Operational Forecasting Workshop for Finance folks Dr Steve - - PowerPoint PPT Presentation

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Operational Forecasting Workshop for Finance folks Dr Steve - - PowerPoint PPT Presentation

Nov 25, 2022 116 likes •567 views

Operational Forecasting Workshop for Finance folks Dr Steve Morlidge Unilever 1978 2006 roles include: Controller Unilever Foods UK ($1 billion turnover) 2002 2006 Leader Dynamic Performance Management Change Project

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Operational Forecasting Workshop

…for Finance folks

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Dr Steve Morlidge

Unilever 1978 – 2006 roles include:

  • Controller Unilever Foods UK ($1 billion turnover)
  • 2002 – 2006 Leader Dynamic Performance Management

Change Project (part of Unilever’s Finance Academy) Outside Unilever

  • Chairman of BBRT 2001 – 2006
  • BBRT Associate/ Non Executive Board Member 2007 -
  • 2006 - Founder Director Satori Partners Ltd
  • 2005 – PhD Hull University (Management Cybernetics)
  • 2007 – Visiting Fellow Cranfield University
  • 2009 - Publish book ‘Future Ready: How to Master Business

Forecasting’

  • 2010 Editorial Board of Foresight Magazine
  • 2011 Founder CatchBull (Forecasting Performance

Management Software)

  • 2017 Publish ‘The Little Book of Beyond Budgeting’
  • 2018 Publish ‘The Little Book of Operational Forecasting’
  • 2019 Publish ‘Present Sense: the Art and Science of

Performance Reporting, with the Brain in Mind’

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Agenda

  • 1. Mutual Introductions
  • 2. Hopes and fears
  • 3. Forecasting Fundamentals
  • 4. The Challenges
  • 5. Forecasting Game
  • 6. Better Forecasting in practice
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Forecasting Fundamentals

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Operational Forecasting Simulation

  • Demand = two die
  • Three decisions
  • What should we hold in stock to start (X)?
  • What do we think we will sell next period (F)?
  • What should we order from our suppliers at the end of every period (Y)?

Period 1 Period 2 Period 3 Opening Stock X (Remaining + Order) Calculated Demand Dice Dice Dice Remaining (X-Dice) (Opening – Dice) Calculated Next period forecast F F F Order Y Y Y

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Forecasting Exercise Debrief

  • How did you decide?
  • What are the commercial consequences of less than perfect

forecasting?

  • Too much stock
  • Too little stock
  • How well did you do?
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Two types of stock

Cycle Stock Safety Stock Safety Stock Perfect Forecast Start End Cycle Stock Cycle Stock Safety Stock Safety Stock Over Forecast Start End Cycle Stock Safety Stock Safety Stock Under Forecast Start End

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Safety Stocks and Service Levels

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How is real life different?

  • Demand changes
  • We can’t predict demand perfectly
  • Failure to predict changes
  • Over forecast
  • Under forecast
  • We don’t know the probability distribution in advance
  • Many products
  • Different demand patterns
  • Different characteristics
  • Shelf life
  • Cost
  • Margin
  • Strategic significance
  • Sensitivity to failure to supply
  • Replenishment lead times
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The Challenges

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Forecasting Exercise

  • 1. Pair up
  • 2. Person 1
  • think of a number (n.n)

between 5 and 10 (include 1 decimal point) – don’t disclose it

  • With eyes closed try to stop

the clock at n.n seconds

  • Reset and repeat 10 times
  • 3. Person 2
  • Record results
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Stopwatch Game

  • 1. Record actuals (A)
  • 2. Guess the targets
  • 3. Calculate the average A

and compare to guess

  • 4. Add hidden target (B)

and calculate absolute difference (C) and average

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Debrief

  • How far adrift were your guesses (percentage) ?
  • Why?
  • What caused the ‘actuals’ to vary?
  • Could this be eliminated?
  • Could this be predicted/forecast?
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ERROR

BIAS VARIATION UNDER- FORECAST OVER- FORECAST AVOIDABLE UN- AVOIDABLE

Economics of Forecast Error

STORAGE COSTS OBSOLESENCE COSTS FINANCING COSTS

Delayed benefit

STOCK

Immediate benefit

Cash Benefit

LOST SALES EXPEDITING COSTS

Profit Benefit

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Forecasting Game

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Forecasting Exercise

1. Working in pairs – given 10 months of history

1. Produce a forecast for the next month – statistics/judgement 2. Get the actuals for the month 3. Calculate the forecast error 4. Repeat for 14 periods 5. Calculate the average error

