A Case Study on Improving Forecast Accuracy through Collaboration at JR286 Inc.

Transcription

1 A Case Study on Improving Forecast Accuracy through Collaboration at JR286 Inc. By Rishabh Sinha ENI-JR286 Inc. Michael Cullen ENI-JR286 Inc. Adil Mujeeb Rapidflow Apps Inc.

2 Abstract This white paper is based on case study on how JR286 Inc. improved its forecast accuracy and demand management process through internal and external collaboration using Demantra Demand Management Module. It includes challenges encountered, lessons learned, benefits of moving forecast from spreadsheets into centralized database & its impact on the various business functions of the organization. The objective of writing this whitepaper is to share the lessons learned during the successful Demantra Demand Management Implementation, Exchange ideas about demand management and collaboration with the users and share the benefits that JR286 Inc. experienced after the implementation. Background Specializing in the sporting goods industry since 1989, ENI-JR286 is a leader in the designing, developing, sourcing and distribution of branded and licensed sports accessories. ENI-JR286 is a successful entrepreneurial business with the vision to become the premiere global sports accessories company. ENI- JR286 is the best in class branded Accessories Company across all functions - including customer service, supply chain management, logistics and information technology. Headquartered in Redondo Beach, California, USA with entities in Canada, China and Europe, we are well positioned to effectively and efficiently service our global customer base. ENI-JR286 is the strongest licensed accessories company in the world, with distribution in over 170 countries, 34 Distributers, 45 sales offices and 40 distribution centers.

3 ENI-JR286 has been using Oracle EBS- Financials, Order Management, Purchasing, WMS, i-supplier and other EBS modules since 2007 and is live on version Recently, JR286 has implemented OBIEE, Demantra Demand Management & Agile Product Life Cycle Management System System Landscape

4 Pre-Demantra Sales Forecasting Prior to the Demantra Demand Management Implementation, the demand management process was manual with each sales representative maintaining its own spreadsheet. At the beginning of every quarter, the demand planner at the corporate office would receive these spreadsheets from over 100+ field sales representative and it would spend weeks consolidating these spreadsheets manually in order to arrive at demand numbers by regions, sales representatives and demand classes. This was a manually intensive effort with prone to errors. The demand planner would then run the reports to compare the demand numbers with the actual sales in the previous period. The sales team did not have a forecasting tool with capability to slice and dice the data across different dimensions and ability to forecast better based on History, Open Orders & Point of Sale Data. Sales team could not collaborate on a real-time basis amongst themselves and any change in demand would take several days or weeks before they are reflected into overall sales plan. The demand planner had to manually combine forecasts and demand to get a clear picture of future revenue, and could not create consistent and efficient buys. This affected inventory levels and problems planning the business from a financial perspective. In Summary: 1. A lot of time was spent by the Sales team in generating forecasts and updating their spreadsheets based on the weekly updated data (avg hr per week depending on the number of accounts) 2. The same set of data reported in different format leading to multiple views of data with no validation and prone to error/rework. 3. Since enormous time was required in collecting information and generating reports, it delayed the forecasting process. This constrained the senior sales management s ability to make quick decisions. Having timely and accurate information readily available for management is critical when running a global operation. 4. One consolidated view of various entities (US, Canada & Other international) was not possible due to data being managed and stored differently. It took days to get one consolidated forecast view and to generate forecast for the different brands and business entities of the company. 5. Poor forecast accuracy would often lead to wrong buys resulting into excess inventory on wrong products making it difficult for JR286 s inventory planners to keep ideal inventory turns and inventory levels. 6. A Lot of money was also spent in expediting products which were not available due to inconsistent and delayed forecast. 7. Bought product too many times a year and were unable to maximize sales and minimize costs, by not hitting MOQ s, not filling full cartons for shipping and shipping multiple times per year when unnecessary.

