customers or processes4. L4L aligns the production and consumption rates of
"A" items, which are typically high-demand and high-value items, and avoids overproduction or underproduction. L4L also enables faster feedback and learning, as well as better responsiveness to customer needs and expectations.
The other options are not as suitable for planning "A" items in a lean environment. Least total cost is an inventory ordering policy that orders the quantity that minimizes the sum of ordering costs and holding costs5.
However, this policy does not consider the demand variability or customer service level, and may result in large batch sizes that increase inventory levels and waste. Min-max is an inventory ordering policy that orders a fixed quantity whenever the inventory level falls below a minimum level6. However, this policy does not reflect the actual demand or consumption rate, and may result in excess inventory or stockouts. Periodic order quantity is an inventory ordering policy that orders a variable quantity at fixed time intervals. However, this policy does not synchronize the production and consumption rates, and may result in mismatched supply and demand.
References: Lean Manufacturing - Definition & Principles - ASQ; Pareto Principle - Definition & Examples - Investopedia; Lot-for-Lot (L4L) Definition | Operations & Supply Chain Dictionary; Pull Production - Definition & Examples - ASQ; Economic Order Quantity (EOQ) Definition - Investopedia; Min-Max Inventory Management: Definition & Examples - Video & Lesson Transcript | Study.com; [Periodic Order Quantity (POQ) Definition | Operations & Supply Chain Dictionary].

Explanation
Locating service facilities differs from locating manufacturing or distribution facilities primarily because service location decisions are driven by revenue concerns, while manufacturing and distribution location decisions are driven by costs. This is because service facilities are usually closer to the customers and depend on their demand and preferences. Service facilities need to consider factors such as customer convenience, accessibility, visibility, traffic, and competition when choosing a location, as these factors affect the revenue potential and market share of the service facility1. Manufacturing and distribution facilities, on the other hand, are usually farther from the customers and depend on their supply chain efficiency and effectiveness. Manufacturing and distribution facilities need to consider factors such as transportation, labor, utilities, taxes, and regulations when choosing a location, as these factors affect the cost structure and profitability of the facility2.
The other options are not correct. Competition is a factor that affects both service and manufacturing or distribution location decisions, as it influences the market attractiveness and strategic positioning of the facility3. Real estate costs are also a factor that affects both service and manufacturing or distribution location decisions, as they represent a significant portion of the fixed costs of the facility4. Surveying customers or suppliers is a method that can be used for both service and manufacturing or distribution location decisions, as it provides valuable information about the demand and supply characteristics of the market5.
References : Service Facility Location: A Review of Applications and Methods; Facility Location - Factors Influencing the Location; Competitive Environment: Definition, Examples & Factors - StudySmarter US; Facility Location | IntechOpen; Seven Key Factors to a Facility Location - Chron.com.

Explanation
A move from acceptance sampling to 100% inspection would be caused by the circumstance of downstream operators encountering recurring defects. Acceptance sampling is a quality control technique that uses statistical sampling to determine whether to accept or reject a production lot of material. It is employed when one or several of the following hold: testing is destructive; the cost of 100% inspection is very high; and 100% inspection takes too long1. 100% inspection is a quality control technique that examines every item in a production lot for defects or nonconformities. It is employed when the cost of passing a defective item is very high; testing is nondestructive; and 100% inspection does not take too long2.
Downstream operators are the workers or machines that perform the subsequent operations or processes on the products after they have been inspected or tested. Downstream operators encountering recurring defects means that the products that have passed the acceptance sampling or testing are still found to be defective or nonconforming by the downstream operators. This can indicate that the acceptance sampling or testing is not effective or reliable in detecting or preventing defects or nonconformities. This can also result in negative consequences, such as rework, waste, delays, customer complaints, or safety issues. Therefore, this circumstance would cause a move from acceptance sampling to 100% inspection, as it would require a more thorough and rigorous quality control technique to ensure that no defective or nonconforming products are passed to the downstream operators.
The other options are not circumstances that would cause a move from acceptance sampling to 100% inspection. History shows that the quality level has been stable from lot to lot is not a circumstance that would cause a move from acceptance sampling to 100% inspection, but rather a circumstance that would support the use of acceptance sampling. Quality level is the proportion of conforming items in a production lot. Quality level being stable from lot to lot means that there is little variation or fluctuation in the quality of the products over time. This can indicate that the production process is under control and consistent in meeting the quality standards or specifications. Therefore, this circumstance would support the use of acceptance sampling, as it would reduce the risk of accepting a defective lot or rejecting a conforming lot.
The company uses one of its qualified suppliers is not a circumstance that would cause a move from acceptance sampling to 100% inspection, but rather a circumstance that would support the use of acceptance sampling. A qualified supplier is a supplier that has met certain quality, delivery, and service standards and has been approved by the company to supply goods or services without inspection or testing. A qualified supplier is expected to maintain a high level of performance and reliability, as well as to report any issues or deviations that may affect the delivery process. Therefore, this circumstance would support the use of acceptance sampling, as it would reduce the need for 100% inspection by relying on the supplier's quality assurance system.
The percent of defects is expected to be greater than 5% is not a circumstance that would cause a move from acceptance sampling to 100% inspection, but rather a circumstance that would require a change in the acceptance sampling plan. The percent of defects is the proportion of defective items in a production lot. The percent of defects being expected to be greater than 5% means that there is a high probability of finding defective items in the production lot. This can indicate that the production process is out of control or inconsistent in meeting the quality standards or specifications. Therefore, this circumstance would require a change in the acceptance sampling plan, such as reducing the acceptable quality limit (AQL), increasing the sample size, or decreasing the acceptance number, to increase the likelihood of rejecting a defective lot.
References := Acceptance Sampling - an overview | ScienceDirect Topics, What Is Acceptance Sampling?
Definition And Examples