Inventory Turnover

Cost of Goods Sold (COGS)

Reference: https://www.tradegecko.com/learning-center/how-to-calculate-inventory-turnover

Also known as inventory turns, stock turn, and stock turnover, inventory turnover is a measure of the number of times inventory is sold or used in a time period such as a year.

This ratio is important because total turnover depends on two main components of performance. The first component is stock purchasing. If larger amounts of inventory are purchased during the year, the company will have to sell greater amounts of inventory to improve its turnover. If the company can’t sell these greater amounts of inventory, it will incur storage costs and other holding costs.

The second component is sales – sales have to match inventory purchases otherwise the inventory will not turn effectively. That is why the purchasing and sales departments must be in tune with each other.

How to calculate inventory turnover ?

1. Determine the Cost of Goods from your annual income statement.

2. Add your Beginning Inventory to your Ending Inventory.

3. Divide the sum of the Beginning and Ending inventory in half to calculate the Average Inventory.

4. Calculate the Inventory Turnover by dividing the Cost of Goods Sold by the Average Inventory. 

The values of beginning and ending inventory can be obtained from the balance sheets at the start and at the end of the time period.

To elaborate further, Average Inventory is used instead of ending inventory because many companies’ merchandise fluctuates greatly throughout the year. For instance, a company might purchase a large quantity of merchandise on January 1 and sell that for the rest of the year. By December, almost the entire inventory is sold but the ending balance does not accurately reflect the company’s actual inventory during the year. Thus, Average Inventory is usually calculated by adding the beginning and ending inventory and dividing by two.

Use inventory turnover ratio to calculate inventory turnover period.

Let’s take the inventory analysis a step further. Once you have the inventory turn rate, calculating the number of days it takes for a business to clear its inventory only takes a few seconds. Since there are 365 days in a year, just take this number and divide it by the inventory turnover rate. The resulting number is the number of days it takes for a particular company to go through its inventory, which may be a more understandable figure. Thus, a turnover rate of 4.0 becomes 91 days of inventory on hand – the company sells through its stock of inventory each quarter. This is known as the inventory turnover period.

Benchmark your inventory turnover ratio against the industry.

A company’s inventory turnover varies greatly by industry. When making inventory turnover ratio comparison between companies, it is important to take note of the industry, or the comparison will be distorted.

For example, making comparisons between a supermarket and a car dealer will not be appropriate, as a supermarket sells perishable goods such as fresh fruits so the stock turnover will be higher. However, a car dealer will have a low turnover due to the product being a slow moving item.

In addition, low-margin industries tend to have higher inventory turnover ratios than high-margin industries because low-margin industries must offset lower per-unit profits with higher unit sales volume. Thus, only intra-industry comparisons will be appropriate and meaningful.

Moreover, it is important to understand that the timing of inventory purchases, particularly those made in preparation for special promotions or new product introductions, can suddenly and somewhat artificially change the ratio.

A useful exercise is to compare the inventory turnover rate of a potential investment against that of its competitors to see which management team is more efficient.

Measure the efficiency of your business.

Inventory Turnover is used to measure the inventory management efficiency of a business. In general, a higher value of inventory turnover indicates better performance and a lower value means inefficiency in controlling inventory levels.

Usually, a higher turn shows that the company is not overspending by buying too much inventory and wasting resources by storing non-salable inventory. It also shows that the organization can effectively sell the inventory it buys and replenishing cash quickly.

An extremely lower inventory turnover rate may be caused by overstocking or inefficiencies in the product line or sales and marketing effort. It is usually a bad sign because products tend to deteriorate as they sit in a warehouse while chalking up inventory holding cost at the same time. Furthermore, excess inventory ties up a company’s cash and makes the company vulnerable to drops in market prices.

On the other hand, an exceptionally high turnover rate may point to strong sales or ineffective buying, ultimately leading to a loss in business as the inventory is too low. This often can result in stock shortages, leading to loss of sales.

A good rule of thumb is that if your inventory turnover ratio multiplied by gross profit margin (in percentage) is 100 percent or higher, then the average inventory is not too high.

Increase profitability and reduce holding costs. 

An item whose inventory is sold (turns over) once a year has higher holding cost than one that turns over twice, or three times, or more in that time. Inventory turnover also indicates the briskness of the business. The purpose of increasing inventory turnover is to reduce inventory for three reasons:

Increasing inventory turns reduces inventory holding cost. You get to spend less on rent, utilities, insurance, theft and other costs of maintaining a stock of good to be sold.

Reducing holding cost increases net income and profitability as long as the revenue from selling the item remains constant.

