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Manufacturing processes for any product is like putting together a puzzle. Products are pieced together step by step, and keeping a close eye on the process is important.

For this project, you're supporting a team that wants to improve how they monitor and control a manufacturing process. The goal is to implement a more methodical approach known as statistical process control (SPC). SPC is an established strategy that uses data to determine whether the process works well. Processes are only adjusted if measurements fall outside of an acceptable range.

This acceptable range is defined by an upper control limit (UCL) and a lower control limit (LCL), the formulas for which are:

The UCL defines the highest acceptable height for the parts, while the LCL defines the lowest acceptable height for the parts. Ideally, parts should fall between the two limits.

Using SQL window functions and nested queries, you'll analyze historical manufacturing data to define this acceptable range and identify any points in the process that fall outside of the range and therefore require adjustments. This will ensure a smooth running manufacturing process consistently making high-quality products.

The data

The data is available in the manufacturing_parts table which has the following fields:

  • item_no: the item number
  • length: the length of the item made
  • width: the width of the item made
  • height: the height of the item made
  • operator: the operating machine
Spinner
DataFrameas
alerts
variable
-- Write your query here
-- Flag whether the height of a product is within the control limits
SELECT
	b.*,
	CASE
		WHEN 
			b.height NOT BETWEEN b.lcl AND b.ucl
		THEN TRUE
		ELSE FALSE
	END as alert
FROM (
	SELECT
		a.*, 
		a.avg_height + 3*a.stddev_height/SQRT(5) AS ucl, 
		a.avg_height - 3*a.stddev_height/SQRT(5) AS lcl  
	FROM (
		SELECT 
			operator,
			ROW_NUMBER() OVER w AS row_number, 
			height, 
			AVG(height) OVER w AS avg_height, 
			STDDEV(height) OVER w AS stddev_height
		FROM manufacturing_parts 
		WINDOW w AS (
			PARTITION BY operator 
			ORDER BY item_no 
			ROWS BETWEEN 4 PRECEDING AND CURRENT ROW
		)
	) AS a
	WHERE a.row_number >= 5
) AS b;

Extended Project below

After identifying individual out-of-control products, the team suspects that certain operators may need further training. They want to pinpoint operators whose machines consistently produce parts outside control limits.

Using common table expressions and aggregations you will identify operators whose machines have a higher-than-average number of alerts compared to the total alerts for all operators.

Spinner
DataFrameas
df
variable
WITH total_alerts AS (
    SELECT 
        operator, 
        COUNT(*) AS operator_alerts
    FROM 
        manufacturing_parts
    WHERE 
        -- Assuming 'out-of-control' parts are identified by some condition, e.g., length, width, height out of specific range
        length > 100 OR width > 50 OR height > 30
    GROUP BY 
        operator
),
average_alerts AS (
    SELECT 
        AVG(operator_alerts) AS avg_alerts
    FROM 
        total_alerts
)
SELECT 
    ta.operator, 
    ta.operator_alerts
FROM 
    total_alerts ta, 
    average_alerts aa
WHERE 
    ta.operator_alerts > aa.avg_alerts;