When you have little knowledge of the factors, ranges, reproducibility of the process or measurement system, or of the results relative to the project goals, before committing time and materials to an experimental campaign consider using an economical minimum run scoping design to check the current experimental space.

A small carefully chosen set of baseline experiments will help to determine whether the equipment and method, together with the factors and ranges you have selected, are likely to achieve the desired goals repeatedly. Using typically just 4 experiments, run the process or method at:

• the mildest conditions or settings
• the most forcing conditions or settings
• repeated centre point settings to provide a pure noise estimate and evidence of whether you are currently operating over an optimum

If it is unlikely you will achieve the goals, you can use the results of a scoping study to help you decide what to do next: add or drop factors; stretch or contract ranges to encompass or focus on an optimum region of your experimental space; follow up with a variation management study or measurement systems analysis due to excessive variation between the centre point results.

To handle scoping designs within the Statease Design Expert DX7^TM software tool, we have further tipsheets to help you:

   Scoping Designs: 2 Building the Design
   Scoping Designs: 3 Analysing and Interpreting your Results

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Building the Design (Factorial Tab: 2-Level Factorial)

 

Choose New Design from the file menu

Select the lowest run design for the Number of Factors you want to study & enter the number of replicated Centre points (e.g., 2) you wish to run to establish the level of noise

Continue to enter the details of the factors and responses. Right click on the Block column of the resulting design and select Sort by Point Type. The repeated centre points will be the first rows in the layout

Leaving the centre point rows and the following two rows alone (reserve these for the mild and forcing conditions), highlight and delete the remaining rows

For the 2 rows reserved for the mild and forcing conditions, overwrite the cell entries in the design layout to the settings you want to run. Display the coded settings of -1 & +1 to help you edit the cell entries for the mild & forcing conditions

Overwrite the run order and then Sort by Run Order to create your preferred order (e.g., run centre points at the start and end of the study to pick up any time related bias)

Additionally, you can Insert a descriptive Categoric factor to label the results relating to mild, forcing and centre point conditions on subsequent plots to avoid misinterpreting factor effects

Warning: mild & forcing conditions in a scoping study are unlikely to both be included in the original fractionated design. To ensure they both appear follow the directions on the right to set up the design

Design Tip: you can always set up the scoping study in Excel & import it into Design Expert for analysis using the Historical Data feature 

Run the Experiments and Enter the Data

 

The mild and forcing settings for the factors in the design layout are not always respectively the Low (L) and High (H) settings in the factor input dialogue

	Mild					Forcing
Batch Size	L					H
Liquid Volume	L					H
Inlet Air Temp	L					H
Spray Rate	L					H
Screen Size	H					L
Fluid Air Volume	L					H

 

 

 

 

 

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Analysing your Results

 

Do not attemt to analyse data from a scoping study as you would a screening study. You cannot test the effects of the parameters, since all the factors are varying simultaneously from their mild-to-centre-to-forcing settings. That means the factors are completely correlated or aliased; leading to just one effect)! Instead click on Graph Columns under the Design node on the left-hand tree structure

Select a factor from the X axis list, or if you created a descriptive factor (e.g., Condition in this example) you should use this. Note, the factor you choose represents all factors changing from mild to forcing

Cycle through all the responses in the Y axis list. You can Colour By each response to emphasize changes in result from mild-to-mid-to-forcing

Check the range & pattern of response values against the response goal or target. Also assess the background variation – differences between the repeat centre point results – is not exceesive and will provide good signal:noise ratio in subsequent studies (e.g., screening). If the results fail to meet your needs, they should suggest a direction for further improvement or additional work.

Analysis Tip: do not expect a scoping experiment to provide you with the same amount of information as a screening or optimisation study. They are not designed to identify which factor(s) are having an effect, or provide a detailed understanding the effects of the parameters in order to establish ideal settings or robust ranges for the parameters

Interpreting your Results

 

Simply interpret the graph columns plots. In this example, you will not achieve the goal for tablet Hardness of >11kp & the target for Dissolution Time of 240 mins ± 20mins using the mild settings. However, the centre point and forcing results suggest that these goals are achievable and are likely to lie inside this space. Use this knowledge to input into the next study (e.g., screening). If you needed to increase Hardness still further, you would need to stretch the forcing conditions beyond their current settings. You can always add experiments in a direction suggested by the scoping study results for verification.

Curvature in the dissolution response over the scoping range suggests you are currently operating over an optimum for this response. The centre point results for both responses are comparable providing some evidence of reproducibility.

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Two-Level Factorial and Fractional Factorial Designs are useful for either screening the vital few factors affecting your process from the many tested, or to evaluate the robustness of your process to small variations of the parameters in a design space predicted to conform to your acceptance criteria. Typically these are resource-efficient studies comprising 10 – 20 experiments.

To handle these design types within the Design Expert DX7 software tool, we have further tipsheets to help you:

   Two-Level Designs: Building the Design
   Two-Level Designs: Analysing your Results
   Two-Level Designs: Diagnosing your Results
   Two-Level Designs: Interpreting your Results

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Build the Design (Factorial Tab: 2-Level Factorial)

 

Choose New Design from the file menu. Select the cell corresponding to the Number of Factors you wish to investigate and the number of Runs you want to perform.

Select the number of Replicates, Blocks and Centre Points required (see Design Tips below)

Click Continue to view the alias pattern and decide allocation of factors to letters; and again to enter the details of the factors and responses

Colour key: The fractionation (superscript), colour and resolution (subscript) indicate how much information you can expect from the design about the key effects

White (Completely Safe) – all effects are capable of being independently estimated

Green (Safe, Go) – main effects & 2-Factor Interactions can be estimated separately of other key effects

Yellow (Caution) – main effects are aliased with 3-Factor Interactions, but 2-Factor Interactions are aliased with one another

Red (Stop, Think) – main effects are aliased with 2-Factor Interactions 

Replicates of factorial combinations, and/or replicated centre points, provide an estimate of the background variability (pure error). Including centre points also enables you to test whether a potential optimum exists within your current ranges (e.g., max hardness/min degradation etc.)

Blocks manage the experiments when they must be performed by different operators, equipment or on different days and these variations are expected to introduce bias

Factor Types can either be Numeric i.e on a continuous scale between Low & High Settings, or Categoric i.e. with discrete settings (e.g., type of lubricant)

Fractionating: The more you fractionate to save resource, the more the effects you would like to investigate get partnered up, or aliased, and so the poorer the resolution of your design

Run the experiments and Enter the Data

 

For each experiment run in Run order, enter the results for each response into the Design (Actual) sheet. An alternative Run Sheet view, for carrying out & recording your results offline, is available under the View menu

 

Display: Right-click on a column heading to display options:

• Std - sort your design into standard order
• Run - sort into a randomised run order
• Type - display type of point e.g. Factorial
• Factor - edit information e.g. ranges
• Response - e.g. simulate results

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