Articles from the RSI team on inventory, planning, and supply chain decision-making.
Big data has been a buzzword in the business world for many years now, and it’s no surprise that supply chains are also trying to utilize its potential. With the rise of e-commerce and globalization, supply chain management has become increasingly complex. Companies need to be able to quickly adapt to changing consumer demands, manage inventory levels efficiently, and maintain high levels of customer satisfaction. Big data promises to help with all of these challenges by providing valuable insights and predictive analytics.
However, despite the potential benefits, many supply chains still struggle to fully reap the benefits of big data. So why is this the case? Here are a few reasons:
One of the biggest challenges in utilizing big data is ensuring its quality. In supply chain management, there is a vast amount of data being generated from various sources such as suppliers, transportation providers, and customers. This data can often be incomplete, inconsistent, or inaccurate, making it difficult to trust and use for decision-making.
To fully reap the benefits of big data, supply chains need to invest in data governance practices and technologies that can ensure data quality and integrity.
Even if the data is of high quality, it may be scattered across different departments and systems within a company. This can create silos where information is not easily shared or integrated, making it challenging to gain a holistic view of the supply chain operations.
To overcome this challenge, supply chains need to break down these silos and adopt data management strategies that allow for the integration and sharing of data across different functions.
Big data is only useful if it can be analyzed and turned into actionable insights. However, many companies lack the necessary skills to make sense of large datasets. Data scientists who possess the technical expertise to work with big data are in high demand, and companies may struggle to find the right talent.
To address this issue, supply chains need to invest in training their employees or look for partnerships with third-party analytics providers who can help them make sense of big data and extract valuable insights.
Implementing a big data strategy can be costly and complex, especially for smaller companies with limited resources. Building and maintaining the necessary IT infrastructure, hiring skilled personnel, and investing in data analytics tools can be a significant financial burden.
To overcome this challenge, supply chains can start small by identifying areas where big data can make the most impact and gradually scale up their efforts as they see results and gain more resources.
While big data holds immense potential for supply chain management, there are several challenges that need to be addressed. By investing in data quality, breaking down silos, building analytics capabilities, and managing costs and complexities effectively, supply chains can fully reap the benefits of big data and gain a competitive advantage in today’s fast-paced business landscape.
What’s the difference between seeing the Loch Ness Monster and achieving perfect inventory balance? One involves a completely unrealistic tale of lore. The other involves a large prehistoric water-dwelling creature with a long neck. Dad jokes aside, the effective management of inventory is a monumental challenge for any business that is involved in manufacturing, moving, storing, or selling physical goods. As a team of career supply chain practitioners, we have found inventory balance to be a source of much consternation and fascination.
Inventory (or the lack of it) is something that touches all our lives. In many ways, Amazon has greatly reduced the impact we experience when inventory is poorly managed. Need something in a day or two? Just find it on Amazon and order away without concern over shipping costs (if you happen to be a “Prime” member). Looking for something at a local store but can’t find it on their shelves? No problem, you can order it from Amazon right on your phone before you even leave the store. Above all else, Amazon is a master of distribution and inventory management. The expert supply chain teams at Amazon have insulated many consumers from the challenges that normally come with needing a particular thing at a particular time. But for manufacturers, wholesalers, distributors, and retailers, the inventory dilemma is very real and very impactful.
So, what’s this inventory dilemma all about anyway? Having too much inventory means a company’s working capital is tied up, not generating revenue, and likely accruing costs (storage, unnecessary processing, scrappage). Having too little inventory means that customers can’t give the company money in exchange for items they wish to purchase. This quite literally defeats the purpose of being in business in the first place. Unfortunately, many enterprises will face both too much and too little inventory at the same time. It’s a cruel fact of business. Forecasting (or educated guessing) of what future demand will be for a company’s products is really hard and is almost never very close to on point at an individual item level.
In the early days of the COVID-19 pandemic, hyper-efficient supply chains ground to a halt as key participants were impacted by dramatic plant closures and immediate pauses in distribution channels. For example, the auto industry supply chain, which has been designed to leverage efficient, connected suppliers, was particularly impacted by disruptions. Ashwani Gupta, Nissan Motor Co.’s chief operating officer, highlighted this reality in a Wall Street Journal article: “The just-in-time model is designed for supply-chain efficiencies and economies of scale,” he said. “The repercussions of an unprecedented crisis like COVID highlights the fragility of our supply-chain model.” In response, some automakers began stockpiling certain parts in an attempt to avoid especially harmful choke points.
As with many aspects of life, the only thing certain about supply chains is that they face constant uncertainty. All businesses face both demand and supply variability. An individual customer doesn’t have precise knowledge of which products they will want to order. Nor do they know the quantities or timeframes they’ll want those products for. Forecasts can provide a rough estimate for future orders (product mix, quantity, and timeframe) but still don’t provide an exact answer. Think about your own individual consumption patterns. If you were asked to forecast all of your purchases for next month, could you do it? You could probably come up with something if you had to. But, you likely wouldn’t appreciate being strictly bound to that forecast without any room to purchase in a way that deviated from your prior month’s estimate. Now imagine you’re an enterprise that offers hundreds (if not thousands) of different products to thousands (if not millions) of potential customers. Preparing for the right level of material purchasing, capacity, production starts, storage, and transportation is no small feat!
