Prescriptive Analytics and its nuances | CPG Industry
By: Naveen Ramprasad
Tags: Time Series Forecasting, Prescriptive Analytics, Data Science, Cpg Industry, Predictive Analytics

Bridging the gap between Predictive Ordering and estimating Inventory

Introduction

In recent times, Consumer Packaged Goods (CPG) domain has been one of the eye-catching markets for Data Scientists. With the retail industry turning to Data Science based solutions to estimate the sales demand of their products across the year, the CPG industry is now looking for patterns and trends to influence their business decisions around optimizing the inventory of the products at the stores. Prescriptive analytics provides one such solution and helps in prescribing optimum inventory quantity of products necessary in the stores to suffice the sales demand. Let us see how this can be achieved!

Problem Statement

The most common problem the CPG industry is currently facing is estimating the optimum quantity of the products to be transported to the stores from the warehouses to suffice the sales demand.

Why is this a million dollar problem? From the cost-cutting stand point, the transport from the warehouse to the stores is often pre-scheduled. The idea is to cover as many stores as possible in fewer trips from the warehouses to the stores. This would mean that carrying optimum inventory of products to suffice the sales demand between the scheduled deliveries is crucial for the CPG industry. As carrying surplus inventory would mean more haulbacks and scarce inventory would mean loss of customers due to out-of-stock.

Here are a few reasons that make this process challenging:

Is the sales of the product influenced by any upcoming events/holidays? If the inventory is lower than the expected demand, it could lead to frequent deliveries, which in turns reduces the profit margins. Also, not to forget the Out-of-stock situation which leads to direct losses If the inventory is higher than the expected demand, that means more backroom storage, which might lead to shortage of storage space for other products of higher demand

However, all of this boils down to two main variables:

1. Demand Forecasting — Estimating the future sales demand of products at the stores.

2. Inventory Estimation — Estimating the day-on-day inventory of the products at the stores

In this article, we will look at how prescriptive ordering can be achieved using the above two components, its benefits and nuances.

Note: We are looking at prescribing the quantity to be delivered for every product across every store from the warehouse.

Benefits of Prescriptive Ordering

Reduction of Out-of-stock scenarios — One of the major losses of revenue is due to out of stocks(OOS). Seasonal products would be the primary beneficiary of this. Overstocking — Although reducing OOS is important, overstocking excess inventory on the shelf or in the backroom might impact profitability by increasing storage costs and increasing the risk of product expiration. Product shrinkage leading to Backhaul — Estimating the inventory considering the loss due to employee theft, shoplifting, vendor fraud, or cashier errors will lead to cost-efficient merchandising.

Prescriptive ordering is designed to solve all the above problems. Let’s look at the solution design to understand how this can be achieved.

Solution Design

Prescriptive ordering can be achieved with a combination of Inventory Estimation modeling and Demand Forecast modeling. Given below is a simple yet powerful equation on prescribing the required quantity to be shipped from the warehouse to the stores. Although it is a simple equation, let us look at the nuances that make it challenging to execute it.

Fig 1.1 PRESCRIPTIVE ORDERING FOR A PRODUCT IN A STORE

Let us delve into each of the following components of the above equation:

Demand Forecast Inventory Estimation Safety stock

1. Demand Forecast component

The sales data of a product in a store is usually captured by either the retail store or through a third party contractor like IRI (Information Resources, Inc.) source. Using the historical sales data we can fit in various time series models like Moving Average(MA), Auto Regression(AR), ARIMA, SARIMA and so on to capture the trend and sales behavior in order to predict the future demand of the product at a store.

Nuances of Demand Forecast that make it challenging

Delay in the arrival of Sales data: The historical data might not be updated until the last day due to unexpected delays in the system. This could impact the time series modeling as any kind of moving average would not have the latest values and would incur a lag in the model. Holidays/Events: Capturing trends during holidays/ events would be a challenge as the sales pattern in the stores might be different during the holiday week than the usual. As the Moving Average models are designed to smoothen out the trend, this might be counter productive in this case.

2. Inventory Estimation component

The idea of estimating inventory is to have a rough estimate of the inventory at a given point of time for the products at the store. Why is it necessary? It is impractical to expect the store manager to count the inventory of each product at the store at a given point of time, and for prescribing the order to be placed, knowing the inventory on the order date is very important. Note that this is not an estimate looking forward but is the estimate for the current date. Inventory estimation can be achieved by considering a simple mathematic equation of Shipments(Deliveries from the warehouse to the store) — Sales at the stores on a day-to-day level. Below is a quick example on how Inventory Estimation works

Fig 1.2 Sample Shipment Data

Fig 1.3 Sample Sales Data

NOTE: The shipments for a product does not arrive every day to the store. Hence we have zero-filled to make the data continuous.

NOTE: The current day considered in the example is 17th Sep.