by David Jacoby
The top-level metric: economic value added
Economic value added (EVA) is an all-inclusive metric for measuring the impact of SCM performance improvement since it captures revenue, cost and asset effects.4 When SCM generates incremental revenue (for example, through lower pricing or rapid new product introductions) EVA is the profit after tax less the true cost of capital employed. It is calculated as:
EVA = net operating profit after taxes (NOPAT) – (capital × cost of capital)
Martin Christopher5 proposes a variant on EVA that accumulates the value of EVA into the future and discounts it back, as in a net present value (NPV) calculation, where market value added (MVA) = NPV (EVA)). Another way of calculating MVA is akin to measuring the company’s market premium over book value (stock price × issued shares – book value of total capital invested). EVA may be approximated by after tax free cash flow, which is net operating income less tax less working capital investment and fixed capital investment. This, however, is harder to measure at the level of the supply chain, so figuring out the supply chain’s impact on after-tax free cash flow is quite tricky.
Primary (CFO-oriented) results metrics
At the highest level, each supply chain strategy delivers certain financial benefits that can be measured, as shown in Figure 12.1 overleaf.
Rationalisation efforts have been demonstrated to result in a 1–4% improvement in the net margin (for example, going from a baseline of 10% to 11–14%), which corresponds to a 4–6% improvement in EVA. (Note that all correspondences of performance to EVA results are based on a company with $1 billion in revenue and a multi-industry average rate of asset intensity and profitability.) Supply chain initiatives in the rationalisation phase focus on reducing supply chain cost, which in turn increases the net margin.
Synchronisation efforts have shown also to have a 1–4% improvement on return on net assets (RONA) – for example, the capability to move from 7% RONA to 8–11% RONA – which equates to a 5–7% improvement in EVA. Supply chain initiatives in the synchronisation phase typically achieve the improvement in RONA by reducing forecast error, and thus achieve level production both within the enterprise and across trading partners, which decreases the need for inventory and fixed assets.
Customisation can lead to 5–7% higher gross margin (for example, the potential to increase from 32% to 37–39%), which translates to a 6–10% improvement in EVA. The principal catalyst of higher gross margin in the customisation stage is greater customer mindshare, which is achieved by increasing customisation at customer touch-points.
Innovation can lead to 10% incremental revenue growth (for example, moving from 13% growth rate to 23% growth rate), equivalent to about 15% or greater increase in EVA. The revenue growth comes mainly from new product introductions, and a key subsidiary metric of revenue growth related to innovation is the percentage of sales derived from new products over the previous 12 months.
Subsidiary results metrics
Table 12.1 on page 194 lists the key metrics that are most appropriate for each supply chain strategy. It is divided into three categories of metrics: primary results, subsidiary results and top-level process. This organisation facilitates the selection of the appropriate metrics depending on the purpose.
Figure 12.1 Supply chain balanced scorecard
Source: Author. Compiled from experience and sources such as Bowersox (Logistics and Supply Chain Management), Carroll (Lean Performance ERP Project Management), Christopher (Logistics and Supply Chain Management), Cohen and Roussel (Strategic Supply Chain Management), Frazelle (World-Class Warehousing presentation), Hugos (Essentials of Supply Chain Management), Lambert (Strategic Logistics Management), Mentzer (Handbook of Global Supply Chain Management), Monczka (Purchasing and Supply Chain Management, p. 202), Poirier (Business Process Management, Advanced Supply Chain Management, pp. 124, 146), Rudzki (Straight to the Bottom Line), Woods (Supply Chain Yearbook)
Rationalisation subsidiary results metrics
At the next level down, functional executives need to track the components of net profit margin in order to ensure that operational changes end up in financial results. The principal components that should be tracked at this level are:
total supply chain management costs;
cost of goods sold (COGS);
cost per unit;
customer segment profitability;
direct labour;
direct product profitability;
inbound freight;
order fulfilment costs;
order fulfilment lead times;
outbound freight;
overhead cost;
total landed cost;
total product cost.
Synchronisation subsidiary results metrics
Companies following the synchronisation strategy will find that one of two sets of subsidiary metrics will be appropriate.
If the company’s supply chain has a high turnover of goods and/or services, it should measure working capital:
cash flow to sales;
finished goods inventory carrying cost;
inventory carrying cost;
inventory value.
If the company is asset-intensive, it should measure the asset performance of its fixed assets, for example:
asset turns;
return on capital employed (ROCE);
asset utilisation;
capital productivity;
return on investment.
Customisation subsidiary results metrics
The most effective subsidiary results metrics for the customisation strategy are:
overall customer satisfaction;
customer complaints.
