WHAT ARE THE MOST IMPORTANT KPIs (“KEY PERFORMANCE INDICATORS”) FOR SUPPLY CHAIN MANAGEMENT AN OPERATIONS?
In this post we will explain the most important metrics that should be defined, monitored and control for the supply chain management and operations business function.
Please note that operations management according to Krajewski, Ritzman, & Malhotra (2013) refers to “the systematic design, direction and control of processes that transform inputs into services and products for internal and external customers. Processes can be linked and synchronized together to form a Supply Chain”
Therefore operations and supply chain management comprises RAW MATERIALS ACQUISITION+PRODUCTION+LOGISTICS+DELIVERY->END CUSTOMER, and the most important goal of this business function would be that the customer has the product/service when requested and on-time.
There are many KPIs for the operations business function, but the most important ones are:
- Days of supply (D/S): It measures the stock level of a company as the total number of days the current stock level can meet the daily demand. If the company defines a company’s D/S goal of 10 days, all the organization should have a stock level being able to meet the demand for 10 days. A D/S level of 30 days it would mean that the company holds a stock level of 1 month of sales, that is no necessary and it has an implied cost (obsolescence, damage, logistics cost, price decrease, etc). Imagine that a company is able to manufacture any product in 5 days, and has a delivery time of the product to the end customer of 3 days. In addition it considers that there should be a security buffer for contingencies of 2 days. Taking into account all this factors, the D/S goal defined as a KPI to be monitor should be D/S=10 days. The next step would be to calculate in a daily, weekly or monthly basis de D/S per product, category, country and company’s overall. The calculation would be based on: D/S= Current stock level in units/Daily sales forecast. If the yearly sales forecast of expected number of units to be sold is 365,000 and the current stock level is 30,000 units, the D/S would be: 30,000 units/(365,000/365)=30 days, that would be above the D/S goal of 10 days. Please note if cost of manufacturing 1 product defined by COGs (see-5.) is a 20% of the retail selling price excluding VAT (€100), the manufacturing cost would be 20%*€100=€20 per manufactured unit. If the current stock level is 30,000 units, the cost of the stock (average method to account stock) would be 30,000*€2=€60,000.
- Dealer Hit Rate (“DHR”): It measures the percentage of products delivery on time to the end customer. Imagine that a customer (either B2C or B2B) has places an order to receive 1,000 TV sets on Dec, 1 and we only dispatch 300. The DHR would be 300/1,000=30%, that means our delivery service level is very poor, and a root cause analysis must be undertaken to understand the causes of this performance. If we measure the organization following a Six Sigma approach, the dealer heat rate would imply that for every million units dispatched, only 3.4 are not delivered on time, that is almost a DHR=100%.
- Backorder. It is the total number of units that should have been dispatched to the end customer but has not been delivered yet. Taking into account the amounts from 3, the backorder would be 1000-300=700 units requested by the customer that has not been delivered on time.
- Transit times and delivery times, that measures how effectively and fast we can deliver de product to the end customer. Let’s put an example, many times it is discussed that China is considered a good option for manufacturing of products compared to Europe or Eastern Europe. It is true that the manufacturing costs, mainly operators expenses, are lower than in Europe, but if we take into account transit times (from Europe to Europe could range 2-4 days versus 1 month freight shipments from China) and without taking into account the import duties that apply for product manufactured in China rather than Europe (no import duties).
- Total Landed Cost, or the total cost that the company bears to place the product at the customer. It takes into account raw materials costs, operators costs, machinery costs and delivery costs. This is a measure of how effective and cost-efficient is our supply chain business function. The manufacturing direct cost per product is called “COGs” (Cost of good Sold), being a KPI, as it defines how effective is the manufacturing process of a company in terms of cost.
- Forecast accuracy. This metric analyzes how good is the sales forecast versus the actual sales orders. The sales forecast accuracy is key for manufacturing and operations, as it is the driver that triggers all the production process from manufacturing sites at a current days of supply goal and stock level. Imagine a company that has shortage of raw materials for production, and the sales forecast accuracy is +-80%. This would mean if the sales forecast is 100,000 units, the placed orders could be in the range of 20,000 to 180,000 units. The impact on the manufacturing function is huge and could generate overstock or raw materials shortages with an impact in the dealer hit rate metric.
- Quality. This topic has a great impact and level of discussion. Quality from a total quality management approach (“TQM”) should be at the core of any organization, and should be proactively build within the organization at all levels (employees, processes, departments) trying to act before the error happens. There are many quality frameworks (TQM, EFQM, Six Sigma, etc) and Toyota is well known for these methodologies. If we take into account a quality level defined by Six Sigma as 3.4 errors per million, an organization with this level of errors would be at a six sigma quality level. Imagine that we accept a 95% quality level, this would mean for the aviation industry where there 100,000 flights per day with 2.5 million daily passengers (up to 37 million flights per year and 912 million passengers), that 5%*100,000 flight could be at a risk. It is clear that this level of quality would not be acceptable. Under a Six Sigma quality level, the total number of flights t a risk on a yearly basis would be 3.4*37=126 flights rather than 5%*37 millions= 1.85 millions of flights. Even a Six Sigma level for the avian industry would not be acceptable either.