You believe a weekly replenishment process that includes reviewing plan, inventory, and sales to make purchase order decisions is profitable. Some people think weekly replenishment increases turns for the business. Your weekly review and reorder for inventory replenishment also suggests razor sharp exception management processes are in place, ready to act. Weekly review and reorder means there is little chance for out-of-stocks or extraneous freight costs to occur. Historically, these ideas promote the belief that human review and intervention on a weekly basis is the correct and profitable course of action.
The Inventory Replenishment Methodology a supply chain solution uses to flag when an inventory replenishment order should be placed is critical to consider when reviewing supply chain software. Review any of the mainstay inventory replenishment systems, and you see what becomes obvious – there are only two methodologies possible. The system signals an inventory replenishment order is needed based on a combination of date, plan, and demand (top down) or based on a combination of service goal, demand, and available inventory (bottom up). Everything after that flag is a mix of tools and features that differ in each software package. While these two inventory replenishment methodologies have similar features and names, the critical things for you to know are:
- How to identify which methodology is used by your software (or software you are reviewing).
- The costs and goals of each methodology and how the differences impact each line of business in your company.
Forecasting replenishment correctly and following standardized inventory replenishment processes continues to deliver significant returns to retailers, wholesalers, and manufacturers. For the retailer/ wholesaler/ manufacturer ready to move away from legacy technologies, there are huge opportunities that cost 50-90% less than legacy systems. A forecast accuracy in the 90% range we know delivers a significant shareholder value increase of 15% or more.
Several documented events support these claims (click a link): a retailer achieved a 25% inventory reduction and a 3% same store sales increase in 90 days, the sales and inventory trend continued going forward (press release), Dr. Mentzer’s 3 page story concerning a collection of businesses that delivered an average 15% shareholder value increase via forecast accuracy improvements which directly impacted forecasting replenishment, and The Home Depot Chairman and CEO, Frank Blake, specifically stated in the 2011Q4 earnings briefing that supply chain investments continued to provide significant benefits including increased turns and same store sales.
Inventory Investment Buying (forward buying) is a strategic part of the buying role that many companies don’t realize today. The truth: Investment Buying that is based on accurate demand forecasting and effective inventory optimization processes delivers significantly higher gross margins, better GMROI, and balanced inventory levels.
Buying and maintaining inventory is often viewed as a cost center and companies struggle with these 4 basic questions:
- When do I buy?
- What quantity should I buy?
- When I buy, how can I balance inventory levels?
- A vendor has offered a discount, how much more, if any, should I buy?
Promotions are Vital for Greater Revenue and Reduced Inventory
In-Store trade promotions are the lifeblood of the supermarket industry and discount retailer. Trade promotions include products featured in ads and in-store circulars, products displayed on end of aisle caps or away from their normal shelf location, and products with temporary price reductions. They create Trial and Repeat Purchases AND create all important Impulse Sales. You know impulse sales; they are all of those items you purchased that were not on your shopping list!
According to a recent study from The Nielsen Company, 42.8% of grocery purchases are sold on promotion, up from 40.8% a year ago. Drug stores, too, sell a significant portion of products on promotion, with 40.4% of sales linked to displays and/or features.
What is Lead Time? Why Is it Important?
Lead Time is the length of days between when an order is placed and the date the goods are available for use. The largest impact to lead time accuracy is found by comparing expected receipt date to actual receipt date for each purchase order. In simple terms, the variance is calculated as the absolute value of the difference [expected or requested receipt date - actual receipt date] for each line on the purchase order. These variances in days across multiple purchase orders establish the need for lead time accuracy testing and lead time forecasting.
What is the Impact When Supplier Lead Time is Not Accurate?
Suppliers provide an estimate of lead time, but these numbers are not always accurate. The differences between your expected receipt date and actual receipt date can become expensive from the resulting unplanned over stocks, out of stocks, and deflated consumer opinions. Lead time tracking and lead time forecasting are mission critical to the success of your supply chain. Lead Time Forecasting, like Demand Forecasting, should use a set of math algorithms to calculate the correct lead time days to use in planning purchase orders. Also, like Demand Forecasting, the Lead Time Forecast should move up and down according to changes in market, business influences and seasonality of product.
