Given an inconsistent system of linear equations, it seems always possible to make it consistent by introducing new variables and adding them in convenient places.
For a baby example, given the system of equations \begin{align*} x + y &= 0 \\ x + y &= 1 \end{align*} one can make it consistent by adding a new variable $w$ to get \begin{align*} x + y &= 0 \\ w + x + y &= 1 \end{align*} (I shouldn't have added it to both equations, however.)
Is there a systematic method to achieve this? How many variables do I need to add to a system of $n$ equations in $n$ unknowns? Is there a "degree of inconsistency" (e.g. the number of equations I need to take out to get a consistent system of equations)?
Write your system like this:
$$a_{11}x_{1} + a_{12}x_{1} + \cdots + a_{1n}x_{n} = b_1$$ $$a_{21}x_{1} + a_{22}x_{2} + \cdots + a_{2n}x_{n} = b_2$$ $$\vdots$$ $$a_{m1}x_{1} + a_{m2}x_{2} +\cdots + a_{mn}x_{n} = b_m$$
Or equivalently as $Ax = b$:
$$\underbrace{\begin{pmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ a_{21} & a_{22} & \cdots & a_{2n} \\ \vdots & \vdots & \cdots & \vdots \\ a_{m1} & a_{m2} & \cdots & a_{mn} \\ \end{pmatrix}}_{A}\underbrace{\begin{pmatrix} x_1 \\ x_2 \\ \vdots \\ x_n\end{pmatrix}}_{x} = \underbrace{\begin{pmatrix} b_1 \\ b_2 \\ \vdots \\ b_m\end{pmatrix}}_{b}$$
This system being inconsistent is equivalent to the fact that $b$ is not in the linear span of the columns of the matrix $\{A_1, A_2, \ldots, A_n\} \subseteq \mathbb{R}^m$ where $A_j = \begin{pmatrix} a_{1j} \\ a_{2j} \\ \vdots \\ a_{mj}\end{pmatrix}$.
In particular, the set $\{A_1, A_2, \ldots, A_n\}$ does not span the space $\mathbb{R}^m$, meaning that $$\dim \operatorname{span}\{A_1, A_2, \ldots, A_n\} < m$$
Adding additional unknowns to the system in the way you propose amounts to adding more vectors to this set, until the set is large enough so that $b$ is contained in its linear span.
Namely, adding an extra unknown to the $i$-th equation augments the matrix like this:
$$\begin{pmatrix} a_{11} & a_{12} & \cdots & a_{1n} & 0\\ a_{21} & a_{22} & \cdots & a_{2n} & 0\\ \vdots & \vdots & \cdots & \vdots & \vdots\\ a_{i1} & a_{i2} & \cdots & a_{in} & 1\\ \vdots & \vdots & \cdots & \vdots & \vdots\\ a_{m1} & a_{m2} & \cdots & a_{mn} & 0\\ \end{pmatrix}\begin{pmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \\ w\end{pmatrix} = \begin{pmatrix} b_1 \\ b_2 \\ \vdots \\ b_m\end{pmatrix}$$
The set of columns is now $\{A_1, \ldots, A_n, e_j\}$. Since the unit vectors $\{e_1, \ldots, e_m\}$ span $\mathbb{R}^m$, it is clear that if you add enough of them into $\{A_1, \ldots, A_n\}$, you will achieve that $b$ is in the span of the matrix columns.
If adding unknowns multiplied by a scalar is allowed, then you can simply add $b$ into the set $\{A_1, \ldots, A_n\}$ so that you have
$$\begin{pmatrix} a_{11} & a_{12} & \cdots & a_{1n} & b_1\\ a_{21} & a_{22} & \cdots & a_{2n} & b_2\\ \vdots & \vdots & \cdots & \vdots & \vdots\\ a_{m1} & a_{m2} & \cdots & a_{mn} & b_m\\ \end{pmatrix}\begin{pmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \\ w\end{pmatrix} = \begin{pmatrix} b_1 \\ b_2 \\ \vdots \\ b_m\end{pmatrix}$$
which is trivially solvable with $x_1 = \cdots = x_m = 0$ and $w = 1$.