RETAIL SUPPLY CHAIN CONFERENCE 2015

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1 CASE STUDY: COMBINING VENDOR PERFORMANCE AND PREDICTIVE ANALYTICS TO DRIVE PROFIT IMPROVEMENTS Pep Boys, a $2 billion retailer, recently participated in a joint case study by Compliance Networks (a vendor performance and compliance solution provider) and Retalon (a predictive analytics solution provider) with the objective to improve overall profitability. The case study focuses on how incremental gains in vendor performance combined with inventory reduction through predicative analytics can lead to substantial profit improvements in a short period of time. The case study proved the following: the relationship between vendor performance, inventory stocking levels and retail profits are closely intertwined. Pep Boys was able to leverage information from typically two disparate departments to drive performance and increase profits. RETAIL SUPPLY CHAIN CONFERENCE 2015

2 Camille A. Fratanduono Vice President, Merchandising Operations The Pep Boys Manny, Moe & Jack Gregory S. Holder Chief Executive Officer & Co-Founder Compliance Networks Mark Krupnik President & Chief Executive Officer Retalon Inc. RETAIL SUPPLY CHAIN CONFERENCE 2015

3 Session Objectives Convey what we have learned on this journey Leave audience with 3-5 key takeaways/ideas Ensure every slide has a purpose Honest fact based direct answers to audience questions Openly discuss challenges related to the journey Focus on metrics typically impacted and specifically how they were achieved NOTE: Adapted from Dan Gilmore, Editor of Supply Chain Digest, Supply Chain Audience Bill of Rights

4 PepBoys Overview Leading automotive aftermarket retail and service chain 800+ locations located in 35 states and Puerto Rico $2.0+ Billion in Revenues 3 lines of business: Service, Retail and Commercial (B2B) Ecommerce enabled ship to store & ship to home Online service appointment capable

5 Compliance Networks Overview Supply chain software & services co. founded 2000 Based in Sugar Land, Texas (near Houston) Data centers in Little Rock, Arkansas and Dallas, TX Heritage in retail operations and retail IT Established to automate the vendor compliance process for retailers Data requirements: PO, WMS, ASN, DC audit & trouble data, hierarchical (merchants, departments, sites, etc) Sophisticated algorithms to identify supply chain failures, calculate charges and to vendors Unforeseen result: a supply chain data warehouse Key Take Away: Chargebacks as a percent-to-cost purchases should be in a range from 0.25% to 1.25% depending on the industry

6 PepBoys and Compliance Networks Contract December 15, 2003; live in May 2004 Scorecard Released June 2006 Primary Goals or Program Focus Phase 1 Reduce problem shipments in the DCs Decrease frequency of early & late shipping Complete docs, ASN arrival & Decrease back-order Primary Goals or Program Focus Phase 2 Improve shipped to arrive on time Increase poor vendor fill rates Convert inbound from prepaid to collect Reduce number of LTL carriers Visibility of flow

7 PepBoys Vendor Compliance Challenges Concern over impact to Vendor Relationships Merchants do not like chargebacks IT/IS resources were completely committed to support and development of other PepBoys projects or programs Key Take-Aways: A well executed vendor compliance program actually improves vendor relationships, removing ambiguity and interpretation. Merchants are more in-tune with the costs of poor vendor performance and the positive impact to the bottom line through improvements to vendor performance

8 PepBoys Goals, Impacts, Results Problem Category DC Problems Poor Fill Rate ASNs & Complete Docs Early/Late Shipping Ship to Arrive Impact to Retailer Increased labor costs Excessive DC cycle times Missed sales ops Cost of excess inventory Increased labor costs Excessive DC cycle times Missed sales opportunities Increased labor costs Excessive inventory Excessive markdowns Extended order cycle times Excessive inventory Excessive markdowns Year DC Problem Improve Percent to PO Percent % % -5.9% % -13.3% % -41.1% % 2.9% Year Total Unit Fill Improve Rate Percent % % 0.7% % 0.5% % -2.4% % 0.4% Year ASNs per Improve Receipt Percent % % -6.2% % 5.9% % -0.7% % 4.5%

9 Case Study: Unit Fill Rate - Apparel Two similarly sized apparel retailers Comparison Years Retailer A (Fill Rate) % 88.6% % 83.5% % 79.9% % 79.7% Retailer B (No Fill Rate) 1. Retailer A includes Fill-Rate as a performance metric and uses chargebacks to support the effort. 2. Retailer B does not create Fill-Rate chargebacks. 3. Retailers have similar footprints and vendors. 4. Data for both retailers is from the same years. Source: Compliance Networks, Data from

