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![Service level within Labatt
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![CDP [%] for different SKU types and regions
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Final_presentation_MatthiasVanKerkhove_SimonWostyn
- 1. Improving service levelof single source SKUs June 24th Matthias Van Kerkhove & Simon Wostyn
- 2. Overview 2 § Problem statement §Background § Problem analysis and root causes - Inventoryanalyses - Product supply systems flow – supply planning and deployment § National pull system - Definition and network configuration - What-ifmodel for 2015 - Inventoryand deployment policies - Test results (innovation vs. non-innovation SKUs) § Comments § Action plan and implementation
- 3. Problem statement &Objective 3 Problem statement Labatt is witnessing growth in a lot of areas that can be considered “non- traditional” to its core business. These strategically important areas are characterized by many SKUs, they are single source (only produced in one brewery) and represent small volumes. These SKUs traditionally receive lower service levels than Labatt’score business. Objective Achieve core product service levels on national single source SKUs (SS SKUs) while maintaining supply chain losses at a comparable/budgeted level. Design the processes, rules and tools that will be embedded to the business to achieve this goal.
- 4. Single source SKUs §595 Single Source SKUs - 50 innovation SKUs less than 1 year on the market - 504 non-innovation SKUs more than 1 year on market - 33 craft SKUs - 8 overlap SKUs § Out of scope: import/export, co-packing 4
- 5. Service level withinLabatt 5 § Impact [hl] = demand [hl] – sales [hl] § Customer Delivery Performance = CDP [%] § CDP [%] = "#$%&' [)*] "#$%&' )* ,-%*.- [)*]
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- 9. CDP [%] fordifferent SKU types and regions Innovation SKUs are the biggest hitters 9 1.4% 0.9% 1.0% 0.2% *CDP = "#$%&' "#$%&',-%*.- Non-SS SKUs
- 10. 10 § Stock-out dueto network shortage - Stock-outs in all/many DC’s due to too low forecast or production issues § Stock-outs due to network dispersion - Stock-outs in certain DC’s while enough/too much inventory in other DC’s à inventory at the wrong location at the wrong time Types of stock-outs Two types of stock-outs are identified
- 11. 11 § Inventory levelanalysis - Impact vs. average inventoryin network - e.g. X% of yearly impact in weeks with Y DOI in the network - Impact in weeks with more than 7 DOI à network dispersion - Impact in weeks with less than 7 DOI à network shortage § Inventory spread analysis - Dispersion = 34567 (59: ;<.= %** =.>";?%* 5@A-) C76DCE6 (59: ;<.= %** =.>";?%* 5@A-) - Dispersion > 0.5 è inventory at the wrong location at the wrong time Identification of stock-outs Two types of inventory analyses are used
- 12. Results National SS SKUsmore network shortages and more dispersed vs. non-SS SKUs Inno SKUs more network shortages and equally dispersed vs. non-inno SKUs 12 SS vs. non-SS Inno vs. non-inno
- 13. 13 Conclusions of theinventory analysis § Single source SKUs experience more network shortages and are deployed less efficiently than non-single source SKUs. § Innovation SKUs experience more network shortages than non-innovation SKUs. Deployment is equally inefficient for innovation and non-innovation SKUs. è Single source SKUs’ inventoryshould be deployed differently
- 14. 14 As-is process andissues Significant room for improvement As-is process Issues as-is process Production and shipments decisions up to 8 weeks in advance Decisions heavily depend on (inaccurate) forecast Deployment based on DC-level forecasts Forecast accuracy is very low at DC-level No standard replenishment policy Deployment heavily depends on supply planner’sexpertise Products are pushed out High level of redeployment and SCL High inventorylevels Innovation SKUs First 2 to 3 productions: based on sales intelligence; afterwards: regular process Innovation SKUs Lower forecast accuracy for innovation SKUs
