Definition of inventory optimisation

Inventory optimisation is the practice of having the right stock to meet your demand, and buffer against unexpected disruption, while avoiding wasteful surplus. At its best, inventory optimisation is an agile practice that not only responds quickly to risk and opportunity but also has the capacity to predict and prepare for it.

Inventory optimisation is more crucial than ever

Inventory optimisation remains one of the most challenging aspects of supply chain management, vulnerable to countless external forces—from social trends and economic shifts to weather events, geopolitical dynamics, and competitive pressure. Recent years have revealed how quickly these supply chain disruptions can overturn traditional planning approaches, emphasising the need for more agile, data‑driven, and resilient inventory strategies.

In addition, the rise of same-day and next-day delivery expectations has fundamentally changed how companies manage stock. Instead of relying on a few centralised warehouses, businesses now operate multi-location distribution networks that demand greater speed, accuracy, and real-time visibility. With heightened online competition and shifting customer loyalties, organisations must balance service levels with tighter margins and greater uncertainty.

Companies that employ modern inventory approaches—using AI‑driven forecasting, scenario planning, and intelligent automation to position stock closer to customers—can reduce holding costs, protect service levels, and respond quickly to market volatility.

The difference between stock management and stock optimisation

Stock management and stock optimisation are both a part of the broad practice and category of stock control.

Inventory management refers to the aim of setting high productivity and efficiency targets for all inventory operations. Modern business and supply chain planning technologies support these processes by giving supply chain managers greater visibility across all the links in the chain. The Internet of Things (IoT) and cloud-connected devices and assets can be automated for greater efficiency in manufacturing. Production, warehouse, and logistics processes also achieve greater efficiency through the application of intelligent technologies such as artificial intelligence (AI), machine learning, robotics, and robotic process automation.

Inventory optimisation is a subset of inventory management that refers more specifically to profit margins and minimising loss. Holding surplus stock leads to loss and waste. It takes up space, becomes obsolete, and often doesn’t sell or must be sold at reduced prices. On the other hand, as we saw during the pandemic, shortages and unexpected demand are the flip side of the inventory coin, where the costs come in the form of loss of potential profit and damage to the brand. Therefore, the goal of inventory optimisation is to best forecast demand and maximise the financial output of the inventory for the company.

The different types of stock

From the consumer’s point of view, inventory primarily consists of finished goods. But for a business, inventory is anything they have to keep in stock, maintain, and replenish. If a company makes soup, then “inventory” could be anything from the seeds used to grow the tomatoes, all the way to the fuel in the company delivery lorries that take it to the grocery shop. Considering inventory management in this more holistic manner provides a greater appreciation of its complexity.

There are four basic stock types:

1. Raw materials: All stock that eventually ends up in the finished product.
2. Work-in-progress (WIP): As the name suggests, this is all the stock that is currently being prepared and packaged. This is an expensive and risky stage, so inventory optimisation solutions can be applied to help find the most cost- and time-effective processes.
3. Finished goods: The most commonly perceived meaning of what stock is, in its packaged ready-to-sell state.
4. Maintenance, repair, and operating supplies (MRO): All the stock required in the manufacturing, production, and delivery of the items. Inventory optimisation is applied to best balance surplus and shortage of these non-consumer items.

The challenges of traditional stock optimisation

Since there have been supply chains and warehouses, one of the greatest challenges to achieving inventory optimisation has been the balancing act between “just enough” and “not too much.” Demand forecasting has traditionally been a retrospective practice. Even though inventory optimisation and demand forecasting experts are highly skilled, there is only so much that human analysis and prediction can achieve. Therefore, linear supply chains that are powered by legacy systems will always be vulnerable, no matter how much expertise is applied. Some of the most common challenges include:

• Legacy systems that can neither gather nor manage big data: Manual and non-connected technologies cannot handle volumes of disparate and unstructured data. It is from this data—through the application of smart technologies such as AI, machine learning, and advanced analytics—that some of the greatest accuracy is achieved, from risk prediction to demand forecasting.
• Rapidly changing customer demands: Every year, consumer demand for swift delivery and bespoke products is increasing. Also, product lifecycles are shorter than ever. It’s expensive for companies to scale up their logistics and supply chain networks to meet these demands, so greater precision is being requested from inventory optimisation.
• Increased competition: An implication of Industry 4.0 and intelligent, connected supply chain technologies is that businesses can set up and grow more quickly than ever—all managed from a central hub. This has led to an unprecedented level of competition and consumer choice. Inventory optimisation solutions are increasingly sought after to help provide a competitive edge.
• Weather events and natural disasters: Every year, we are seeing more debilitating storms and destructive wildfires. Obviously, there is no way to accurately predict such events, but with the use of advanced analytics and cloud-connected solutions, inventory managers can give themselves a fighting chance during the resulting periods of fluctuating demand.

Inventory optimisation forecasting processes

There is a wide range of inventory optimisation challenges from business to business. For certain seasonal or B2B products, the process may be fairly straightforward, whereas large retailers, for example, may have hundreds or thousands of SKUs and a highly changeable market and customer base.

