Nov 18, 2013 - Conceptual geographical & demographic analysis. > Making assumptions! > Carrying out review of
MAKING LOGISTICS DECISIONS USING EXCEL MODELS Supply Chain Network 18 November 2013 Kirsten Tisdale
OUTLINE > A bit about what I do > How simple Excel models can help put the facts and figures around decisions > Things to consider before, during and after any decision > Why most projects should be like a lemon
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A BIT ABOUT WHAT I DO > Aricia provides direction to corporate clients that are planning change in their physical logistics: > Helping to develop ideas – operational, new business > Putting facts and figures around options > High level supply chain modelling - end-to-end trade-offs > Conceptual geographical & demographic analysis > Making assumptions!
> Carrying out review of existing solutions and suggestions > Data visualisation and analysis > Industry research > MapPoint Training > UKAS technical expert / assessor for storage and distribution accreditation
WHAT IS AN EXCEL MODEL?
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DECISIONS BETTER MADE WITH FACTS > All companies have drivers for change within their logistics operation: > Strategic change > Industry direction > Pet ideas > Forced hand > Growth or volume loss > Sweating assets… > And a need to make decisions > Decisions are better made with facts and figures > It’s common sense
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WHY EXCEL? > Good for What ifs > Simple when nothing else needed > Easy to add complexity when required > Can be easily shared > No hidden mysteries… > Anyone can work through the decisions the model has made
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A FEW RESULTS FROM EXCEL MODELS > 10% of significant vehicle fleet removed > Stopped £10m being wasted on an inappropriate project > Win-win - union supported changes that protected company profit and maintained driver wages – 13.5% cost increase avoided > Surprise element found to be key in end to end supply chain costs from manufacture through to sales floor > Targets identified to give 5+ years life to a manual picking operation > Stopped a parts distribution operation make the wrong decision to move to centralised DC
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TRADE-OFFS > Pre-retailing - getting the garment ready for the sales floor > Saving in store, by spending elsewhere in the supply chain > Trade-offs depend on circumstances - don’t make assumptions
Image from: Aricia Limited based on client data
FROM ASSUMPTIONS TO ACTION > > > > > > > > > > > >
Client had decided to source parts from China Making big savings in purchase cost, but with …increased logistics New centralised DC put out to tender Quandary: how to assess cost results? Answer: get in some consultants! Labyrinth 40K SKUs - real variety of large and tiny parts, including within departments Client had dimensions/weights for 1K SKUs I collected dims/weights collected for just 44 additional SKUs And then created an assumed stock profile Imaginary warehouse designed by another Labyrinth team member using that profile Tender submitted by most likely provider within 3% of our calculated cost Client decided DC route too expensive
HOW TO MEASURE CONGESTION? 1/3 > Existing operation, manual picking starting to get congested, big growth expected over next 5 years, older site > Collated lots of data on real picking trips
> Spent time in aisles – what does congestion in the peak hour look like – how many people does it take to create congestion > Analysed how many people were in key aisles every quarter minute for peak hour
HOW TO MEASURE CONGESTION? 2/3
5yrs if no change 5yrs with targets
Current picture
HOW TO MEASURE CONGESTION? 3/3 > Targets set: > Flatten the peak hour of week – probably by flattening peak day > Improve the hit rate of trays on a trolley that access busy aisles – do they have to visit that aisle? > Speed up time for actual pick > Mix and match products in busy / quieter aisles > Targets seemed to have worked! > But what about measuring congestion for an operation that doesn’t yet exist?
DECISIONS > Getting going with an Excel model > Taking decisions > Afterwards
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GETTING GOING WITH A MODEL > Write down the objective …and keep returning to it > Have separate input page/s/area and make every variable variable! > Keep it simple – in particular don’t make any one equation too complex > If you’re having difficulty thinking through a formula in Excel, write down in plain English how you would do it in real life > Use data to populate a model for different scenarios, to identify key ratios… > Record every decision / assumption
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ASSUMPTIONS > Often needed for filling in gaps > > > >
Not a dirty word… Provided you know you’re making them You state them clearly as such You know the basis on which you made them
> > > >
Walk the supply chain Interview staff Capture gut feel Sift fact from fiction - as important as analysing data
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TAKING DECISIONS > Cross check your results - gross error analysis, triangulation, top down versus bottom up > Use your model for sensitivity analysis and breakpoints > Use your model to set targets / baskets of targets > Risk analysis > Understand the cost of change > Make the decision!
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FOOT-TAPPERS AND HEAD-SCRATCHERS
90%?
AFTERWARDS > Plan the work, work the plan > Do your spring cleaning nice and early > Review, review, review > Make sure there is responsibility for delivery – including / particularly? less tangible benefits > Build key assumptions and targets into normal monitoring
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WHY A PROJECT SHOULD BE LIKE A LEMON
Sift fact & fiction Model scenarios Data collection Conclusions Visit sites Understand aims Analysis Talk to staff
Understand risks Decision Share results
Assess options External research Ideas & concepts
Image on this and other pages from: http://www.bonappetit.com/test-kitchen/tools-test-kitchen/article/lemon-peel
EXCEL …AND OTHER TOOLS > Key software I use includes: > Excel > Multiple sheets for multiple operations > Macros for batch results > Ease for client
> MapPoint – also Microsoft > Visualisation > Geocoding > Batch mileages
> Omniscope – more specialist …and expensive > Multiple view visualisation > Interactive data mining > Data heavy projects
EXCEL …AND OTHER TOOLS Build a model – varying complexity Macro for multiple results
Excel
Visualise and analyse – get the stories
Ease of client use
Omniscope Analyse multiple results Presentation of results
> But that’s another story…
Industry research and other assumptions
Data usually arrives as Excel or .csv
Get Lat Longs and / or batch mileages
MapPoint
MAKING LOGISTICS DECISIONS USING EXCEL MODELS Thank you! Any questions?