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- 16. 16 Requirements of thesolution Issues as-is process Requirements of the solution Decision heavily depends on (inaccurate) forecast Remove or reduce the influence of the forecast on deployment decisions Separate the deployment and production decisions in time Forecast accuracy is very low at DC-level Avoid making low-level decisions early in the process Deployment heavily depends on supply planner’s expertise Standardized reorder points and reorder quantities High level of redeployment and SCL High inventory levels Reduce redeployment and inventory levels Lower forecast accuracy for innovation SKUs Less dependence on forecasting
- 17. 17 National two-bin replenishmentpull system with a multi-echelon inventory optimization § National two-bin replenishment system - Two-bin pull system - Uses fixed order quantitiescalled “bins” - When one bin is consumed, a signal is sent upstream to replenish it § National cascade of warehouses - Reevaluatedeployment decisions at hubs in the network
- 18. 18 Cascade system Introduction ofhubs in nation-wide network
- 19. 19 Cascade system Introduction ofhubs in nation-wide network
- 20. 20 National two-bin replenishmentpull system with a multi-echelon inventory optimization § 2-bin replenishment system § Cascade of warehouses § Multi-echelon inventory optimization - Applying a pull system to a cascade of warehouses - Minimizing total network safety stock - By determining optimal service level in replenishment DC’s - And keeping end customer service level fixed
- 21. Proof-of-concept by what-ifmodel 21 “What would the results (stock outs, inventory, ...) have been in 2015 if our solution would have been used?” § Simulation of 2015 inventory, shipments, stock-outs, … based on input of 2014 § Comparison with actual inventory levels, service levels, shipments,…
- 22. 22 § Input § Salesdata 2014 § Required service level to end customer § Lead times § Network configuration/product flow § Inventory and deployment policy § Output § Obtained service level § Inventorylevels § Inventoryspread § Constraint § 2015 production schedule implemented in model Proof-of-concept by what-if model
- 23. Inventory policy 23 § Binsizes and safety stock § Re-order point and volume (#bins that are ordered) § How to treat backorders § Warehouse/region prioritization for shipments
- 24. 24 𝑆𝑎𝑓𝑒𝑡𝑦 𝑠𝑡𝑜𝑐𝑘 = 𝑘 ∗ 𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒V.X ∗ 𝐷𝑎𝑖𝑙𝑦 𝑆𝑎𝑙𝑒𝑠 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝐵𝑖𝑛 𝑆𝑖𝑧𝑒 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑎𝑖𝑙𝑦 𝑆𝑎𝑙𝑒𝑠 ∗ 𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒 + 𝑆𝑎𝑓𝑒𝑡𝑦 𝑠𝑡𝑜𝑐𝑘 2 A realistic model only uses 2014 data toestimate these parameters Inventory policy Calculation of bin sizes and safety stock 2014 data
- 25. 25 § First 3monthsof the year à use daily sales of 2014 § After 3 months à resize bin sizes based on sales data of 2015 α = defgh ijkgli ml nglogpq fe rgpst uVvw defgh ijkgli ml nglogpq fe rgpst uVvx 2015 Bin sizes based on 2014 data April Revised bin sizes based on α Inventory policy Rules for bin sizes and safety stock for non-innovation SKUs
- 26. 26 § First 3monthsafter launch à unchanged process § After 3 months à bin sizes based on innovation SKUs in 2014 à rescaled to reflect the volume of the given SKU 𝛼 = 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑 𝑖𝑛 3 𝑚𝑜𝑛𝑡ℎ𝑠 𝑎𝑓𝑡𝑒𝑟 𝑙𝑎𝑢𝑛𝑐ℎ 𝑖𝑛 2015 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑 𝑜𝑓 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 𝑆𝐾𝑈 𝑖𝑛 3 𝑚𝑜𝑛𝑡ℎ𝑠 𝑎𝑓𝑡𝑒𝑟 𝑙𝑎𝑢𝑛𝑐ℎ 𝑖𝑛 2014 2015 No pull implementation 3 months after launch Bin size based on averageinnovation in 2014 and α Inventory policy Rules for bin sizes and safety stock for innovation SKUs
- 27. 27 § If inventorylevel < reorder point à order 1 bin § If inventory level = 0 à order 2 bins § If forecasted sales in 1 week > 3 bins à order sufficient bins to anticipate for huge sales Inventory policy Rules for reordering
- 28. 28 § If ordercannot be delivered à order becomes backorder § Backorder is planned for the next dayuntil order is delivered Inventory policy Rules for backorders