The fundamental practices that underpin inventory optimisation have not changed for decades—even centuries. But, what has changed are software solutions that enhance these processes and the specialists who carry them out. But even the most sophisticated digital systems are still grounded in many of the familiar and traditional stock optimisation protocols and formulae:

• ABC analysis allows you to identify the most and least popular products as well as those that are most and least profitable. This has traditionally been accomplished through the analysis of past sales data. But with advanced analytics and smart technologies, it is now possible to better predict trends and anticipate rising and falling stock requirements before they occur.
• Demand forecasting with predictive analytics helps anticipate customer demand. It is also used to help predict trends or risks. Again, where this was traditionally a more backward-looping process, inventory management software now allows supply chain managers to minimise the risk of shortages and waste, and more accurately forecast demand.
• Materials requirements planning is a system that manages planning, scheduling, and stock control for manufacturing. Increasingly, legacy MRP systems are being replaced by integrated business planning systems and demand-driven MRP (DDMRP) systems that deliver greater accuracy and resilience.
• Reorder point formula reflects the minimum amount of stock before you need to reorder. This has traditionally been a complicated process because it differs from product to product—even within very similar products. For example, white socks and black socks may well have a different reorder point. Inventory optimisation technologies can keep even the most complex multi-location inventory levels accurate and visible—everywhere and in real time.
• Perpetual stock management is particularly relevant for fast-moving consumer goods (FMCG) where products move at lightning speed. With smart technologies, perpetual inventory management processes can be fully automated across omnichannel purchasing touchpoints. And machine learning can help these tools become smarter and more accurate over time, even monitoring news, trends, and weather reports to provide live insights and stock status reporting.
• Safety stock and inventory buffers is the process of ensuring that there are realistic inventory buffers in case of the unexpected. Since supply chains began, this has been a fundamental challenge because shortages and wastage both lead to a loss of income. Modern supply chain software solutions bring speed, connectivity, and advanced data analysis functions to the stock management process. This allows businesses to optimise their buffer margins with impressive accuracy.

Inventory optimisation systems: Benefits and outcomes

Historically, the benefits of even small improvements to strategic inventory optimisation could be realised in lowered costs and better profit margins. With the application of integrated business processes and stock management software, these benefits become more robust and measurable—and only improve over time as the software learns and adapts.

• Greater business-wide visibility: The enhanced transparency enabled by inventory optimisation software extends from sales, marketing, and accounting to raw materials suppliers and even global partners, assets, and expenses. Cloud connectivity enables all teams involved in the supply chain to work together in real time.
• Improved demand forecasting and predictive capabilities: Smart technologies can process complex data from sources both within and outside the business—and provide accurate predictions and insights. When supply chain technologies are powered by AI and machine learning, predictive analytics and demand forecasting become more accurate and insightful.
• More sophisticated optimisation outcomes: With intelligent systems that can analyse complex and diverse data sets, inventory managers can see not only which products are the most profitable, but also things such as which locations are best for each SKU and which combinations of products sell best at different times of the year.
• Scalability: Companies must scale up quickly for many reasons including success and general growth, unexpected events, or seasonality. Intelligent software and modern databases are infinitely scalable and can increase and optimise operations on a global scale.

Going above and beyond with multi-echelon inventory optimisation (MEIO)

Complex (especially global) supply chains benefit from MEIO solutions, which build on the basics of traditional inventory optimisation but use modern supply chain and cloud technologies to obtain a more centralised, real-time picture of global operations. An effective MEIO solution recommends the optimal stock levels at each link—or echelon—in the supply chain by simultaneously optimising stock balance across multiple locations.

With an MEIO approach, manufacturers can analyse demand forecasts with an end-to-end view of the supply chain. And as businesses grapple with consumer expectations for fast, easy delivery and convenient returns, MEIO solutions help them cope with today’s more geographically distributed, smaller inventories.

Begin using inventory planning best practices

Modern technologies and smart solutions can deliver enormous benefits to every area of supply chain management, but, in the end, it’s practices and people that run a business. Cloud connectivity helps you connect to your teams and supply chain partners around the world, so put that visibility to work by sharing and rewarding sound practices and efficient stock planning strategies.

1. Use robust demand forecasting techniques. Demand forecasting is a key factor in informing how businesses strategise inventory management and other processes, such as resource purchasing, inbound logistics, manufacturing, financial planning, and risk assessment.

2. Make your inventory budget a Q1 priority. Every business has cycles and changes throughout the year. By establishing a quarter-by-quarter stock budget, supply chain planners can set more realistic and deliverable targets and KPIs.

3. Implement standard inventory review systems. Reviewing systems can be customised for different types of inventory and help to improve efficiency and streamline workflows. It is not uncommon for complex organisations to use different systems within their business. The main point is having consistency and putting a plan in place. There are two main types of inventory review systems:

   • Continuous review system: In this model, the same quantities of items are ordered in each cycle and inventory managers must monitor stock levels continuously and replenish stocks whenever the quantity of an item drops below a set level.
   • Periodic review: In this model, inventory managers order products at the same time during each business cycle. At the end of the cycle, necessary stock is ordered based on quality levels at that point in time. This system does not use fixed re-order levels and is more efficient for slower-moving products.

4. Listen to your customers. Many businesses only listen to the squeakiest wheels and end up making decisions based upon the loudest feedback. The best stock management software solutions will be able to regularly gather and analyse data from all your customers and buyers and offer insights and recommendations – in real time – about that input. This supports inventory optimisation efforts by ensuring that inventory management decisions are informed and data-driven.

5. Use just-in-time (JIT) and on-demand principles. Shorter-than-ever product lifecycles and growing consumer demand for speed and personalisation mean that inventory optimisation must be fast-moving and agile. Technologies such as 3D printing and robotic automation enable businesses to maintain virtual inventories. Supply chain manufacturing and logistics increasingly operate using networks of on-demand providers and suppliers. With intelligent software, inventory managers can make real-time inventory optimisation decisions, confident that the data is backing them up.

Next steps to better inventory planning and optimisation

As with any business transformation, it is important to establish good communication across your inventory optimisation and supply chain team. Start by breaking down silos, developing robust change management and communication strategies, and speaking to your team leaders. Within your workforce lies a goldmine of information about current risks and opportunities, which can be utilised to establish actionable inventory planning and optimisation strategies. Software suppliers can also help you develop a roadmap to get your inventory optimisation journey underway.