organisation chart with latex

Question

Third Level Organizational Chart using Latex Tikz

adding branches directly under the Controller to set up his secretariat and certain resposibilities directly under him--how do i do it??

Answer 1

You can always add some TikZ commands to add features to the tree. In this case this is the \draw command at the very end. I also gave the root node a name using alias. To generate more space, you need to nudge the tree a bit by adjusting the fork sep and adding some yshift, say.

\documentclass[border=20pt,tikz]{standalone}
\usepackage[edges]{forest}

\forestset{
  direction switch/.style={
    for tree={edge+=thick, font=\sffamily},
    where level>=1{folder, grow'=0}{for children=forked edge},
    where level=3{}{draw},
  },
}
\begin{document}

\begin{forest}
  % forest preamble: determine layout and format of tree
  direction switch,
  for tree={fork sep=3em}
  [Life Prediction Approaches and Techniques,yshift=3em,alias=LP
    [Model-base
      [Physics-of-Failure
      ]
      [Satistical Model
        [Proportion Hazard Model]
        [Logistics Regression Model]
        [Cumulative Damage Model]
      ]
      [Kalman/Particle Filtering
      ]
      [Nonlinear Dynamics
      ]
    ]
    [Knowledge-base
      [Expert Systems
        [Rule-Based]
        [Model-Based]
        [Case-Based]
      ]
      [Fuzzy Logics
      ]
    ]
    [Experience-base
      [Parametric Distribution
        [Location Scale \& Log-Location Scale]
        [Normal \& Lognormal]
        [Smallest \& Largest Extreme Values]
        [Something Beginning with W]
        [Logistic \& Log-Logistic]
      ]
      [Nonparametric Distribution
      ]
    ]
    [Data-driven
      [Multivariate Statistical Method
        [Principal Component Analysis]
        [Something \& Something Else]
        [Another Thing]
        [A Final Thing Here]
      ]
      [Black-Box Methods
        [Decision Trees]
        [Multilayer Perceptions]
        [Neural Networks]
        [Radial Basis Functions]
        [Vector Quantification]
      ]
      [Signal Analysis
        [Auto-Something Here]
        [Fourier Transform]
        [Filters]
        [Tidal Functions]
      ]
      [Graphical Model
        [Bayesian Network]
        [Hidden Markov Networks]
      ]
      [Self-Organising Feature Maps
      ]
    ]
  ]
\draw[thick]
([yshift=-1.5em]LP.south)  -- ++(-6em,0) node[left,draw,font=\sffamily,thin]{ABC}
([yshift=-1.5em]LP.south)  -- ++(6em,0) node[right,draw,font=\sffamily,thin]{XYZ};
\end{forest}
\end{document}

OLDER ANSWER: This is as simple as adding a level and shifting the numbers in the where clauses by 1.

\documentclass[border=20pt,tikz]{standalone}
% ateb: https://tex.stackexchange.com/a/271349/ addaswyd o gwestiwn OOzy Pal: https://tex.stackexchange.com/q/271170/
% https://tex.stackexchange.com/a/271349/194703
\usepackage[edges]{forest}

\forestset{
  direction switch/.style={
    for tree={edge+=thick, font=\sffamily},
    where level>=2{folder, grow'=0}{for children=forked edge},
    where level=4{}{draw},
  },
}
\begin{document}

\begin{forest}
  % forest preamble: determine layout and format of tree
  direction switch,
  [Life Prediction Approaches and Techniques
   [ABC
    [Model-base
      [Physics-of-Failure
      ]
      [Satistical Model
        [Proportion Hazard Model]
        [Logistics Regression Model]
        [Cumulative Damage Model]
      ]
      [Kalman/Particle Filtering
      ]
      [Nonlinear Dynamics
      ]
    ]
    [Knowledge-base
      [Expert Systems
        [Rule-Based]
        [Model-Based]
        [Case-Based]
      ]
      [Fuzzy Logics
      ]
    ]
   ]
  [XYZ  
    [Experience-base
      [Parametric Distribution
        [Location Scale \& Log-Location Scale]
        [Normal \& Lognormal]
        [Smallest \& Largest Extreme Values]
        [Something Beginning with W]
        [Logistic \& Log-Logistic]
      ]
      [Nonparametric Distribution
      ]
    ]
    [Data-driven
      [Multivariate Statistical Method
        [Principal Component Analysis]
        [Something \& Something Else]
        [Another Thing]
        [A Final Thing Here]
      ]
      [Black-Box Methods
        [Decision Trees]
        [Multilayer Perceptions]
        [Neural Networks]
        [Radial Basis Functions]
        [Vector Quantification]
      ]
      [Signal Analysis
        [Auto-Something Here]
        [Fourier Transform]
        [Filters]
        [Tidal Functions]
      ]
      [Graphical Model
        [Bayesian Network]
        [Hidden Markov Networks]
      ]
      [Self-Organising Feature Maps
      ]
    ]
   ]    
  ]
\end{forest}
\end{document}