February 2022

The bankruptcy of Australian electronics retailer, Dick Smith Group, continues to be instructive. According to press reports, the company’s administrators highlighted poor inventory planning as one of the key reasons for the group’s collapse. “Inventory decisions… were not consistent with consumer demand, and DSG was ultimately left with a considerable level of obsolete and inactive stock, requiring a major write down.”

Ignorance of successful inventory planning tactics is a result of poor business management by both large and small companies.

What does it take to correctly plan the right levels of inventory for your business?

Inventory algorithms

Formulas or algorithms for calculating the optimum level of inventory have been available for many decades and are still the subject of research by mathematicians and operations research scientists striving to improve predictions for all sorts of business activities.

Unless you aspire to be a modern day Pythagoras, these planning algorithms have been incorporated into all manner of software, at a range of costs. Successful supply chain management requires an understanding of the key drivers behind the calculation formulas.

These drivers form the basis of inventory planning algorithms:

Key inventory drivers

Demand

This is the most obvious. Quite simply, the more that customers want to buy from you, the more stock you’ll need to have on hand.

Demand Variance

This statistical term is a measure of “spread” of demand around a mean. The more irregular or sporadic the demand is likely to be, the more stock that is required to be sure of delivering good service. Think of an ice cream seller stocking to cover hot and cold days as an example of sporadic demand.

Lead Time

If it takes two weeks to get a fresh supply of widgets from your supplier. If you sell ten items per week, you need twenty widgets just to cover the lead time for the next shipment.

Lead Time Variance

If you have a supplier that averages two weeks to get stock to you, but can take one or four weeks, then you have to cover for when things arrive later than usual.

Economic Order Quantity

This formula calculates the optimum quantity of stock to purchase each time from specific vendors. This formula takes into account several factors – what it costs for you to place a single order to a supplier, quantity discounts, costs for holding inventory, and costs of stock-outs.

Service Level

Are you wanting to hold enough stock to make sure that customers are satisfied 99% of the time? Or do you want to have sufficient quantities to satisfy them 95% of the time?.

In summary, inventory planning management algorithms consider all of these criteria, and more, to assist you in calculating how much inventory to hold.

The inventory planning process

An inventory planning process is the discipline for determining the optimum amount of inventory for delivering a prescribed level of customer service. The steps required for building the process are:

Segment your customers and your products according to the service level that you wish to deliver. This is done by using one of the most simple but effective of all management tools, the Pareto Distribution, or 80/20 rule. Eighty percent of sales typically come from 20% of your product range and 20% of your customers. Prepare a curve showing cumulative revenue against products or customers and see for yourself. It is these customers and these products that should receive your highest service level of service.

Give lower levels of service to less profitable products. Optimize your inventory investment in this way. Invest in smart people who have aptitude with figures. Invest in software that can manage and calculate required stock levels for your business. Even a mathematical genius could not carry out these calculations for a large business without assistance. One of the great advantages of great inventory and order management software is that it captures point of sale information and transmits it back to suppliers, reducing lead times, and lead time variance.

Make sure your software solution provides advanced analysis and reporting capabilities to help you better understand actual customer demand drivers. It should allow you to monitor your inventory performance by taking into consideration inventory turnover, stock-outs, stock in excess, and stock in obsolescence.

If you found this article helpful, and would like to learn more about Cin7 inventory and order management software, click here to book a demo.

Inventory Planning – Inventory planning is the process of determining the optimal quantity and timing of inventory for the purpose of aligning it with sales and production capacity.

As the owner of a growing retail business, an accurate understanding of customer sentiment will help you stock just enough of your hottest products and allocate your cash flow for optimal profitability.

Workforce management firm Altametrix defines inventory planning as the process of estimating future customer demand based on available historical sales data.

Many experts would have agreed to that definition in 2019, but Covid-19 has proven that even the best plans based solely on historical sales data can be disrupted by unforeseen circumstances. On top of possible global unrest, product sellers also need to factor in supply chain delays and fickle consumer tastes. Making a mistake in gauging the future popularity of a product can lead to obsolete stock if you overbuy, and stockouts if you buy too little.

Not an easy task..

But Cin7 is here to help you shine at forecasting demand. We’ll provide 5 inventory planning best practices here so you can increase your chances of success – increasing profit margins and reducing costs associated with stagnant inventory.

Inventory planning is a process of predicting what your customers will buy, how much they’ll buy and when they’ll buy it.

Whether production planning, inventory management or entering a new market, demand forecasting will help you make better decisions for managing and growing your business.

Here are inventory planning best practices:

#1 Create a repeatable monthly process

Accurate demand forecasting requires a consistent and repeatable monthly process that systematically analyzes previous forecasts and compares them to actual sales results.

Through this process, the data will show when your predictions were right or wrong and what actual market demand has been.

You can sort any “deviations” (when you were right or wrong) from highest to lowest and evaluate the top 20% to determine why you were wrong and how to be closer next time.

By following a monthly process and evaluating your past successes and failures, you will minimize future errors.

#2 Determine what to measure and how often

WIth an inventory and order management system that has robust analytics and reporting can measure virtually anything in your business. To accurately forecast demand, you should focus on the most relevant data points.

Here are a few data points you should consider measuring:

Competitor sales data
POS data
Amount of obsolete stock
Frequency of stockouts
Shipments
Orders

Feel free to add any more relevant data points to the list. Then, depending on your industry and rate of inventory turnover, choose whether to measure those data points on a weekly or monthly basis.

#3 Integrate data from all of your sales channels

If you are a seller with multiple sales channels – a multichannel ecommerce strategy – then you should aggregate all the data from every sales channel for each individual product into a single data set.

Once you’ve done this for all of your SKUs, you’ll be able to see which channels offer the highest ROI for each product and what your shipping and order requirements will be – helping you to make smarter decisions.

#4 Measure forecast accuracy at every level

A study by Gartner found that only 17% of respondents indicated that they conducted forecast demand at the SKU, location, and customer planning level.

This is unfortunate because a primary driver of demand volatility is increased customer requirements.

Mr. Steutermann, the research Vice President at Gartner said, “Customer or sales forecast accuracy should be measured for continuous improvement and accountability. The appropriate place to measure for continuous improvement is in the sales and operations planning (S&OP) review process.”

If you measure demand error down to the customer level, you’ll be able to better understand the source of the error – allowing you to improve your process.

#5 Maintain real-time, up-to-date data

You can’t accurately forecast demand if you don’t have accurate data.

Demand forecasting best practices revolve around access to up-to-date inventory, sales, raw materials and finished goods data.

To make smart forecasts, you’re going to need that data as close to real-time as possible so you don’t calculate demand with any missing data points and so you can continually forecast demand on a weekly or monthly basis with fresh information.