What is Inventory Optimization?
Inventory Optimization is defined as a method of balancing the manufacturers’ capital investment constraints and goals along with the defined service-level goals over a large assortment of stock-keeping units (SKUs) while considering all demand and supply volatility situations. It includes the practice of having the right levels of inventory to meet your target service levels while keeping a minimum amount of capital locked for inventory.
Inventory optimization is also considered as the next level of inventory management for warehouse and supply chain managers. Which is why, manufacturers can achieve holistic Inventory Optimization, by considering both supply and demand fluctuations.
Inventory Optimization can easily help manufacturers overcome warehouse management and stocking problems. Studies show that almost 65% of damaged inventory ends up that way due to the incorrect method of packing, storing, or securing in freight containers. Inventory optimization can help overcome these errors to a large extent. Also in 2015, the global cost of overstocking goods was $470 billion, while the cost of under-stocking was more than $600 billion. With accurate Inventory Optimization, manufacturers can streamline stocking issues using predictive analytics and data-driven insights.
What are the Important Aspects of Inventory Optimization?
1. Demand forecasting
Accurate demand forecasting is an important element of inventory optimization. Demand and Supply chain forecasting can be done in several ways depending on the kind of products or services involved, the product life cycle, and the industry being catered to. Using last year’s or last period’s demand numbers and also using specific request forecasts from the sales teams could be one technique. Manufacturers need to have complete knowledge about the specific product lifecycle to accurately forecast demand and to find out where in the lifecycle are those SKUs. They also need to keep a track of the seasonality trends and new product introductions which can impact demand forecast numbers.
2. Inventory strategy
A good understanding of which products need to be stocked, in what quantities and across what time intervals, is also important. Using ABC analysis to stock SKU quantities is a good method as it divides the SKUs based on their annual consumption value. It also helps understand the safety stock calculations to address sudden fluctuations in demand, supplier variations, or other unforeseen disruptions. Finally, you also need to consider the number of warehouses you have to optimize your inventory to be distributed over your locations in the right quantities at the right time and place.
3. Stock replenishment
This step is important to understand which quantities are needed to be reordered at what points in time and then actually place the order for them. Manufacturers need to keep in mind the supplier reliability for this as each supplier has his/her own lead time and production cycles. Also, manufacturers need to keep a track of the goods which are in transit and not just those which are in stock at the warehouse. While this may be obvious, most ERP systems do not capture this information easily.
Challenges to Inventory Optimization
Demand forecasting challenges
This is a primary area of focus as forecasting sales with fluctuating demand cycles becomes difficult to achieve accurately. There is a direct impact as the less accurate the sales forecast the lesser accurate are the stocking levels.
Traditional inventory management
Most traditional inventory management methods do not work well with modern e-commerce techniques. The more complex the volume sales and bulk shipments, the more difficult traditional inventory management becomes.
Today’s multi-channel order fulfillment methods create a large number of complex variables influencing sales velocity. Also, since online and offline store channels operate differently with different KPI targets, creating optimum inventory levels to manage all of this could be a challenge.
Those items that cannot be sold due to irrelevance or going out of style or not being useful anymore can deter inventory optimization efforts. Keeping dead stock for longer periods of time (usually beyond 12 months) can add to the dead weight and impact the purchase of similar items in the future, thus hampering overall inventory management efforts.
Lack of automation
Most retailers and suppliers today lack automated and digitized distribution processes which make it difficult to sufficiently add analytical, predictive, and Artificial Intelligence capabilities. This prevents quick decisions on optimal stocking levels.
Lack of performance tracking mechanisms
Not having accurate inventory tracking and reporting to measure tangibles as well as intangibles can impact inventory optimization efforts. To prevent inventory problems, product managers should monitor daily fill-rates, and inventory turnover based on sales cycles.
Inventory Optimization Best Practices
1. Standard inventory reviewing systems
Using the right inventory reviewing system can add a lot of value to help in inventory optimization efforts:
- Continuous Review System: In this type of review system, the same quantities of items are ordered in each cycle. Manufacturers must ensure they monitor inventory levels continuously and replenish stocks whenever the quantity of an item drops below a set level.
