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Operations Management, Operations Management Business Side - Coggle Diagram

- - - - done across 3 time horizons
        
        aggregate scheduling < 5 years into the future
        
        master scheduling < a few months into the future
        
        dispatching immediate scheduling
  - - - 1: set the criteria, standards and objectives for operations processes
      - 2: measure the state and performance of individual processes
      - 3: compare the measured data to expectations
      - 4: act decisively in different time horizons to bring the process in line with objectives
- - - - small volume, high variety
      - work in parallel
      - product stationary
      - complex organisational task
      - extensive and highly specialised workers
    - - small volume specialised products
      - uses general-purpose tooling
      - highly skilled workers
      - planning difficult due to irregularity and uncertainty
    - - larger volume
      - flexible manufacturing systems
      - pre-planned and documented tech
      - worker activities in small, incremental steps
      - mix of workers with different skills
    - - large/ large volume of products
      - high degree of automation
      - mainly unskilled workforce
      - low flexibility and variety
      - integral products: line op
      - dimensional products: continuous op
    - - new manufacturing process incorporating IT and production operations systems enabled by IT
      - industrie 4.0, smart factory
      - use modelling, analysis, simulation and real time control of all factory equipment to optimise efficiency
      - enables unprecedented flexibility and low cost
  - - - highly customised
      - delivered by experts (often professionals)
    - - intermediate customisation and volumes
      - delivered by semi skilled workers
    - - high/highest volume
      - automated where possible
      - low-skilled workers
- - - - based on assumptions that future behaviour of the subject under investigation is in some way shaped by its past behaviour
      - investigate variables that change over time
      - existence of trends in unrelated variables leads to false insights if incorrect statistical methods are applied
      - non-trivial, highly technical, decomposes complex behaviour of variables over time
      - different trendlines fitted to historical demand using many software packages
      - moving averages used to identify underlying patterns
    - - uses variables that don't change over time
      - less complex, use relatively simple regression methods in cross-sectional statistical studies
      - statistically infer causal relationships between a dependent variable and a cross-section of independent variables, used to predict change in dependent variables