Items that turn over quickly increase responsiveness to changes in customer demands while allowing the replacement of obsolete items. This is a major concern in fashion and technological industries.

Boost shareholder confidence.

A business’ inventory turnover also shows investors how liquid a company’s inventory is. Think about it, inventory is one of the biggest assets a retailer reports on its balance sheet. If this inventory can’t be sold, it is worthless to the company. This measurement shows how easily a company can turn its assets into cash.

In addition, creditors are particularly interested in this because inventory is often put up as collateral for loans. Banks would want to know if this inventory will be easy to sell.

Now that you've learnt quit a bit on inventory turnover, you just need a solid system to track and monitor your inventory so you can apply this newly-gained knowledge.

Demming PDCA Cycles

PDCA cycles enable to improve and innovate your business processes continuously and make it possible to create business scenarios, compare them and choose the best one.

PDCA 반복 사이클을 통해서 기업의 업무 프로세스를 지속적으로 개선하거나 혁신할 수 있으며, 의사결정을 위해 정교하게 정의된 시나리오들을 비교 검토함으로써 최선의 의사결정을 가능하게한다.

Reference : https://icrontech.com/blog_item/optimization-vs-heuristics-which-is-the-right-approach-for-your-business/

Optimization vs. heuristics: Which is the right approach for your business?

Author: Z. Caner Taşkın

In today’s hypercompetitive and highly complex business environment, companies are constantly searching for ways to gain competitive advantage by improving the speed, efficiency, and quality of the goods and services they deliver to customers through their supply chains. The key to unlocking success in supply chain management is being able to make optimized business decisions by finding the best possible solution to your company’s planning and scheduling problems.

There are many techniques – such as constraint programming, mathematical programming, metaheuristics, local search, machine learning algorithms and evolutionary algorithms like genetic algorithms and simulated annealing – that are used to solve supply chain planning and scheduling problems. These algorithms can be classified into two main categories, which we are going to examine in this blog: heuristics and optimization.

The aim of optimization and heuristic solutions is the same – to provide the best possible solution to a given supply chain problem – but their outcomes are often dramatically different.

Here we examine the differences between optimization and heuristics, and explore the pros and cons of each approach.

Defining the difference between heuristics and optimization

Fundamentally, every supply chain planning and scheduling problem is at heart an optimization problem. Its solution involves determining the best way to synchronize supply and demand across the supply chain network – to boost customer satisfaction and bottom-line results.

One popular technique that businesses employ to solve their supply chain planning and scheduling problems is heuristics. Simply put, a heuristic is a problem-solving approach that utilizes a practical process (commonly referred to as “rule of thumb” or “best practice”) to produce a feasible solution that is good enough to quickly solve a particular problem and achieve immediate goals – but not necessarily an optimal solution.

In contrast, an optimization model employs an intelligent, automated process to generate an optimal solution to a particular problem – taking decision variables such as production, inventory, and shipment quantities as well as constraints and key performance indicators (KPIs) into account. Supply chain optimization solutions aim to offer the best possible avenue to achieve optimal performance across your procurement, production, inventory, and distribution operations – maximizing delivery performance and overall profitability.

The pros and cons of the heuristic approach

The main advantage of adopting a heuristic approach is that it offers a quick solution, which is easy to understand and implement. Heuristic algorithms are practical, serving as fast and feasible short-term solutions to planning and scheduling problems.

The main downside of the heuristic approach is that it is – in the vast majority of cases – unable to deliver an optimal solution to a planning and scheduling problem.

Heuristic approaches can offer a quick fix to a specific planning or scheduling issue, but are not capable of serving as viable solutions that deliver the best possible results. This means that heuristics tend to “leave money on the table” – they often stop with a solution, even though there are better solutions of the same problem that yield lower supply chain cost, higher order satisfaction performance or higher overall profit. Over time, as your business model and processes evolve and develop, heuristic solutions will inevitably falter and fail – as they are simply not supple enough to accommodate your company’s evolving needs and requirements.

Another disadvantage is the lack of flexibility that heuristic approaches possess. If, for example, key decision variables, constraints or KPIs change, or if a new machine is added to the production line that shifts the bottleneck in the production process, a hard- or pre-coded heuristic may no longer be capable of serving as a valid and viable solution and might need to be reconfigured. Furthermore, a modest change in your operational processes or the underlying data patterns, such as distribution of demand over time or product mix, can have a major impact on the performance of the heuristic – and this can pose a serious risk to your company’s overall productivity and profitability.

