As I thought, you have repeats. I would do the following:
1. First set your data table up so you have the Treatment combinations grouped (there should be 20 rows of each treatment combination, a total of 160 rows) and 1 column for the response variable (the 20 repeats should correspond to the 20 rows for each treatment combination). If you had the 20 data points for each treatment as separate columns in your data table, then simple Stack those columns.
2. Graph the within treatment data. You can use the Variability (Analyze>Quality and Process>Variability/Attribute Gauge Chart) or Graph Builder. If Variability Chart, use run order or treatment combination in the X, Grouping. You can also plot the distributions within treatment (Analyze>Distribution (By treatment). Look at the data. Are there any unusual data points? If not , you will need to determine what enumerative statistics best describe the central tendency and variation of those distributions.
3. Summarize the within treatment variation (due to repeats: pc-to-pc, within pc and measurement error components). Table>Summary> highlight the Y and select the appropriate statistics from the Statistics drop down menu (You will likely select multiple statistics)>Put the factor columns in the Group window. This will get you back to the 8 treatments with summary statistics for each treatment.
4. Perform your Analysis of the experiment Always Practical>Graphical>Quantitative. Does the data makes sense? How does it compare to your predictions, your hypotheses? Did the Y change of any practical significance? How you analyze the data is a personal choice. I suggest saturating the model and getting Normal, Pareto and Bayes plots to analyze un-replicated designs (e.g., Analyze>Fit Model). Identify the insignificant effects and remove them from the model, then re-run the analysis to get residual plots.
"All models are wrong, some are useful" G.E.P. Box