- Periodic Review: This system is adopted when manufacturers order products at the same time each period. At the end of each period, the items needed are ordered based on quality levels at that point in time. This system does not include any fixed reorder levels.
2. Adequate quality control practices
Having an accurate quality control process can ensure the quality of inventory is directly linked to customer satisfaction and business growth. To start with, manufacturers can create checklists that provide all procedures to be followed while taking stock of products and then move to standard operating procedures to qualify or disqualify products. Establishing common goals of inspection can help streamline quality check procedures. This inventory optimization best practice can avoid over or under stocking as workers will no longer offer customers inappropriate merchandise.
3. Relevant forecasting techniques
To help enhance the accuracy of predictions of future stocks, manufacturers apply a variety of forecasting techniques to calculate optimal stock levels. These forecasting techniques act as guideposts to guard them against over-and under-stocking. Usually, a dynamic mix of historical and predictive forecasting measures are used to assess future inventory needs. These are often completed with computer-aided multi-model simulation methods to analyze the demand data and determine optimal stock levels. Additionally, some manufacturers also leverage AI-driven analytics to predict potential customer demands.
4. Leverage just-in-time (JIT) principles
Over the past two decades, several retailers and manufacturers have restructured their inventory processes according to JIT principles to address growing consumer demand for newer and customized products. These manufacturers achieve JIT & supply chain efficiency in inventory optimization by streamlining their inventory purchasing and delivery operations. This also helps them eliminate waste in the inventory process by practicing a number of Just In Time manufacturing, purchasing and delivery strategies, and remove bottleneck operations that constrain inventory flow through factory operations.
5. Well planned inventory budget
Many manufacturers use an annual inventory budget which is usually prepared well in advance before the inventory is procured. The inventory budget should include the total cost of ownership to keep inventory on hand during that year’s account period. The budget incorporates materials cost, fixed operational costs, transportation and logistics costs, redistribution costs, and any additional miscellaneous costs that impact the total cost of ownership.
Why is Inventory Optimization Software Right for Your Factory?
The core concept behind inventory optimization is to ensure adequate inventory is available in the right quantity, at the right place, at the right time and at the right cost. With a good Inventory Optimization Software, manufacturers can ensure the above and benefit in multiple ways:
1. Increase profit margins with real-time insights across inventory levels, order tracking status, boost customer satisfaction, and enhance performance throughout the sales process.
2. Realize the potential to reduce operating and inventory costs by effectively managing labor requirements and optimize inventory levels in the warehouse. This can help improve business performance by becoming more lean and profitable.
3. Improve quality by getting things right in the first attempt. This helps increase overall operational excellence and cuts down inventory investment by reducing unnecessary reorders due to inaccurate knowledge of what stock is already on hand.
4. Enhance performance by leveraging relevant data and reports to understand product variability and to improve strategic and operational decision making.
5. Offer competitive rates with a better understanding of your inventory to give the lowest price or fastest shipping guarantees. Manufacturers also get an understanding of what markets and customers need, and based on this they drive organizational design, strategy, products, and services.
The Future of Inventory Optimization with Artificial Intelligence
Manufacturers usually have a large amount of capital investment tied up to the inventory they hold. Studies show that including accounts receivable and accounts payable, inventory represents $1.1 trillion in cash – this is equivalent to 7% of the United States’ gross domestic product.
The good news is that Artificial Intelligence (AI) has the potential to drive great value from this cash. With Industry 4.0 and its widespread technologies, manufacturers are leveraging AI-based smart and data-driven distribution centers. Using this technology, distributors will move away from guesswork to predict demand for products and will instead merge datasets to make accurate predictions about the future. This will enable them to make well-informed business decisions.
ThroughPut’s ELI is an industry-scale, automated, enterprise-ready Artificial Intelligence (AI) software, using which manufacturers can drive inventory optimization efforts with a well-instrumented, data-driven strategy. ELI helps manufacturers boost inventory turnover and labor productivity rapidly at scale. It also helps cut down multiple shipping costs and streamline inventory to critical expenses only.