5 Implementation Goals ENI-JR286 engaged Oracle Gold-Partner Rapidflow Apps to implement Demantra Demand Management module. It decided to utilize standard out-of-box Demantra data model with few modifications to the seeded data model. ENI-JR286 embarked upon the Demantra initiative with the following goals in mind: Improve sales forecast accuracy through better collaboration amongst the various sales representatives and the corporate demand planning team Eliminate manual steps in the demand management process to reduce the overall Forecasting cycle time which allows sales reps to devote more time in the field selling products Ensure unified view of US, International and Canada entities. Factor in the growth in the business amid growing global operation of the company Centralize reporting database to avoid different variations for the same report Build trust in the system to ensure maximum utilization of the system Keep the implementation cost low without compromising on the quality of delivery Maintain integrity of data Eliminate the manual reports generated for the sales representative Using as a sell-in tool, not only a forecasting tool

6 Implementation Challenges Product Hierarchy Even though JR286 wanted to utilize the seeded data model, there were many more levels the sales team needed to aggregate the data than available in the out-of-box product hierarchy. The challenge was to modify the product hierarchy in a manner that still allows JR286 to leverage the out-of-box integration of Demantra with Oracle EBS Custom Product Levels and Hierarchy Item Style Activity Item Style Concat Category Item Style Product Line Item Style Sub Category Item Color Item Size Item Item Status Item Gender Item UPC Location Hierarchy JR286 did not require sales data to be stored at the customer site level and storing the data at the Customer level would suffice but taking out the lowest level site would have rendered the out-of-box integration useless. In order to overcome this issue, it was decided to roll-up the multiple site sales history data onto one site per customer by using oracle supplied data collection hooks.

7 Customer Site (One Site) Customer Sales Representative Account Executive Sales Organization (Lowest Level) Pricing JR286 has a complex pricing model with multiple levels of qualifiers and modifiers and it would forecast future revenue based on the actual prices of the products which could be different for different customers. Some of the complexities included: a. having generic price list per region like US, Canada, APAC, EMEA etc b. having key customers modifiers with hard coded prices for certain products c. Special modifiers on price changes due after certain time d. Prices to be calculated in Net, Wholesale & Retail Prices Therefore, the challenge was to collect the actual prices of the products per customer after applying all the discounts, qualifiers and modifiers. JR286 had to develop a custom pricing API in order to collect the actual sales price product per customer. Forecasting Seasons Being into sports accessories business requires JR286 to forecast the demand of their products by seasons. It has categories into four forecasting seasons: 1. Spring 2. Summer 3. Fall 4. Holiday Every season new products are offered to different customers that mean each season hundreds of thousands of new combinations would be created leading to massive data growth. In order to overcome this issue, in each season, it identifies the product list offered to each customer and only these itemcustomer combinations are loaded into Demantra. This helps to have only the required level of combinations created into Demantra instead of loading for all items customer combinations. This approach would help JR286 limit the data growth manageable thereby increasing performance and speed. Different Forecasting Levels Forecasting is done at the following levels:

8 1. Sales Forecast- Forecast entered by sales representative at the customer item level in reference to history, open orders and Point of Sales (POS) Data. 2. Management Forecast Forecast entered by Sales Director at the category level to meet the company revenue goals 3. Updated Management Forecast Consensus forecast after discussion with Sales Representative and Sales Director 4. Operations Forecast This forecast is used by operations to secure capacity, materials and eventually produce orders to the Factories 5. Product Forecast Entered through Agile PLM system

9 Demantra Benefits Though ENI-JR286 is still far away from realizing the full potential of the Demantra Demand Management capabilities, this initiative has already started benefitting the business. Some of the benefits that ENI- JR286 has experienced post Demand Management implementation included: Real Time Visibility to Sales Rep Demantra has become the portal for Sales Reps to get all information about their accounts: 1. Open Orders 2. Invoices 3. Last Year History 4. Point of Sale Data 5. Item Offering 6. Current Item Status 7. No. of Styles offered in a Product Line 8. No. of Colors offered & its Sizes Ability to Slice & Dice Data Sales Rep now have the ability to slice & Dice data in whatever fashion they want within the elaborate parameters provided. Single Source of Truth / No Data Discrepancy Now, the data coming is fully integrated with Oracle EBS & data is consistent. There is no manipulation of data and there are no errors. Ability to Aggregate / Disaggregate data Forecasting can now be done at any level & the roll up or down will happen proportionally based on the history. This has been a major achievement as it has reduced the forecasting time a lot. Dashboards for Sales Reps Demantra has a unique capability to show graphical representation of the selected series. This helps the sales rep on key series while forecasting Real Time Collaboration Forecast entered by Sales Rep is immediately available to the Sales Director to run his consolidated view on the forecast and make quick decisions during the sales meetings. He can identify key categories & then drilling into product line and styles he can make quick decisions along with the sales reps. Increased Forecast Accuracy Demantra stores all history and POS data and at the same time gives the ability to compare Forecast vs. Actuals, which helps to track and manage forecast accuracy. Reduction in Inventory Level Better forecast accuracy is helping in improve inventory planning Lead time Reduction Increased accuracy helps to secure capacity and buy long lead time material prior to needing it, with less risk. Improved Turns Ability to plan product flow in and out of DC s better.

