SPSS sees an MA(1) process (that is the (0,0,1) term) and no seasonality in the data (that is the (0,0,0) term). An MA(1) term has very little impact on the point forecast. Here is an example in R with toy data, where I artificially inflate every 13th observation. Actuals in black, in-sample fits in red. Note how the forecast in blue is almost flat, although the series and the fits show clear spikes:
xx <- rnorm(200)
xx[13*(1:15)] <- xx[26*(1:5)]+8
model <- arima(x=xx,order=c(0,0,1),seasonal=list(order=c(0,0,0),period=52))
ARIMA models can be extremely unintuitive. I suggest you play around with simulated ARIMA(p,d,q) models to get a feeling for them. I always recommend this textbook to learn more about forecasting.
To return to your data: you have giant spikes there, and an ARIMA model won't be able to explain those and forecast them. I suggest that you go back to the data source and see whether you can find some explanatory variables (e.g., promotions) to build a causal model. Such a model would allow you to forecast the spikes in the future again.