10 Case Study: Inventory (ASN) Accuracy Multiple retailers, primarily multi-sku cases Program Year Accuracy Rate Perfect Vendor Percent % 17.1% % 27.8% % 24.9% % 17.2% % 26.6% % 25.7% % 16.8% ASN Accuracy measures the ability for a vendor to accurately transmit via the ASN the contents of the carton. ASN Accuracy directly affects Inventory Accuracy Retailers include apparel, hard goods, and general merchandise. Study Conducted by Dr. Brian Gibson in Conjunction with RVCF. Source: Auburn University, Compliance Networks and the Retail Value Chain Federation

11 Case Study: On-time Shipping - Apparel Year After Implementation On-Time Shipments % Improvement % % % 51.4% % 0.0% % 14.8% Performance Improvements When the Retailer Made On-Time Shipping a Key Metric and started creating chargebacks for late shipping. Source: Compliance Networks

12 Prove the relationship by connecting data PO Lifecycle Data: From PO to Receiving Describes inbound shipping performance We integrated the data sets Inventory to POS Data: From Inventory to Sale Describes demand against inventory 1. Create PO 5. Invoice/Pay Receipts 2. Transmit PO 4. POS 3. Ship PO 4. Receive PO 1. Receipts to inventory 2. Allocate inventory 3. Distribute inventory for sales Integration point

13 Just In Time? In a perfect JIT world you don t need any safety stock The next shipment arrives at the moment your last item is sold The reality, though, is almost perfect

14 Yes, it s on the dock now!

15 Yes, we do drop shipments!

16 Demand Volatility

17 Real Supply Chain Environment In the real supply chain environment you need safety stock to protect yourself from - Variability in Demand - Forecast Error - Lead Time Variability - Fill Rate Variability Why?

18 Inventory OH by Period Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Lost Sales

19 Okay. Let s Just Keep More Inventory Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

20 Inventory Cost This is very expensive. How expensive? This is the question we plan to answer today, using

21 Why are there 3 of us here? 1. To Improve (CN) o Lead Time Variance & Fill Rate Variance 2. To Know (Retalon) o To calculate how much stock you really need, based on o Variability in Demand and Forecast Error o Lead Time Variance and Fill Rate Variance 3. To Act (PepBoys) o Communicate the improvement effect across the organization o Change Corporate Policies

22 What Can be Done? To reduce volatility improve vendor reliability, and compliancy Risks: Variability in Demand Forecast Error Lead Time Variability Fill Rate Variability To minimise the cost and keep only inventory you truly need to protect yourself from all risks

23 Example How to calculate safety stock to protect from risk of Lead Time Variability?

24 Lead Time Variability (LTV) Average LT is 120 days Frequency (how many times occurred) LTV

25 In 68% of cases the LTV was 14 ± 3 LTV

26 Desired Service Level (DSL) 95% DSL means that you need to have extra inventory to be protected from out-of-stocks in 95% of cases. The diagram below shows that you need about 70% extra inventory. Service Level (a chance to be in-stock) 98% 97% 95% 30% 40% 50% 60% 100% 150% Additional Inventory

27 Before LT variability Improvement Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

28 How Much Stock do I need TODAY? Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

29 and After Improved Vendor Compliancy

30 Maximum Benefit Keep only Inventory that Adds Value Improve Vendor Compliancy

31 Calculated Inventory Cost Reduction Product Type Product Count % Contribution to business Revenue Inventory Reduction A products 5% of products First 60% 12% B products 15% of products Next 20% 16.5% C products 20% of products Next 15% 21.3% D products 20% of products Next 4% 28.8% E products 40% of products Next 1% 34.1%

32 Calculated Inventory Cost Reduction Product Type Inventory Reduction A products $ 915,600 B products $ 3,776,850 C products $ 6,500,760 D products $ 8,789,760 E products $ 20,814,640

33 Total $40,797,000 in Cost Savings

34 So, how to Make it Really Work? 1. Improve 2. Know 3. Act - YOU You have to Measure, Communicate, Change Step 1 Let s measure

35 Lead Time Variability Benefit Calculator

36 Lead Time Variability Benefit Calculator

37 Lead Time Variability Benefit Calculator

38 Lead Time Variability Benefit Calculator

39 Lead Time Variability Benefit Calculator

40 Lead Time Variability Benefit Calculator

41 Questions? Camille A. Fratanduono Gregory S. Holder Mark Krupnik Vendor Lead Time Variability Benefit Calculator RETAIL SUPPLY CHAIN CONFERENCE 2015

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