In sum, heuristic techniques are practical and offer fast and feasible short-term solutions to planning and scheduling challenges, but lack the power and flexibility to create ongoing, optimal solutions that create pathways to greater productivity and profitability.

The pros and cons of the optimization approach

The main advantage of the optimization approach is that it produces the best possible solution to a given planning and scheduling problem.

Indeed, optimization algorithms are guaranteed to generate optimal solutions, which outperform their heuristic counterparts and enable businesses to maximize cost- and operational-efficiency.

One of the chief benefits of optimization models is their flexibility, as they can automatically adjust and adapt to take into account the myriad decision variables and changing goals, constraints, and complexities in any business environment and generate the best possible planning and scheduling solutions.

Optimization techniques empower planners to make optimized decisions and achieve higher levels of productivity and performance.

There are, though, some disadvantages to the optimization approach. Firstly, optimization models are highly sophisticated, and specific expertise and technologies are required to devise and deploy optimization solutions. For example, in order to generate an optimization solution, a thorough understanding of mathematical programming concepts and utilization of special solvers are necessary.

Also, compared to their heuristic counterparts, optimization algorithms typically take more time to execute – as they are mathematically difficult to solve. Furthermore, some real-world processes cannot be adequately modeled using linear optimization techniques, and it is sometimes difficult to model intangible business objectives such as “fairness” in an optimization model.

Which approach is right for your business?

Ultimately, there is no “best” approach to solving your supply chain planning and scheduling problems – it all boils down to which approach is right for your business.

If we compare heuristic versus optimization algorithms in terms of solution quality, the latter is the clear winner. Solution quality is often a critical success factor for tactical and strategic level supply chain optimization decisions, which makes optimization a natural choice.

But if your business needs a reasonably good solution in a short amount of time, which is often the case in real-time operational settings, then a heuristic solution may be the right choice for you.

In many cases, however, a complementary approach between optimization and heuristics is the most effective solution. ICRON supports not only optimization and heuristics, but also other algorithmic paradigms including evolutionary algorithms, rule-based algorithms, local search and multi-objective optimization. And using ICRON’s innovative modeling system GSAMS, it is possible to design hybrid solution approaches.

For example, it is possible to employ a heuristic that utilizes business know-how and decision maker experience to generate a good solution for the problem. This heuristic solution can then be passed as a starting point to the optimization model. Then the solver either proves optimality, or improves the heuristic solution instead of solving the problem from scratch.

Another hybrid solution approach that balances solution quality and computation time for those businesses that are urgently looking to solve a planning and scheduling issue, but don’t have time to wait for an optimal solution to be found is “optimization-based heuristics.” This type of heuristics employs optimization techniques to speed up the solution process and deliver solutions that are better than those generated by traditional heuristic approaches, but not necessarily optimal.

ICRON possesses the ability to create optimization-based heuristics and other hybrid solution approaches – and this is a unique feature of our Optimized Decision Making platform. Our customers benefit from this complementary approach, as they can design and deploy a planning system that perfectly fits their business requirements.

Search : Google.com

Book : Fundamentals of semiconductor manufacturing and process control

Webinar: Solving complex DC-to-store distribution challenges

Not all solutions are made equal. That adage holds true when it comes to route optimization solutions. Rigid solutions are destined to fail. However, it is possible to improve efficiency without compromising customer satisfaction. The trick here is not to measure your business according to KPIs, but to manage your business towards those indicators.

Reference: kmworld.com

Forecast Value Added: The Key to Managing the Business Forecasting Process

Business executives want their processes to be effective, efficient, and void of waste. They don't want to squander company time and resources on activities that have no benefit to customers, or to their own bottom line. So when it comes to the business forecasting process, how can they distinguish good performance from bad? How do they know that efforts are "adding value" by making the forecasts more accurate, thereby enabling better service to customers, and making more money?

Business forecasting can be a significant consumer of company resources. There may be forecasting software to license, install, and maintain, and forecast analysts to hire and train to generate the forecasts. There is also, quite commonly, an elaborate consensus or collaborative processes where forecasts are reviewed and adjusted by stakeholders in sales, marketing, finance, operations, or elsewhere. And there can also be a final executive review and signoff, where a general manager or CEO can make final adjustments before "approving" the forecast.

All of this is high-cost management time. We tend to assume these extra reviews and inputs and adjustments are making the forecast better. But the reality is that each human touch points is just one more place that biases and politics and personal agendas can negatively impact forecast accuracy. The unfortunate thing is that our traditional forecasting metrics, by themselves, cannot tell us this.

