Complete en examengerichte samenvatting Operations Management
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Course
Operations Management (F000242A)
Institution
Universiteit Gent (UGent)
The summary includes everything seen in the lectures, guest lectures & interpretation of formulas. It is exam-oriented, to the point and the ideal preparation for understanding everything in order to have enough time to do exercises.
,1 INTRODUCTION
Operations management= the planning, scheduling and controlling the transformations of
inputs into goods & services. These inputs can be materials, people and knowledge.
Decision horizon
• OPERATIONAL Short-term
→ Purchase, stock, production level ...
• TACTICAL
→ Workforce, forecasting ...
• STRATEGIC
→ New facilities, sales channels ... Long-term
Sustainability as operations strategy
• Lean management: sustainable operations → cut costs
• Sustainable innovations → increase efficiency
• Sustainability as marketing strategy → increase sales
Productivity
𝑂𝑈𝑇𝑃𝑈𝑇
𝑃𝑅𝑂𝐷𝑈𝐶𝑇𝐼𝑉𝐼𝑇𝑌 = = 𝐸𝐹𝐹𝐼𝐶𝐼𝐸𝑁𝐶𝑌 + 𝐸𝐹𝐹𝐸𝐶𝑇𝐼𝑉𝐸𝑁𝐸𝑆𝑆
𝐼𝑁𝑃𝑈𝑇
𝑂𝑈𝑇𝑃𝑈𝑇
𝑃𝐴𝑅𝑇𝐼𝐴𝐿 𝑃𝑅𝑂𝐷𝑈𝐶𝑇𝐼𝑉𝐼𝑇𝑌 =
𝑃𝐴𝑅𝑇𝐼𝐴𝐿 𝐼𝑁𝑃𝑈𝑇
Little’s law
𝐼 =𝑅×𝑇
→ Waiting times cause WIP inventory
↔ LEAN: eliminate waiting time → Min WIP
→ Waiting time is caused by:
• Mismatch between demand & supply
• Uncertainty
Customer Order Decoupling Point
= Inventory point in the value chain where the product is linked to specific order
UPSTREAM DOWNSTREAM
Low inventory to deal with demand Produce for inventory to shorten lead time
uncertainty & product variety & save by producing batches
→ Production based on actual orders → Production based on forecasts
➔ PULL strategy ➔ PUSH strategy
→ Master Production Scheduling (MPS) is done at place of the CODP
2
, 2 FORECASTING
2.1 STATIONARY SERIES
Stationary series are series without a real trend.
Moving Averages MA(n)
𝑡
1
𝐹𝑡+1 = ∑ 𝐷𝑖
𝑛
𝑡−𝑛+1
Exponential Smoothing ES(𝛼)
𝐹𝑡+1 = 𝛼 × 𝐷𝑡 + (1 − 𝛼) × 𝐹𝑡
→ Form of weighted moving averages considering all previous observations with an
exponentially declining weight for older observations.
Comparison
2
• For 𝛼 = 𝑁+1 both methods have the same level of accuracy (not same forecast)
• Stable when:
o 𝑛 is high
o 𝛼 is low
• Both always lag a trend behind (mend for stationary series)
• MA(n) requires to save last n observations while ES(𝛼) only needs the last one
2.2 NON-STATIONARY SERIES
Regression models have a trend: 𝐷𝑡 = 𝑎 + 𝑏 × 𝑡
• 𝑎=𝐷 ̅ − 𝑏 × (𝑛+1)
2
𝑆
• 𝑏 = 𝑆𝑥𝑦
𝑥𝑥
Double Exponential Smoothing (Holt)
𝐹𝑡,𝑡+𝜏 = 𝑆𝑡 + 𝜏 × 𝐺𝑡
Triple Exponential Smoothing (Winter)
𝐹𝑡,𝑡+𝜏 = (𝑆𝑡 + 𝜏 × 𝐺𝑡 ) × 𝑐𝑡+𝜏−𝑛
Alternative method for seasonality
1. Deseasonalize data: divide each observation by its seasonal relative
o Divide all observations by the mean
o Average (& normalize) per season
2. Forecast on deseasonalized data
3. Multiply forecast by the corresponding seasonal relative
2.3 PERFORMANCE MEASURES
1
Mean Absolute Deviation 𝑀𝐴𝐷 = ∑|𝑒𝑖 |
𝑛
1
Mean Squared Error 𝑀𝑆𝐸 = ∑(𝑒𝑖 )2
𝑛
1 |𝑒𝑖 |
Mean Absolute Percentage Error 𝑀𝐴𝑃𝐸 = ( ∑ ) × 100%
𝑛 |𝐷𝑖 |
3
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