In the previous article, we covered the common symptoms of supply chain troubles. We would encourage you to go back to the article and run through the checklist yourself, if you have not done so already.
We also started talking about gathering the data you will need to assess the size of the undesirable effects (UDEs) you are presently experiencing. First, we encouraged you to take a look at your Throughput. We described the calculation, but here it is again in a visual format:
We ended the previous article in this series with a valuable KPI we designated IDDD (Inventory-Dollar-Days-Delayed). This was a single KPI that included both the Throughput value of delayed shipments (read: cash-flow) and number of days shipments were being delayed.
But out-of-stocks and delayed shipments are usually only about half the problem for most participants in supply chains. They are frequently facing equally vexing troubles with too much inventory of the wrong things. We need a metric for that, too. A way to help us rationally look at the size of that problem.
For this, we recommend IDDOH (Inventory-Dollar-Days-On-Hand).
Using calculations shown in the spreadsheet below, supply chain managers and executives have data that is more valuable that just inventory turnover rates, for example.
It is one thing to know that you have more than 300 days inventory on-hand for an item that costs you a fraction of a cent and is purchased 100,000 at a time. It is quite another matter to have more than 300 days inventory on-hand for an item where, perhaps, tens of thousands of dollars of cash are needlessly tied up in aging inventory.
By using a spreadsheet similar to the one shown above, it readily becomes obvious that management priority should be given to those items with the largest values in column ‘F’, Inventory-Dollar-Days.
Not all products (SKUs) are equal. Sales of some products result in greater Throughput for your business (where we define Throughput as Revenues less Truly Variable Costs [TVCs]). And, since it should be your goal to maximize Throughput while minimizing Operating Expenses, we suggest Throughput Productivity be measured according to the following formula:
Throughput Productivity = Throughput / Operating Expenses
The Throughput Productivity KPI can be based on an operating period. For example, you can compare last year’s Throughput Productivity to this year’s results. Management might also find it valuable to compare last year’s average Throughput Productivity to last month’s Throughput Productivity ratio in order to measure ongoing improvement. Monthly Throughput Productivity might be graphed to show gains made over time, as well.
Again, since Throughput is not equal for all SKUs, and since another goal should be to maximize Throughput while minimizing your firm’s investment in inventory, we find the Throughput Turnover Ratio to be a valuable KPI. The Throughput Turnover Ratio is calculated as:
Throughput Turnover = Throughput / Average Inventory
By the way, a simple way to get to “average inventory” for a period is to take the beginning inventory value, add the ending inventory value, and divide the total by two.
Tracking your firm’s month-to-month Throughput Turnover Ratio is another great way to chart your firm’s progress in supply chain management improvement over time.
The total replenishment lead-time for any given SKU consists of three components:
We call the sum of these three component lead times the ToC Replenishment Time (TRT), where ‘ToC’ stands for ‘Theory of Constraints.’
Our recommendation is that you calculate or, at the very least, come up with good estimates of these three lead times for your SKUs. In many cases, these can be grouped by purchase product line to get to pretty good numbers.
Now is a good time to gather everything that you and your management team have calculated into one place and take a good long look at it before we move on with next steps. Here is what you should have at your fingertips now:
* Note: We use SKU-Locations, or SKULs, here because different locations (e.g., warehouses or stores) may be replenished from different sources and have differing average daily demand and ordering policies. As a result, any or all of these parameters may be different by location.
In our next article, we will talk about taking action based on what you have learned from these data-gathering and calculation exercises.
In the meantime, we would like to hear from you. Please feel free to leave your comments here, or feel free to contact us directly, if you wish.