Intermodal Supply Chain Optimization at a Large Retailer Part 2: Experimentation and Results Scott J. Mason, Ph.D. Fluor Endowed Chair in Supply Chain Optimization and Logistics Professor of Industrial Engineering

k distribution centers The Logistics Network Mode: LTL Modes: LTL or TL Modes: Intermodal and TL i suppliers j facilities k distribution centers Suppliers Consolidation Points Distribution Centers Scott J. Mason, mason@clemson.edu

Client-Desired Experiments CP closings as a function of DC demand fill rate Closed due to low volume considerations CP expansions as a function of DC demand fill rate Expanded due to high volume issues CP openings as a function of DC demand fill rate Opened due to high volume issues Scott J. Mason, mason@clemson.edu

Phase 1: Product Mix and Data File Generation Example data for actual DC demand for each supplier was provided as a suitable product mix During first 16 weeks of the year, an average of 81M pounds of total demand was experienced across all 19 DCs April product mix used to scale up demand requirements to appropriate 6-month demand for first half of year of 2.1B pounds Cube restrictions were not found to be binding Scott J. Mason, mason@clemson.edu

CP Closings as a Function of DC Demand Fill Rate Using base case 6-month data file, CPs assessed for closing in terms of reverse Pound Rank CP 6910 (Pound Rank of #19) CP 6911 (#18) CP 6992 (#17) CP 6917 (#16) CP 6940 (#15) Baseline model with 19 CPs 18 CPs (close CP 6910) 17 CPs (close CP 6910 and CP 6911) And so on Scott J. Mason, mason@clemson.edu

CP Closings as a Function of DC Demand Fill Rate In conjunction with CP closings, required DC demand fill rate is varied Current level of 100% down to 95% in 1% increments The “as is” condition is 19 CPs and 100% demand fill rate for every DC Six different CP configurations evaluated at six different DC demand fill rates 6 x 6 = 36 model runs with baseline 6-month data file Scott J. Mason, mason@clemson.edu

CP Closings as a Function of DC Demand Fill Rate Performance metric is ratio of the cost of CP and fill rate combination to the “as is” configuration 1.00 is the baseline Scott J. Mason, mason@clemson.edu

CP expansion options pre-determined by client CP Expansions CP expansion options pre-determined by client Examine CPs with 100% utilization that can be expanded For example, CP 6910 cannot be expanded Scott J. Mason, mason@clemson.edu

CP Expansions Expand each CP to its maximum capacity Also consider expanding each CP by one-half of its available dock door capacity CP 6935 currently has 50 dock doors can be expanded to 100 Analyze this CP being expanded to both 75 doors and 100 doors Both CPs expanded individually, as well as simultaneously CP expansions also examined in concert with potential CP closings Determine trade-offs for client to drive towards reduced CP network transportation costs Scott J. Mason, mason@clemson.edu

Expanding Each CP Individually Scott J. Mason, mason@clemson.edu

Expanding CP 6915 and CP 6935 Simultaneously Scott J. Mason, mason@clemson.edu

CP Openings Examination experimental results in concert with assessment of current CP locations reveals California-based third-party consolidators CAC and CAI are Located in isolation Handle the majority of west coast demand Fully utilized at 100% CP 6927 and CP 6938 are primary CPs responsible for serving northeastern U.S. (with some demand supported by third party consolidator JNJ) Both CPs are utilized 100% in base case optimization results Both CPs not expandable past current number of 20 dock doors Scott J. Mason, mason@clemson.edu

CP Openings: California Study CAI operating at maximum capacity of 124 dock doors CAC operates 50 doors, with expansion to 80 doors a possibility Expand CAC to 80 dock doors with varying DC demand rates Evaluate attractiveness of constructing new CP with 100 dock doors at same basic CAC site Scott J. Mason, mason@clemson.edu

CP Openings: California Study Scott J. Mason, mason@clemson.edu

CP Openings: Northeast US Study Investigate the effect of consolidating CP 6927 and CP 6938 Construct new CP facility at each current location independently Client stated that all new CPs will have a minimum of 50 doors, with a maximum of 100 Investigate new CP opening with 50, 75, or 100 dock doors Scott J. Mason, mason@clemson.edu

CP Openings: Northeast US Study Scott J. Mason, mason@clemson.edu

CP Openings: Northeast US Study Results More cost effective to locate new CP in New York than in Pennsylvania Should be built with 50 doors No additional transportation cost decrease results for >50 doors due to insufficient demand volume at the current time Scott J. Mason, mason@clemson.edu

5 Year Strategic Analysis Results suggest client’s CP network is 82.5% utilized based on first half of year demand data Retailer stated that it expects an average annual demand growth rate of 10%-12% Investigate CP network changes that must occur (and their respective timing) in order to accommodate demand growth over five years using sequential optimization Scott J. Mason, mason@clemson.edu

5 Year Strategic Analysis Results Resulting transportation costs expressed relative to current CP network base case conditions Scott J. Mason, mason@clemson.edu

5 Year Strategic Analysis Summary Eight CP locations need to be expanded to maximum dock door capacity over next four years to accommodate projected DC demand growth Expansions suggested for second half of year 5 leaves CP network 98.8% utilized Additional expansion warranted for subsequent periods Second half of year 5 optimization results suggest only two CPs not 100% utilized Scott J. Mason, mason@clemson.edu

Network Equilibrium—What is the Desired Operating State? Assume each CP has infinite capacity Modeled by assuming very large number of doors One can think of the minimum achievable transportation cost solution as the CP network’s equilibrium or desired operating state A number of model inputs are responsible for some portions of this equilibrium solution Transportation lane restrictions Fixed and variable transportation costs Investigated equilibrium solutions for Period 1 and Period 10 of 5-year strategic analysis Highlighted expansion and contraction opportunities Scott J. Mason, mason@clemson.edu