2. After

1. The winner gets a prize 2. Compare vs ‘perfect’ forecast 3. Calculate impact of avoidable forecast error 4. Calculate value added/destroyed

3. Debrief – what have you learned?

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Forecasting Exercise - Template

Provided Forecast –

  • ne period

in advance Calculate Plot actuals and forecasts

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Alternative forecasts

  • 60
  • 40
  • 20
20 40 60 80 100 120 Jan- 19 Feb- 19 Mar- 19 Apr- 19 May- 19 Jun- 19 Jul- 19 Aug- 19 Sep- 19 Oct- 19 Nov- 19 Dec- 19 Jan- 20 Feb- 20 Mar- 20 Apr- 20 May- 20 Jun- 20 Jul- 20 Aug- 20 Sep- 20 Oct- 20 Nov- 20 Dec- 20

Best Forecast

  • 80
  • 60
  • 40
  • 20
20 40 60 80 100 120 Jan- 19 Feb- 19 Mar- 19 Apr- 19 May- 19 Jun- 19 Jul- 19 Aug- 19 Sep- 19 Oct- 19 Nov- 19 Dec- 19 Jan- 20 Feb- 20 Mar- 20 Apr- 20 May- 20 Jun- 20 Jul- 20 Aug- 20 Sep- 20 Oct- 20 Nov- 20 Dec- 20

Naive Forecast

  • 60
  • 40
  • 20
20 40 60 80 100 120 Jan- 19 Feb- 19 Mar- 19 Apr- 19 May- 19 Jun- 19 Jul- 19 Aug- 19 Sep- 19 Oct- 19 Nov- 19 Dec- 19 Jan- 20 Feb- 20 Mar- 20 Apr- 20 May- 20 Jun- 20 Jul- 20 Aug- 20 Sep- 20 Oct- 20 Nov- 20 Dec- 20

Actual Forecast

  • 80
  • 60
  • 40
  • 20
20 40 60 80 100 120 Jan- 19 Feb- 19 Mar- 19 Apr- 19 May- 19 Jun- 19 Jul- 19 Aug- 19 Sep- 19 Oct- 19 Nov- 19 Dec- 19 Jan- 20 Feb- 20 Mar- 20 Apr- 20 May- 20 Jun- 20 Jul- 20 Aug- 20 Sep- 20 Oct- 20 Nov- 20 Dec- 20

Actual Forecast

  • 80
  • 60
  • 40
  • 20
20 40 60 80 100 120 Jan- 19 Feb- 19 Mar- 19 Apr- 19 May- 19 Jun- 19 Jul- 19 Aug- 19 Sep- 19 Oct- 19 Nov- 19 Dec- 19 Jan- 20 Feb- 20 Mar- 20 Apr- 20 May- 20 Jun- 20 Jul- 20 Aug- 20 Sep- 20 Oct- 20 Nov- 20 Dec- 20

Actual Forecast

Winner Non Winner

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Forecasting Exercise Debrief

  • 1. Analysis of results
  • 2. Discuss in tables

1. How well did you feel that you were doing? 2. Were you surprised/disappointed by the results? 3. How easy did you find the exercise? 4. How could you do things better? 5. Has it changed your view about operational forecasting?

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What could this be worth?

Cost of Sales Per €1bn revenue1 Total Cost of Error 4%-6% €20m-€30m Value Added by Forecasting 0%-2% €0m-€10m Avoidable Error 1%-3% €5m-€15m Avoidable Inventory2 €1m-€5m

1Assuming 50% Gross Margin 2Assuming 10 weeks stock cover

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Better Forecasting in Practice

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How VALUE is ADDED

Simple error statistics are MISLEADING Because volatile demand patterns are less FORECASTABLE

10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100

Absolute Error % Demand Volatility %

Top 200 Packs by Size

Destroying Value Adding Value

also ‘same as last time’* is the ultimate ALTERNATIVE TO FORECASTING

* naïve forecast

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Country B Country A Country C

Forecastability trap: case study

Because of differences in forecastability, the region with the lowest errors…

1 Country A 7% 2 Country B 11% 3 Country C 47% 1 Country C 88% 2 Country B 16% 3 Country A 6% 1 Country C 0.53 2 Country B 0.70 3 Country A 1.02

Mean Absolute Error Demand Volatility

(Forecast error vs naïve error*)

Relative Absolute Error

(MAE/DV) * Naïve forecast = sell this month what we sold last month

Actuals Forecast

…often doesn’t have the best forecast

Error

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Any other questions?