10 Lessons Learned Like any new initiative in every organization is met with resistance and often viewed with skepticism especially with Sales Rep, ENI-JR286 also has had its share of ups and down with this initiative. The initial Excitement over Demantra initiative during the initial phase of the project resulted into Frustration mid-way during project into a sense of Cautious Optimism close to go-live and again back to Excitement in post go-live phase. ENI-JR286 team has learned some valuable lessons during the implementation. Some of them are: 1. Keep it simple and try to make as much use of the out-of box repository as possible. 2. Do not try to replicate spreadsheet forecasting system into Demantra. Demantra helps to rethink the forecasting process and how can it be improved to achieve critical business success. 3. Do not wait to start rolling out the system to the users until Everything is perfect. Engage the end users early; the more they work with the system, the better they get with the tool. 4. IT should be mindful of the performance and the future growth in the data and accordingly, make provisions for the hardware. 5. Make sure your hierarchies, both product and customer, are clean and accurate before starting the process. 6. Engage multiple divisions into the process as it should be a companywide initiative. 7. Training should start early and often. Critical Success Factors Getting the right partner It is very important to get an implementation partner who has been there and done that. End User Involvement ENI-JR286 ensured maximum user involvement throughout the project to make it a success along with the Rapidflow team. The user involvement during the data validation phase was critical. Utilize seeded data model ENI-JR286 tried to use the seeded Demantra data model as much as possible and never attempted to create a custom data model from the scratch. This allowed JR286 to leverage the standard integration with Oracle EBS. Cost Control ENI-JR286 kept the cost of project under control by utilizing mix of onsite and offsite resources. It utilized technology (web-conference tools) to minimize the onsite presence of the consulting team and got the majority of the work done offsite. Top Management Support Management supported the project team throughout the implementation phase and helped the team overcome the change management.

11 Conclusion JR286 is growing rapidly with aggressive growth plans for the future. In order to meet the strategic revenue targets of the company, the sales team needs to be more efficient with the demand management process and more accurate with the sales projections. As such, it needs a forecasting tool that is scalable with the increasingly global operation and at the same time allows sales team to collaborate amongst themselves to make better sales projection. This phase of Demantra Demand Management implementation has ensured just that and it will play a crucial role in the JR286 s future success. About the Authors: Rishabh Sinha, IT Director, ENI-JR286 Inc. Rishabh Sinha has over 12 years of industry experience in Oracle Applications and Information Technology consulting. Rishabh has joined JR286 in 2010 and have brought significant changes at JR in terms of IT improvements & stabilization. Prior to JR286 Inc, he has consulted with corporations in different countries and has led various successful global implementations. Rishabh holds a Bachelor of Technology and Masters of Business Administration from Indian Institute Of Technology. Michael Cullen, Demand Planning Manager, ENI-JR286 Inc. Formerly the sales manager for Central and South America, Michael has been tasked with the role of Demand Planning Manager to minimize the inventory levels and increase forecasting accuracy. He has a Bachelor degree in Business Administration from the University of San Diego. Adil Mujeeb, Project Manager, Rapidflow Apps Inc. Adil Mujeeb has over 15 years of industry experience in Supply Chain Planning and Information Technology consulting. Prior to Rapidflow Apps Inc., he has worked with Oracle Corporation and has been part of many complex and challenging assignments involving Oracle Value Chain Planning suite of products, Business Intelligence and Product Lifecycle Management.