Transcript Managing the Long Tail (Oct 2016)

Managing the Long Tail
Ed Goetting – Director of Sales & Operations Planning
Oct 2016
APICS / VIA Meeting
About Solo Cup/Dart Container
Page 2
SKU Proliferation
• 20,000+ SKU’s total
― 11,000 legacy Solo
Legacy Solo SKU's - Avg Dmd Per Day - Cases
• Causes:
― Marketing differentiation
― Material substrates
― Environmental
― Special prints
― Private Label
― Custom packaging
― Merger activity
― Manufacturing complexity
Avg Dmd Per Day - Cases
PET, PP, PS, PLA
Height of nesting rings,
Rim diameter,
Lid application
Case Packs, Combo Packs
Bottom Embossed, Non-Embossed,
Ounce/Milliliter Capacity
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Dealing with the Long Tail at Solo
• S&OP formally introduced at Solo in 2010
• Emphasis on inventory reduction, fill rate improvement,
supply/demand matching, and forecast accuracy
• Focus tended to be on large volume items
• Cross-functional SKU Rat efforts
• Used ABC methodology in Manugistics – static # of days, based on
volume
―C&D targets were insufficient for many low-volume items
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New Challenges from the Dart Acquisition
• Dart acquired Solo in May 2012
• Case Fill Rate Target increased from 98.5%
to 99.6%
• “One Truck One Invoice” (OTOI) initiative
will increase distribution of Solo product from
9 distribution points to 21
― Legacy Solo products:
 MTF w/ Fcst: Increase from 11,000
to 20,000+ SKU’s
Existing ABC methodology not sufficient in the new environment
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Implementation of MEIO
• Multi-Echelon Inventory Optimization (MEIO)
― Multi-Echelon - Evaluates stocking policies and levels across Raw Materials, WIP, FG at
various stages of distribution.
 For the purposes of the OTOI project, we limited the scope to FG at buffer warehouses
and forward-deployed warehouses.
― Targets a specific service level outcome which can also be tailored to focus on particular
customers or products
― Statistical Safety Stock model considering demand and supply volatility
Previous Methodology
New Methodology
Advanced Planning System
Safety Stock set by volume
(ABC)
MEIO
Safety Stock set based on
demand/supply volatility and
replenishment parameters
Extract Master &
Transaction Data
Arithmetic
Statistical
Optimized for 99.6% fill rate
Import Results
Buffer DC’s used for overflow
Buffer DC’s used to reduce
total inventory requirements
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Example SKU’s
412TN/2111 at DC041
Fcst = 1.0 cs/day; dmd variability = 3.2; 49 orders in 2014; Statistical SS Target = 28 days
New SS
Target
Old SS
Target
GSP49/JR505 at DC052
Fcst = 1.2 cs/day; dmd variability = 9.5; 7 orders in 2014; Statistical SS Target = 155 days
New SS
Target
Old SS
Target
Both items are “C” items with a prior Manu SS target of 21 days
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Optimization Results / Next Steps
• Current network: Can achieve 99.6% with significantly less inventory
• New statistical targets recently implemented at all locations and categories
• New network: Increase from 9 to 21 distribution points will require more
inventory than optimized current network, but still less than existing inventory
• Running parallel environments for the current network (pre-OTOI) and the new
network (post-OTOI)
― Implementing current network targets in Manugistics for deployment and
short-term production scheduling
― Implementing new network targets for longer-term production planning and
capacity models
• Using combination of current and new network targets to help plan/execute
OTOI moves
• Currently transitioning to a new prod planning system – S099 will remain as
bolt-on
Page 8
Thank You
Ed Goetting
Dart Container Corporation
Director of Sales & Operations Planning
[email protected]
[email protected]
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