Better Inventory Planning with Basic Data Analysis
While the prediction of inventory needs will never be absolutely perfect due to variation in customer orders and external factors, a look at your inventory data can help free up cash.
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Editor’s Note: Tracey Smith is an internationally recognized business author, speaker and analytics consultant. She is the author of multiple books and hundreds of articles. Tracey has worked with and advised organizations, both well-known and little-known, on how to use data analytics to impact the bottom line. Her career spans the areas of engineering, supply chain and human resources. If you would like to learn more, please visit www.numericalinsights.com or contact Tracey Smith through LinkedIn. You can check out her books on her Amazon Author Page.
Inventory optimization is the provision of the right inventory in the right quantities to meet the supply and demand of the organization. Even today, getting the right amount of inventory in stock at the right time can be a challenge. While we would love for our customers to order equal amounts of product each month, that’s just doesn’t match reality.
There are significant benefits to inventory optimization. Inventory is essentially cash that tied up and sitting on your shelf. It can’t be used for other business initiatives. Reducing the amount of inventory for each product will free up cash. However, too little inventory will increase the risk that you will be out-of-stock for a customer order.
A reduction in inventory can also reduce associated carrying costs and reduce the risk that you are left with extra inventory when a product becomes obsolete.
If you’re a large company, the number of data records required to analyze your inventory needs might be so large that you’ll need an enterprise solution to analyze the patterns. However, if you’re a smaller company, or if you wish to analyze your inventory at the division or business until level, the number of parts you carry will be small enough that patterns can be analyzed with tools like Excel, Tableau or Minitab.
So, what exactly is it that we would like to analyze in order to make decisions about our inventory? If you’re a retailer or distributor, then the products on your shelves are likely sold as complete entities. In this case, we can examine the customer ordering patterns for each product over the past few years to determine which products are trending upward, which ones are high volume, which ones are seasonal, which ones may be approaching obsolescence and which ones are highly volatile in order quantities each month. A review of this information will provide guidance on future inventory decisions, especially when combined with an analysis of product profitability, i.e., how much each product contributes to the bottom line.
If you are a manufacturer, the inventory on your shelves is a series of parts that are used to make final products. In this case, we would like to analyze the parts usage over time and look for the same patterns as before. Low use of a specific part may indicate a newly launched product or a product heading into obsolescence. A large list of part numbers in the same category (example: gears or fittings) may indicate an opportunity for product redesign to reduce the number of unique part numbers.
The value of each part also comes into play. If a part is inexpensive, then we can afford to carry more inventory without a substantial increase to the amount of money tied up in inventory. However, if a part is expensive, we need to carefully choose the number that we keep in stock.
Volume is another factor in inventory selection. If a product and its associated parts are ordered in high volume each month, carrying too little inventory would create too much risk in disappointing a large number of customers. This is especially important if the high-volume part is also a high-profit part. The loss of revenue due to cancelled orders would be significant.
The factors discussed in this article can help you classify inventory into groups such as A, B or C parts with A representing the highest priority inventory. Classifying inventory is not a simple task since multiple factors need to be considered like ordering patterns over time, part profitability, and lead times for replenishment. If completed, it does help in providing visibility into the most crucial parts.
Regardless of the approach you use, analyzing the historical data or your customer ordering and / or parts usage can provide guidance in setting inventory levels for each item. While the prediction of inventory needs will never be absolutely perfect due to variation in customer orders and external factors, a look at your inventory data can help free up cash. In addition, if opportunities exist for part consolidation, a company can also obtain the benefits associated with reduced complexity and the time it takes to manage extra parts and suppliers.
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