Innovation subsidiary results metrics
Innovation through SCM is best measured by the share of customer’s attention that the brand receives, which can be measured directly or inferred from repeat purchases and customer penetration rates (up-selling, cross-selling, etc). A short list of level 2 metrics for innovation-driven SCM should include:
percentage of total sales from products introduced in last 12 months;
percentage of total SKUs introduced in last 12 months;
customer share;
cycle time for new product development and delivery;
decrease in cycle time for new product design and development.
The next level down: key process metrics
Below the top level there are hundreds of additional metrics that can be used to track the effectiveness of SCM processes and sub-processes. Since each company is extremely different at the operational level, the selection of metrics below needs to be evaluated on a company-specific basis.
Rationalisation process metrics
The success of rationalisation strategies can be affected by metrics such as:
percentage of spend sourced in last two years;
percentage of spend outsourced;
percentage of SKUs value-engineered;
decrease in number of parts per unit;
cost per delivery;
visibility to end-customer;
cost per engineering change order (ECO);
number of Six Sigma black belts6 at core suppliers;
percentage of materials on consignment;
percentage of excess cost designed in;
percentage of transactions paperless.
Synchronisation process metrics
The success of synchronisation strategies is affected by two process metrics related to SCM’S struggle against the bullwhip effect: first, the variability of order volume over time and through the supply chain; and second, the assets needed to support that variability.
The variability of order volume over time measures the extent to which supply chain partners fail to co-ordinate, resulting in peaks and valleys of capacity and prices over time. The variability of order volume through the supply chain – for example, the standard deviation of order volume at the finished goods level, divided by the standard deviation of the volume of raw material purc
hases – measures the counterproductive reverberation of the overcorrection on an order or shipment basis. Many people measure forecast error as a proxy for this since it measures the extent to which the bullwhip effect has been avoided.
Measuring the variability of order volume over time (cyclicality) is like measuring the business cycle. It can only be done over a long time horizon, so it tends to be measured mostly by large companies that are in cyclical industries and are at the raw material source, so get the worst of the bullwhip effect, being at the tip of the whip.
Recent economic patterns and globalising markets have demonstrated the propensity for overcorrection. Therefore, long-term decisions that involve substantial investment and risk often hinge on an accurate forecast of industry conditions. Which markets to buy from, which suppliers have capacity, what is the right price to pay and whether to partner, enter into a joint venture or acquire are questions that all depend on a reasoned and accurate view of future conditions (see Figure 12.2 overleaf).
Figure 12.2 Representative forecast of cyclical prices
Source: Boston Strategies International
Saudi Aramco measures the variability of order volume over time through a sophisticated supply market intelligence initiative. For 50 categories of purchased materials and services, it estimates the future demand, order lead times, capacity utilisation and prices quarter by quarter, with forecasts three years forward. It tracks prices and projections to determine the peak of the cycle and proactively works with suppliers to avoid shortages and price spikes.
Companies that measure long-term cyclical trends and forecasts in an attempt to mitigate the costs of a bullwhip effect establish market intelligence and risk management at three levels: supplier, supply market and macroeconomic. At the supplier level, they should monitor supplier ratings and qualifications, performance history, financial results and news. At the industry level, they should monitor capacity utilisations, lead times, costs, prices, regulatory changes and productivity. At the macroeconomic level, they should monitor demand, growth rates, regional conditions, risk factors and prices for underlying commodities.
Other tier 3 synchronisation metrics include: percentage production lines on JIT; frequency of S&OP meetings; number of black-belts on staff; frequency of sharing demand forecasts with suppliers; time since constraints review; percentage SKUs on ELDP; percentage market share of end customer; percentage perfect orders; percentage peak; percentage transactions via EDI or XML; standard deviation of delivery time; mean standard error; and percentage direct ship.
For simplicity, many companies measure demand forecast accuracy, or actual versus forecast sales. Forecast error, if converted to a supply chain results metric, would equate to the reduction in inventory or working capital, which contributes about 5–7% to EVA, as previously mentioned.
Customisation process metrics
While the success of the customisation strategy is ultimately measured in gross profit margin, many processes need to be aligned in order to yield the financial results. Here is a sampling of some process metrics that would provide a snapshot of the health and adequacy of customisation supply chain processes:
on-time delivery performance to customer request time;
on-time delivery performance to the time that the sales representative promised the customer;
percentage of transactions where up-sale offered;
percentage of transactions where the sales representative offers to sell products or services of a type that the customer is not currently buying (cross-sale);
percentage of SKUs that have been reviewed using the “house of quality” approach (see page 132 and Figure 8.1);
percentage of interaction history accessible on demand;
percentage of transactions using customer data;
percentage of customers segmented;
percentage of customers with known profitability;
percentage of prices that are dynamic;
percentage of orders via formal profitability assessment;
percentage of product line customised;
percentage of product line personalised;
customer satisfaction;
percentage of transactions for which available-to-promise (ATP) used;
time required to increase volume by 20% in response to a customer’s request or surge in demand;
complete manufacture to order ready for shipment time;
degree of demonstrated flexibility;
response accuracy;
response time to enquiries.