Sales Forecasting is the wrong tool for inventory replenishment and inventory planning. Sales Forecasting, by its very name itself, is a measure of total sales. In our last article, we discussed that the key difference between sales forecasting and demand forecasting is whether (or not) sales data is broken out into type of sale, analyzed, and the results input into the forecasting algorithms. Sales type might include any or all of the following: regular, lost, promo, event, and close out sales. Without knowledge that sales went up or down due to market factors like out of stock and promotions, a sales forecasting system will forecast based only on the total sales. This may not be the intended goal for inventory replenishment or inventory management.
Key Limitations to Sales Forecasting
Sales Forecasting, by its very nature, doesn’t know why sales rise or fall and cannot connect events to sales behavior. For example, when sales were down 20% four weeks ago, you probably knew this was due to constrained supply which created out of stock issues. The sales forecasting system will react to the 20% drop by lowering the forecast. The resulting inventory replenishment orders from the new forecast will be low, creating a repeat scenario of lost sales again next month.
Demand Forecasting and Sales Forecasting are different, and the results of each can have a dramatic impact on your profitability. Demand Forecasting and Sales Forecasting should be calculated with some similar and some different data points. While closely related, the two resulting forecast numbers will not be the same in most business situations. The forecast results will impact the inventory replenishment by impacting available inventory, expected inventory orders, and sales. An inventory replenishment system that is based on a demand forecast (demand driven) can reduce the risk of lost sales while improving service. This in turn delivers higher sales by connecting inventory levels with demand forecast.
What is Sales Forecasting
Sales Forecasting is the easier of the two choices: you load your sales history into the sales forecast engine and the system delivers a sales forecast. Sales Forecasting is critical for the retail business to create financial plans with the banks, plan sales growth, and plan resource strategies. Sales Forecasting systems have a ‘vanilla’ approach that is clean and simple, and it works without issues for the most basic of products. Legacy systems often will pair the sales forecasting with their demand planning tools to determine inventory replenishment for the business.
Reviewing Seasonal Indexes is critical for an accurate demand forecast. Seasonal Indexes, also called seasonal multipliers, are used to adjust the demand forecast by multiplying the product base forecast by a multiplier. The effect will raise or lower the demand forecast for the time period, often a week or month. The results are often used to help calculate the inventory needed to support sales. Holidays like Easter, seasons like springtime, and events like the Super Bowl that repeat based on some factor of time are frequently better serviced with a seasonal index applied across the year.
Problems with Seasonality
The problem with seasonality is that it can change each year, and your current fiscal year may not map back to your seasonal index. Sales from the last seasonal event may impact your base demand forecast to create inaccuracy. Easter is in a different month and fiscal week this year. How did you account for the differences when purchasing Easter inventory for this year? Next year, Easter is again in a different month and will be several weeks different from this year. Thanksgiving is a holiday that based on the time of year can add or subtract a whole weekend of December holiday shopping. There are two issues that need to be reviewed and adjusted: the current year fiscal week seasonal index values and the base demand forecast.
Slow and Intermittent products make up 35-40% of most retailer assortments. These products often are critical to the assortment because a top 20% product is often paired with a selection from an assortment of slow demand product choices. This large group of products in your assortment can ruin your turn goals and your GMROI when managed incorrectly. There are two key pieces that must work together for a retailer to win with slow movers: the demand forecast and how the supply chain software uses the demand forecast to manage the inventory. The results of poor buying are low turns and loss of capital for other product.
Slow and Intermittent Product Demand Forecasting Myths
“How do you forecast slow and intermittent demand products?” The same question was posed to me in three different meetings at NRF this year. Many software companies differentiate their demand forecast capability from their competition by highlighting their skill in forecasting slow and intermittent product demand; at the same time, they strike fear into the hearts of retailers by highlighting retail losses delivered due to poor demand forecasting of slow moving products. The key to this discussion is to not get trapped into a no win conclusion. More than a great Demand Forecast is needed to attain winning results with these product groups. Like the story of the ‘Tortoise and the Hare’, Slow demand products are part of any assortment and can be big winners.