Speaking of preparing to support forecasted customer demand, supply variability is also a daily companion of operations around the world. Even if a perfect demand forecast could be conjured (crystal ball? time travel?), supply chains are faced with events that are clearly not within their control. We need look no further than the COVID phenomenon. While predictions of a global pandemic have been present for some time, it would be impossible to know the ways in which this particular global pandemic impacted multiple major facets of our society and business. As highlighted above, hyper-efficient systems don’t deal well with major outlier situations. Per Merriam-Webster, a buffer is “a means or device used as a cushion against the shock of fluctuations in business or financial activity.” Reducing supply chain buffers looks good financially...only if variability is within normal or understood ranges. The pandemic has exposed just how interdependent these global supply chains really are. Highly efficient and interdependent supply chains are particularly susceptible to non-normal variability. Stockouts at a manufacturer in Asia can not only impact retail customer sales in Europe but also every stage of the supply chain in-between. Systems without buffers will feel shocks that systems with proper buffers will not.
So, is the answer to start adding buffers all over the place? Not quite. Maintaining a reasonable level of safety stock (also known as buffer stock) is an accepted means of buffering against the variability that occurs in both demand and supply signals in our real world. Hopefully you agree that having too little inventory is bad for enterprises. Stockouts mean that willing and able customers are forced to find what they need elsewhere. Often, customers permanently switch from one enterprise to another when they can’t find what they need, after just a single disappointing experience. On the other hand, maintaining too much inventory can be costly, as it ties up capital that could be used elsewhere and requires storage and space. Holding the “right” level of safety stock allows a company to strike a balance between having enough inventory to meet demand, while avoiding the costs and risks associated with holding too much inventory for too long.
As the impacts of the pandemic subsided, supply chain conditions began to flow more freely. At the same time, demand softened in many industries, and companies began to face more of the challenges associated with holding too much inventory (working capital locked up as inventory, added storage costs, unnecessary processing, and scrappage). Given all this variability, the effective management of safety stock has never been more important. Enterprises need not feel like they are trapped between a rock and a hard place though. Right Sized Inventory’s patented predictive analytics incorporates all the required supply chain factors to accurately determine optimal inventory levels. Items are run through over 2,000 simulations to find the inventory level that will most effectively achieve the desired service level without excess. Variability can’t be eliminated, but it can be modeled and managed via counter measures determined through the simulations including adding safety stock in appropriate quantities and locations based on known market conditions.
Safety stock is a key component of inventory management that helps companies protect against unexpected disruptions, maintain continuity of operations, and reduce the risk of overstocking. By carefully managing their safety stock levels, companies can ensure that they have the inventory they need to meet customer demand and maintain a strong, reliable supply chain without incurring those unnecessary financial risks associated with too much inventory. And that’s an outcome that is good for all of the bottom lines!
When a company finds itself with too much of one item and not enough of another, the first instinct is to blame the forecast. The response is familiar: a new demand planning tool, more data sources, a forecasting initiative with an accuracy target. Accuracy may improve by a few points. Yet the warehouse is still full of the wrong things and customers are still waiting on the right ones.
The problem is rarely the forecast alone. A forecast is an input. What determines inventory performance is the set of decisions made with that input.
A forecast estimates what customers will buy. An inventory decision answers different questions: how much to hold, where to hold it, when to reorder, and how much buffer to carry. Between the forecast and those answers sit lead times, supplier reliability, order minimums, service targets, and the cost of the capital tied up in stock.
Two companies with identical forecasts can end up in very different inventory positions depending on how they translate that forecast into policy. One holds the right stock in the right places. The other carries excess on slow movers and runs short on the items that matter most to its customers.
At the individual item and location level, forecasts will always carry error. This is especially true for the long tail of slow-moving and intermittent items that make up a large share of most catalogs. Better methods can narrow the error, but they cannot remove it.
The more useful question is how large that error is and how it behaves. Policies that treat the forecast as a single number tend to compensate with blanket rules, such as keeping a fixed number of weeks on hand for every item. Those rules overprotect some items and underprotect others. Policies that account for the full range of likely outcomes can set stock levels deliberately, item by item.
In our experience, the largest gains in inventory performance tend to come from the decisions downstream of the forecast:
Reorder points and safety stock set by rules of thumb that have not been revisited in years. Lead-time variability that is ignored or averaged away. The same service target applied to every item, regardless of its value to customers. Inventory positioned in the wrong locations across a distribution network.
Each of these is a policy problem, and none of them is solved by a more accurate forecast. Correcting them often frees more working capital, and protects more service, than the next forecasting upgrade would.
The practical first step is to review current inventory policies against how demand and lead times actually behave, item by item and location by location. Where a policy is out of step with reality, the gap shows up as excess, as stockouts, or as both.
This is why RSI starts with an assessment before recommending changes. Running current policies through a simulation of real demand and supply variability shows where inventory is misallocated and what a better policy would look like, before any money is spent.
Better forecasts are still worth pursuing. But they deliver the most value when the decisions built on top of them are sound.
Set up a call with one of our team and we’ll walk you through a demo, identify trouble spots in your business, and see how implementing RSI can transform your business through data-driven inventory optimization.