Innovation process metrics
Generating revenue, the level 1 innovation metric depends on a range of sub-processes coming together. Specific supply chain processes that need to be fully functional in order to generate innovation include the following, which should be measured:
percentage of sales from new products;
percentage of SKUs new in last 12 months;
time to feed back test market sales information;
first prototype stage as percentage of EVA;
percentage overlap between design and engineering;
stage of first supplier involvement;
stage of first customer involvement;
total marketing cost;
response time before/after the postponement point;
percentage of SKUs via assortment planning;
percentage of SKUs designed with supply chain involvement;
customer share;
new production introduction (NPI) cycle time;
new product development (NPD) cycle time;
decrease in NPI cycle time;
decrease in NPD cycle time.
Additional detailed process metrics
Companies use close to 700 additional process metrics to measure supply chain success.
More than 200 tier 2 metrics are more detailed versions of the primary results metrics (one degree of separation from the original metrics). An example is the cost per mile of trucking operations. This can be linked to corporate performance via the cost per delivery, which in turn affects the total supply chain cost, which in turn affects the net profit margin. Assuming that the company’s supply chain strategy is at least partly focused on rationalisation, this level 2 metric makes sense.
Nearly 500 highly operational level 3 metrics are at least two degrees of separation from the top-level metrics. Some of these are extremely helpful in achieving incremental improvements in cost, flexibility, cycle time or customer service. For example, back orders tie to fill rates, and fill rates tie to customer satisfaction, and customer satisfaction ties to pricing and margin strength. So if the company is following a customisation strategy, it makes sense to track back orders, even though the relationship to corporate financial performance is indirect.
The risk with allowing tier 3 metrics to proliferate is that the logical and quantitative link to high-level value-creating metrics used at the c-level can become obscure. One of the principal responsibilities of a supply chain leader is to champion a select number of metrics that logically and intuitively connect SCM to supply chain strategy, and supply chain strategy to corporate value.
A metrics maturity model
Stage 1 companies without a supply chain focus usually have a hard time collecting the data to compile the metrics. As a result, when they need to cut costs, they do so by executive mandate. There are wide inventory variances as a result of not only the bullwhip effect, but also shrinkage (theft, damage and obsolescence). Returns are handled chaotically, customer service provides one uniform response to all customers, and each new product introduction (NPI) shakes the company. The chief procurement officer at a UK-based mining company expressed dismay at the quality of the master data on external expenditures. Even after years of centralised management of the procurement function, poor data quality was still an impediment to implementing supply chain programmes. Moreover, budgets and estimated savings did not meet expectations, and the combination of bad baseline data and erroneous and unaudited pro
jections created a culture that defied accountability. An Economist Intelligence Unit study reports:7
Fully 31% of respondents say getting accurate and timely spend data is a top challenge to achieving overall success in purchasing strategies and initiatives. An inability to measure company-wide expenses accurately means “indirect [spend] for BAT until very recently has been ostensibly a virgin area, significantly larger than direct spend, and is spread across disparate profit centres, without good spend visibility or strategic sourcing,” says Andrew Brock of BAT in South Korea.
As companies approach stage 2, they often experience a decrease in profitability or a loss, and hire a supply chain professional to institute processes and detailed metrics that help to contain costs. A US pharmaceutical company launched an SCM initiative to align its cost base when its stock price performance slipped behind that of its principal rivals. This usually results in a programmed cost reduction, the establishment of commodity management teams and the implementation of information systems that automate order entry (as in e-procurement systems, MRP and better materials control systems to track and manage SKUs through their life-cycles. Still, at this stage they usually measure cost on a first-cost basis (as opposed to life-cycle costs) and they focus on getting high equipment or plant utilisation (as opposed to balancing loads in lean style).
Stage 3 is often brought on by volume growth or new shareholder-oriented management. IBM significantly beefed up its internal SCM by buying PricewaterhouseCoopers Consulting in 2002 as it faced new market realities in the PC market. Typical steps in stage 3 include instituting externally (supplier and customer) focused metrics and processes that support collaborating with suppliers and customers on demand planning, negotiating with suppliers’ suppliers (tier-skipping) and structuring performance-based agreements that favour customers’ needs over internal needs.