- 29. 29 § Criteria 1:first backorders § Criteria 2: number of bins requested § Criteria 3: size of bins requested (DC importance) Inventory policy Rules for shipment prioritization
- 30. 30 National two-bin replenishmentpull system with a multi-echelon inventory optimization Our solution fulfills the requirements Requirements of the solution Our solution Remove or reduce the influence of the forecast on deployment decisions Separatethe deployment and production decisions in time Pull system Cascade of warehouses Avoid making low-level decisions early in the process Cascade of warehouses Standardized reorder points and reorder quantities Bin sizes and reorder points Reduce redeployment and inventorylevels Pull system and multi-echelon optimization Less dependency on forecasting Pull system
- 31. 31 Simulation result –non-innovation SKU National CDP improvement and inventory reduction Simulation 2015 data Overall CDP in ON 1.01% 0.80% Overall CDP in QC 1.12% 8.36% Overall CDP in the West 3.24% 18.20% Overall CDP national 1.79% 11.26% Average daily inventory national (units) 2962 3722 Inventory reduction national 20.53% Keith’s White 12x341 7x24 (28588)
- 32. 32 Simulation result –non-innovation SKU More stable pattern of inventory levels
- 33. Simulation 2015 data OverallCDP in ON 0.37% 3.27% Overall CDP in QC - - Overall CDP in the West 7.32% 1.07% Overall CDP national 1.85% 2.82% Average daily inventory national (units) 13278 20260 Inventory reduction national 34.46% Shock Rasp 473 10x288 (47875) Simulation result – innovation SKU National CDP improvement and inventory reduction
- 34. 34 Comments § Multi-echelon - Complexityvs. added value - Full application requires specialized software § National network shortages § Two-bin replenishment system - Large sales variability - Lead times exceeding time required to empty a bin § Supply chain losses - But: less lost sales, delay production, inventory reduction § Seasonality - Refine bin size definition for different periods in the year
- 35. 35 Action plan 1. Furtherrefinement and testing model through scenario analyses 2. Aggregateresult for all single source SKUs 3. Development of model for Montreal brewery 4. Incorporation in daily operations 5. Development of a cost model 6. Trial phase evaluated with relevant KPI’s 7. Introduceto other types of SKUs
- 36. Conclusion 36 Objective Achieve core productservice levels on national single source SKUs (SS SKUs) while maintaining supply chain losses at a comparable/budgeted level. Design the processes, rules and tools that will be embedded to the business to achieve this goal. Achievements Proof of service level improvement Proof of inventory level reduction Proof of inventory spread reduction Proof of practical feasibility
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- 39. Route to markets §West - Brewery > 8 DC’s of Beer Distribution Ltd (BDL) > Retail + on-trade - BDL is a joint venture (non-profit with one competitor) § Ontario - Brewery > 1 central T1 > beer store/LCBO - Labatt owns the store (together with two competitors) § Quebec - Brewery > 1 central T1 > 20 small T2’s> directly to more than 20,000 stores/pubs on-tradeor off-trade - We own the entire network § Atlantic - Out of scope - Small volumes 39
- 40. Inventory level vs.inventory spread 40 Inventory spread vs. level Impact when inventory > 7 DOI Impact when inventory < 7 DOI Inventory spread > 0.5 Impact occurred when there was on average a lot of inventory left in the network. However, the spread indicates that some DC’s have a lot of inventory while others have very little. à Stock-outs due to inventory dispersion, hence poor deployment. Impact occurred when there was little inventory left in the network. However, the spread is still large indicating poorly maintained inventory levels. Hence, if product would be available, distribution would still be deficient. à Stock-outs due to network shortages. On top ofthat, deployment is inadequate. Inventory spread < 0.5 N.A. Little inventory was left in the network when impact occurred and all DC’s have more or less the same DOI left. à Stock-outs due to a network shortage. Inventory deploymentis less of an issue.