Traditional forecasting performance metrics, such as Mean Absolute Percent Error (MAPE), tell you the size of your forecast error. But MAPE tells you nothing about what the error should be - what is the best you can expect to do? And MAPE tells you nothing about how efficient you were at achieving the level of forecast accuracy you did attain. Traditional metrics, by themselves, are not enough to properly evaluate and manage forecasting process performance.

Forecast Value Added (FVA) is a forecasting performance metric that has gained wide industry adoption. FVA is defined as "The change in a performance metric that can be attributed to a particular step or participant in the forecasting process." FVA works with whatever traditional metric you use (commonly MAPE, Mean Absolute Deviation, Bias, etc.). FVA is concerned about the change in the metric due to some activity in the forecasting process. Consider an example of a simple forecasting process:

Sales History→Forecasting Model→Statistical Forecast→Analyst Override→Final Forecast

In this process, historical sales information is read into forecasting software, which models the history and generates what we call the "statistical forecast" (i.e., the forecast generated by the software). At that point, the forecast analyst can review and adjust the statistical forecast, resulting in the final forecast.

FVA analysis is the application of basic scientific method to the business forecasting process. Just like the evaluation of a new drug, it involves comparing a treatment (e.g., the new drug, or the statistical forecast) to a placebo. If those patients who take the new drug do better than those who take the placebo, we may conclude that the drug is "adding value" by helping cure their affliction. Similar, if the statistical forecast is more accurate than a "naïve forecast" (described below), then we may conclude that our software and modeling efforts are "adding value" by making the forecast better.

A naïve forecast is something simple to compute, requiring the minimum of effort, and serves as the "placebo" in FVA analysis. For example, using last month's actual sales as the forecast for this month's sales. Such a forecast can be generated at virtually no cost to the organization. So, if our resource consuming forecasting process is not performing any better than the naïve forecast, why bother? Simply use the naïve forecast and free those resources to do more productive activities (or just eliminate those resources that had been used in forecasting).

In conducting FVA analysis, we make this kind of comparison for each sequential step in the forecasting process. In our process example above, we would compare the statistical forecast to a naïve forecast, and also compare the analyst overridden final forecast to the statistical forecast. We might find, for example, that the statistical forecast is better than the naïve forecast (we should certainly hope to find this, given how much we spend on forecasting software!), but that the analyst override just made it worse.

FVA is a tool in the "lean" approach to business management. FVA allows the organization to identify waste - those process steps that are failing to improve the forecast, or may even be making it worse. By eliminating the non-value adding steps or participants from the forecasting process, those resources can be redirected to more productive activities. And by eliminating those steps that are actually making the forecast worse, you can achieve better forecasts with no additional investment.

Discrete and Continuous Probability Distribution

Reference : Wikipedia

In probability theory and statistics, a probability distribution is a mathematical function that, stated in simple terms, can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. For instance, if the random variable X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 for X = heads, and 0.5 for X = tails (assuming the coin is fair).

In more technical terms, the probability distribution is a description of a random phenomenon in terms of the probabilities of events. Examples of random phenomena can include the results of an experiment or survey. A probability distribution is defined in terms of an underlying sample space, which is the set of all possible outcomesof the random phenomenon being observed. The sample space may be the set of real numbers or a higher-dimensional vector space, or it may be a list of non-numerical values; for example, the sample space of a coin flip would be {heads, tails} .

Probability distributions are generally divided into two classes. A discrete probability distribution (applicable to the scenarios where the set of possible outcomes is discrete, such as a coin toss or a roll of dice) can be encoded by a discrete list of the probabilities of the outcomes, known as a probability mass function. On the other hand, a continuous probability distribution (applicable to the scenarios where the set of possible outcomes can take on values in a continuous range (e.g. real numbers), such as the temperature on a given day) is typically described by probability density functions (with the probability of any individual outcome actually being 0). The normal distribution is a commonly encountered continuous probability distribution. More complex experiments, such as those involving stochastic processes defined in continuous time, may demand the use of more general probability measures.

Demand sensing is a forecasting method that leverages new mathematical techniques and near real-time information to create an accurate forecast of demand, based on the current realities of the supply chain. Gartner, Inc. insight on demand sensing can be found in its report, "Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven." ^[1]

Traditionally, forecasting accuracy was based on time series techniques which create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. However, past sales are frequently a poor predictor of future sales. Demand sensing is fundamentally different in that it uses a much broader range of demand signals (including current data from the supply chain) and different mathematics to create a more accurate forecast that responds to real-world events such as market shifts, weather changes, natural disasters, consumer buying behavior etc.

Reference: Wikipedia.org

The most valuable inventory optimization solutions

How inventory optimization opens pathways to profitability