Is your consultant worth the price tag?

For as long as human beings have conducted business, they have worried about the gap between theory and practice. This age-old problem is still a concern today, especially for a relatively young discipline like supply chain management (SCM), where theoretical concepts have gained widespread acceptance but implementation of efficient and effective SCM practices is still rather limited.

One possible reason for the slow pace of implementation is that transforming theoretical concepts into actual business practice is extremely challenging. It is therefore not surprising that so many companies today are seeking help with SCM implementations from consulting firms.

Article Figures

[Figure 1] Example company's supply chain scorecardEnlarge this image

[Figure2] Impacts on cash flowEnlarge this image

[Figure 3] Formula for net present valueEnlarge this image

[Figure 4] Relevant figures for example companyEnlarge this image

[Figure 5] Delta cash flows in future yearsEnlarge this image

[Figure 6] Net present value in future yearsEnlarge this image

Before they hire a consultant, however, decision makers want to know that at some point in the future they will realize tangible benefits from that investment. Many companies use an estimate of logistical improvements to justify SCM consulting projects, but that is not enough. What they need instead are measures that will show the financial justification for such undertakings.

In this article, we propose a methodology for measuring a project's expected financial benefits by converting anticipated improvements in delivery reliability, responsiveness, and inventory into cash flows. To illustrate it, we use the example of a German automotive supplier that employed this methodology to justify the cost of a consulting project, including implementation and software licensing fees.

Performance measurements
If a company is to determine the extent of the supply chain improvements it can expect from a project, it has to measure current performance and establish a benchmark before any work begins. This step can be difficult to accomplish because supply chain performance measurement is still in its infancy.¹ One reason for this circumstance is that there still is no universally accepted definition of "supply chain management."² Another reason is the complexity of supply chains. It may be relatively easy to define and measure internal processes for a single company, but processes in huge, multicompany networks are hard to identify, and thus it is nearly impossible to properly measure efficiency and effectiveness.

Because there is no general agreement on what constitutes the "best" supply chain performance, we have chosen to measure performance based on two objectives: (1) improvement in customer satisfaction and (2) inventory reduction. There are many other criteria for measuring supply chain performance, of course, but these two are among the most important. By focusing on them, companies can achieve cost reductions, market-share growth, and overall improvement in financial performance.

Companies can chart their progress toward those objectives by using a "supply chain scorecard" with key performance indicators (KPIs), a method that is based on the balanced scorecard concept developed by Kaplan and Norton.³ When using such a scorecard, it is important to select KPIs that will represent the performance of the entire supply chain. Each KPI must also be precisely defined so that the results of different consulting projects can be accurately compared.

To measure the degree of customer satisfaction, we will use two key performance indicators: reliability and responsiveness. Reliability is defined as the percentage of orders that are filled with no exceptions while meeting predetermined criteria for delivery of a complete order. These criteria might include order completeness (shipping the right amount); on-time delivery; or absence of damage to the delivered product, among others. A proper analysis of reliability requires knowledge of the performance standards that are promised to customers. Responsiveness represents the percentage of order requests that can be filled using available capacity and resources.

The level of inventory held by a company—our second objective—is a crucial indicator of tied-up capital. Inventory levels are to a great extent determined by performance in three areas: the accuracy of sales forecasts, the accuracy of material planning, and total order lead time. (Other influencing factors, such as the type of manufacturing process, speed to market, and so forth, will not be specifically considered in this article.)

Sales forecast accuracy, the first KPI we will use to analyze performance relative to inventory, measures how far the predicted demand for finished products deviates from the actual demand during a specified time period. Sales forecast accuracy affects inventory levels because companies typically hold safety stocks of finished products in their warehouses to respond to the uncertainty associated with such deviations.

The second KPI, material planning accuracy, measures the accuracy of demand forecasts for production inputs. Inaccuracy in material planning also forces companies to increase safety stock.

Finally, order lead time measures how long it takes to fulfill an order, from the time the order is received until the time it arrives at the customer's premises. This period includes time for manufacturing, waiting, and stocking. The longer the lead time for a single order, the greater the number of orders in the supply chain pipeline, that is, the greater the work in process (WIP). The lead time is especially important for products with very short product lifecycles, as they are at risk of becoming obsolete before they are sold.

Determining project targets
Figure 1 represents a sample supply chain performance scorecard for a German automotive supplier, which plans to hire a consulting firm to help it improve its supply chain performance. The table displays the current status before the project (the column marked "Current"); the best-in-class benchmark ("BM"), based on the equivalent KPIs of other companies from the same industry; the performance level the project aims to achieve ("Target"); and the difference between the current and targeted performance levels ("Delta").

If the current performance of a KPI significantly deviates from the best-in-class benchmark, it signals that there is potential to improve in that area. The company might set an optimistic goal and strive to achieve the best-in-class benchmark. But it often is unrealistic to expect a single project to achieve bestin-class performance, and in this example the consultants set lower targets based on their experience with comparable projects. The difference between Current and Target (shown in the Delta column) represents what they believe is the achievable level of improvement.

