Working Capital Optimization: Safety Stock
When it comes to inventory management, companies often underestimate the importance of safety stock and the need to maintain optimal levels. While it may seem like the path of least resistance from a service level standpoint, holding excessive inventory stock level leads to suboptimal working capital management and, as a result, hurts profitability by curbing one of its key components: capital turnover, as Trindent Consulting has seen on medical devices inventory improvement projects. Although determining optimal inventory level is challenging for several reasons, there are ways of optimizing it without disrupting the chosen service level.
Calculating Optimal Stock Levels
Depending on the business realities and the type of inventory management (Fixed Order Quantity or Q-model or Fixed Time Period or P-model), the two main questions of inventory management are when and how much to order. The answer to both questions must include two components: covering demand between the reorder periods, and safety stock to account for demand variability. If it were not for demand variability, the decision on the size and timing of orders, and therefore inventory levels, would boil down to a simple exercise in Economic Order Quantity (EOQ), which balances ordering costs with inventory holding costs to arrive at the optimal frequency and size of ordering based on stable demand. In this perfect scenario, we would boast 100% in-stock and fill rates and the minimum inventory determined by the EOQ calculation.
However, there is always demand variability and the desired service level to take into consideration. Service level is particularly important in the healthcare industry, where it’s not only desired but required.
To show the logic behind inventory management, let’s look at the Q-model. Let’s assume we are dealing with a product for which the expected demand is Dp, for the period in question P. The lead time for the delivery from the supplier (or warehouse) is L. Let’s also assume that the demand is random and follows a normal distribution (which will not always be the case) with the mean µ and standard deviation of σ. We can then calculate stock quantity S, which we should be attained. As we will see below, the stock quantity will contain a sufficient amount of product to cover demand for period P+L plus the safety stock, which will depend on the service level we will choose to pursue. The formula for calculating S is as follows:
S=D_(P+L)+z*σ_(P+L)-I
where:
D_(P+L): is the demand for the period P and the lead time L, and can be calculated as D_(P+L)=µ*(P+L)
z: is the number of standard deviations in normal distribution corresponding to the service level we choose
σ_(P+L): is the standard deviation of the demand for the period P+L
I : is the inventory on hand at the time of ordering.
Notably, z*σ_(P+L) is the safety stock portion and depends on the variability of the demand, represented by the standard deviation, and the chosen service level, represented by the number of standard deviations corresponding to it.
Choices and Challenges
While calculating safety stock using this framework seems like an easy exercise, there are several challenging choices that need to be made, including determining the properties of expected demand when past performance may not be a good indicator of the future. This may bring the mean and the standard deviation of the normal distribution into the spotlight, and sometimes even the distribution itself may not be normal. Another challenge is lead time variability. While the formula above assumes lead times are stable, there may need to be an additional level of analysis to arrive at a proper value for it.
The main challenge, however, is choosing the necessary service level to meet. The most calculated approach to determining service level follows the same logic as EOQ determination: by counter-balancing costs of overage (or holding costs, or h) and costs of underage (also known as backorder costs, or b), aiming to minimize both. Holding costs include the cost of capital, storage, obsolescence, and other costs related to holding inventory. Backorder costs include the lost profit from transactions that do not happen due to stock-out situations. It is, however, very common to attribute additional meaning to backorder costs in an attempt to evaluate other negative effects of a stock-out, such as reputational cost and the effect on market share. Since we measure service level as the probability that stock-out does not occur (Pr(DP+L≤S)), the optimal service level can be reflected as follows:
Pr(D_(P+L)≤S)=b/(h+b)
As we can see, depending on the value attributed to backorder costs (b), stocking out may well be decided as prohibitive.
In addition to properly calculating necessary stock levels, there are several strategies to minimizing inventory without affecting the critical service levels. For example, organizations often apply the same calculation to all the products in their roster, so products that are very important may dictate the service level for the entire list. By a simple stratification of products into A/B/C tiers and by assigning appropriate service levels to each, considerable savings may be attained.
Click here to find out how Trindent Consulting can help your organization optimize your safety stock.
Six Sigma: What’s Needed to Succeed
Six Sigma, the popular methodology for process improvement, is a statistical concept that identifies the variation inherent in any process. By subsequently working to reduce these variations once it defines them, the Six Sigma methodology diminishes the opportunity for error, thus reducing process costs or increasing customer satisfaction.
The core objective of Six Sigma is to implement a measurement-based strategy that focuses on process improvement and variation reduction. At a high level, this is accomplished through the use DMAIC, an improvement cycle (define, measure, analyze, improve, control) for existing processes that lack efficiency, and the statistical representation of Six Sigma, which describes quantitatively how a process is performing.
There are, of course, many proven methodologies an organization can consider for process improvement. So, why should they use Six Sigma, and what do they need to make sure they succeed?
When to Use Six Sigma
Given the similarities between continuous improvement methodologies, it can be difficult to determine which one is right for a given situation. To help organizations make that decision, the Six Sigma Council outlines the following scenarios, and the benefits of Six Sigma can bring to solving each one.
When facing the unknown – A process is operating out of control but the problem causing the deficient output is not known.
Six Sigma looks for potential causes and using sigma level calculations prioritizes them. It then sets up the framework to resolve the causes and get to a solution.
When problems are widespread and not defined – The problems in the process are known and understood, but the scope of the solution is not defined, leading to constant scope increases and lack of viable solutions due to their unmanageable size.
With control measures in its methodology, Six Sigma stays clear of unmanageable scope escalations in favor of incremental improvements over time.
When solving complex problems – A problem with many variables causes a complex process, where it’s challenging to identify an approach, definition, and measure for a successful outcome.
Due to its statistical basis, Six Sigma can handle problems that contain large amounts of data and variables, deciphering them to give hypotheses, premises, and conclusions to base changes on.
When costs are closely tied to processes – A process that has a high cost risk due to its very small margin of error – where one incremental change can translate to millions of dollars of loss or gain – requires solution accuracy before implementation.
Six Sigma leans on its statistical process control to create assumptions, therefore when implemented properly, this method is significantly more accurate than its alternatives.
Success in Six Sigma
The Six Sigma method is not without its challenges, of course.
To be successful, Six Sigma requires support – primarily in the form of resources and data – at all levels of an organization. Adequately staffed engagement teams with necessary levels of subject matter expertise are a must for positive results, as is access to consistent and accurate data streams to enable calibration factors and the capturing of necessary KPIs – crucial to data outputs value.
But ultimately, taking advantage of how customizable this approach is to fit your industry and organizational needs will be the key to successful process improvement.
Is It the Right Choice for You?
When starting on a process improvement initiative and considering the Six Sigma methodology, it is important to have all the information first. Knowing when this method is best applied can set you on the path to operational process perfection.
The Six Sigma methodology has been adopted by top operational excellence consulting firms, including Trindent Consulting. Click here to learn more about how we can work with you to utilize this valuable tool in driving the efficiency of your organization.