Founded in 2014, this is a leading artisan food manufacturer that specializes in Mexican American, grain-free products. The products range from grain free tortillas and chips to spices, sauces and dairy free queso. Their mission is to create products that foster wellness, and provide options to people with dietary conditions.
Many start up organizations that have double-digit, year-over-year growth usually end up with processes and systems that are lagging, as growth exceeds their current processes and capabilities. Without many systems in place, a majority of the supply chain functions were conducted in Excel.
To add an additional layer of complexity, the company is constantly innovating and launching new products. Demand planning became cumbersome and difficult to estimate customer needs. In the CPG specialty foods industry, service level requirements are coupled with shelf-life guarantees. To meet stringent customer requirements, it is critical to have optimal inventory levels and a view of inventory position across a time horizon. Through Accelytics 6 step process, our implementation team was able to isolate issues, optimize processes, and configure the following best in class Anaplan solution.
A complete end to end supply chain planning system was created with Anaplan. Prior to the engagement, there was a lack of usage of statistical demand planning models. Accelytics was able to create a seasonality index by product, and develop structure around the demand planning process. One of the key wins was establishing a demand planning system that allows the company to scale for future growth. A model that needs specific mentioning is the supply planning model. The logic and design of the supply plan encompassed complete management of demand, inventory, procurement, and user customization. More than ever before, the company is set up to successes in the future.
The Supply Chain Director at the company stated, “The supply plan Accelytics created is the most robust supply plan I have ever worked with.”
After thorough requirements gathering and analysis, a Data Hub was used to address data issues and migrate manual planning in Excel to a cloud-based platform. The Data Hub provided one source of truth to service all ancillary supply chain models. The next step in the implementation was to create an adaptable Demand Planning solution.
The Demand Planning solution needed to address four major tasks: new product introduction, end of life, statistical forecasting, and Sales/Marketing planning. The company launched 20 new products in the last 6 months, so it was essential to design a new product introduction solution that would provide suggested forecasts. The suggestions were derived by using the estimated demand for new product and spreading the demand by similar product seasonality or overall product seasonality. Many of the new product launches were replacing old products, so the end-of-life design used 3 methods to forecast final demand values. The user could input a remaining duration of product life and select scrap, straight line, and forecast methods to apply to the declining demand. Statistical methods were applied to products that had 12 or more months of historic demand data, and the best fit method was selected. The demand solution contained 26 different statistical methods, ranging from Additive Decomposition to Logarithmic Regression. Sales and Marketing needed the ability to apply growth and promotional uplifts to the final forecast. The solution combined customer store count and product velocities to adjust for growth and declines in demand. The multiple inputs could be reviewed through forecast iterations allowing the demand planning team to arrive at a final forecast. The next step for the solution design was to Optimize Inventory.
The requirements for the inventory solution were to calculate safety stock, reorder point and spoilage based on customer service levels, shelf life, lead time, and demand variations. Customer service levels are the inventory percentage requirement, so the company has to properly service each customer distribution center.
Utilizing the uncertainty of the demand safety stock formula, which utilizes a Z Score %, translates to a standard deviation coverage of demand variation. Customer shelf-life guarantees are a product longevity requirement-based days remaining until expired. An inventory aging table was created at the lot level to ensure the useful life of the product in days remaining was greater than the minimum days required. Reorder point calculation combined forecasted demand, lead time, and safety stock to suggest a weekly purchase order (PO) quantity to submit to the co-manufacture.
Lastly, Accelytics was tasked with creating a supply plan to ensure stable inventory levels. The Demand Plan is pushed to the Supply Planning model to show forecasted demand for future periods. Each product had an assigned forecast look forward period in weeks, in which demand was aggregated to place POs ahead of time. The aggregated demand is then netted against inventory, open POs, and spoilage to arrive at net requirements. Additional functionality that was designed within the model was PO Scenario planning. A line item was created in the supply plan to apply PO values in any week. All calculations would automatically update to simulate inventory position impacts as Open POs are received in future weeks. Additional customization was created in the form of “Overrides.” The overrides in the supply plan allows the user to override the suggested PO value and apply a custom value.