- 41. 41 Results in TheWest SS SKUs more network shortages and more dispersed vs. non-SS SKUs Inno SKUs more network shortages and more dispersed vs. non-inno SKUs SS vs. non-SS Inno vs. non-inno
- 42. Results in Quebec SSSKUs more dispersed vs. non-SS SKUs Inno SKUs more network shortages and more dispersed vs. non-inno SKUs 42 SS vs. non-SS Inno vs. non-inno
- 43. Results National SS SKUsmore network shortages and more dispersed vs. non-SS SKUs Inno SKUs more network shortages and equally dispersed vs. non-inno SKUs 43 SS vs. non-SS Inno vs. non-inno
- 44. Possible Directions ofsolutions § Improving forecast accuracy - Root cause of CDP hits - Either incorrect or (must be) used too soon in the supply chain § Hold more overall inventory - Negative impact supply chain losses and warehouse costs § Deploy differently à postponement - Mitigatethe effects of (low) forecast accuracy - Room for improvement confirmed by inventoryanalysis 44
- 45. 45 Low payoff High payoff Easy to implement - Premium safety stock Difficult to implement -Increase number of deliveries or productions - Cascade of warehouses – tier as function of brewery distance - Pull/EOQ/Kanban from national to regional (and down) - Fixed time period EOQ Solutions – PICK chart
- 46. 46 National two-bin replenishmentpull system with a multi-echelon inventory optimization § Multi-echelon inventory optimization P T1 T1 T2 T2 T2 SS2 SS2 SS2 SS1 SS1 T2 SS2 1. Fix end customer service level 2. Determine service level at T1’sto 3. Minimize total SS (2*SS1+4*SS2) 99.7% 99.7% 99.7% 99.7% SL? SL?
- 47. 47 National two-bin replenishmentpull system with a multi-echelon inventory optimization Multi-echelon optimization relocates safety stock and reduces network safety stock § Multi-echelon inventory optimization example In this examplethe overall safety stock is reduced by 7% Before ME implementation After ME implementation
- 48. 48 Simulation 2015 data Nationalspread 0.40 0.56 Simulation result – non-innovation SKU Less inventory dispersion
- 49. 49 Simulation 2015 data Nationalspread 0.53 0.65 Simulation result – innovation SKU Less inventory dispersion
- 50. 50 Launch Start pull system Network shortage during summer Simulation result– innovation SKU More stable pattern of inventory levels
- 51. 51 Incorporation in dailyoperations Plan for everyday utilization Steps in the process Responsibilities Demand planning/forecasting Remains as-is – forecasting for coming 8 weeks Supply planning National production based on national forecast and inventory levels – lock-in brewing plan 2 weeks before production No shipment decisions Sequencing Remains as-is – lock-in 1 week before production Deployment Actual shipments based on pull orders independent of production schedule
- 52. 52 Incorporation in dailyoperations Treatment of non-innovation SKUs Bin sizes based on 2014 data April Revised bin sizes 2015 Timing Action First 3 monthsof the year Use daily sales of 2014 After 3 months Resize bin sizes based on sales data of 2015
- 53. 53 Incorporation in dailyoperations Treatment of brand new SKUs Timing Action First 3 monthsafter launch Unchanged process After 3 months Resize bin sizes based on sales data of innovation SKUs in 2014 2015 No pull implementation 3 months after launch Bin size based on averageinnovation in 2014 and α