The next step for the automotive supplier is to convert the estimated supply chain improvements to financial performance measures. This conversion will allow the company to evaluate the expected benefits of the consulting project.

After a review of numerous financial performance measures, net present value (NPV) was selected as an appropriate measurement for this analysis. NPV has several advantages compared to other performance measures like the trademarked economic value added (EVA) or return on investment (ROI). Because it is based on actual cash flows, NPV eliminates the possibility of manipulating by modifying depreciation or reserves in the balance sheet. Cash flows can simply be determined by measuring receivables versus payables over a specified period of time, usually years. Net present value also works better than Payback time, a measure sometimes applied by consultants. Payback time is not always an accurate measure of success because it often does not consider the interest rates companies will have to (virtually) pay on their future returns. Since cash today is worth more than cash in the future, consultants (intentionally or not) may—by neglecting the interest rates—make projects seem more successful than they actually are.

Impacts on company cash flows
NPV is an appropriate financial measurement because poor operational performance can have a huge impact on cash flows. For instance, if a company does not meet its promised level of order reliability, customers might choose to do business elsewhere, and payments from these customers would not continue in the future. By improving reliability, therefore, a company can avoid future loss of revenue, thereby generating a positive delta cash flow (the additional revenue that can be attributed to improvements made during the project).

Responsiveness also has an impact on a company's finances. If responsiveness falls below 100 percent because a company lacks the capacity to handle orders in accordance with its guaranteed level of service, then revenues will decline. Thus, when responsiveness can be improved, cash flows will increase commensurately.

Improvements in the other three KPIs of the supply chain scorecard can lead to additional cash flows by reducing inventory levels and freeing tied-up capital. For example, improving the accuracy of both sales forecasts and material planning reduces the risk of deviating from actual demand, and therefore the amount of safety stock can be reduced. By lowering the total inventory level, moreover, interest payments on capital tied up in inventory also can be reduced.

Any reduction in order lead time has the positive effect of reducing the total number of orders in the manufacturing pipeline. Overall WIP decreases, and therefore the total level of inventory also shrinks. Again, less capital tied up in inventory results in lower interest payments and ultimately in a positive delta cash flow. (See Figure 2.)

To determine the actual effect the consulting project will have on financial performance, the estimated additional cash flows (CF_t) must be summed up per future period (t = 1, 2, … n), discounted by the future interest rate (i) to the point of decision at the beginning of the project (t = 0), and charged against the initial payment (I₀) to the consulting firm. (See Figure 3.) As stated earlier, cash today is worth more than cash in the future.

The primary purpose of the discounting factor (i, representing the interest rate) is to make all cash flows comparable, regardless of when (that is, in which year) they occur. The value of i depends on the relevant customer's interest rate. The NPV that has been calculated at this point in the analysis shows whether (and when) the project will provide financial benefits.

A practical test of the concept
The previously mentioned consulting project under consideration by an international automotive supplier provided an opportunity to test this methodology for practical applicability. In their proposal, the consultants suggested that the automotive supplier should implement SAP's Advanced Planner and Optimizer (SAP APO) supply chain management software. SAP APO, which belongs to the category of advanced planning systems (APS), offers its users several advantages, including a clearer overview of worldwide locations and facilities.⁴ The company was already using SAP R/3 as the primary framework for its information technology system.

On average, this car-parts supplier generates sales of 200 million euros per year from 40,000 orders. It handles as many as 300,000 individual products that are so different in characteristics and price that one cannot say the supplier has a "main product." To generate the 200 million euros in sales, the company uses 110 million euros' worth of material. It also spends 75 million euros on labor and machinery. In total, the company has an input-output ratio of 92.5 percent, as shown in Figure 4.

To begin the analysis, the company used the supply chain scorecard described above to establish current performance benchmarks. Then it set a realistic target for each KPI for the consulting project and estimated the potential improvement. (The actual figures are shown in Figure 1.)

A pre-assignment analysis conducted by the consultants fixed the company's current reliability rate at 95 percent, with 5 percent of orders insufficiently completed. These failures were primarily related to late delivery to customers. (This was significant because experience had shown that customers that were served late even once would not order from the company in later years. Instead, they ordered products that are comparable in quality and price from a direct competitor.) To prevent late deliveries, the consultants recommended the SAP APO module Global Available to Promise (GATP). With this software, the user can conduct a global analysis of product availability before accepting an order and thus ensure that promised delivery times would be realistic. GATP also reserves any required components and capacities and allocates them to a specific customer order. The software thus could prevent the company from promising the same inventory to multiple customers, one of the reasons for late delivery. The consultants estimated that implementing GATP could increase the company's reliability rate from 95 to 96 percent.

Meanwhile, the scorecard analysis found that the automotive supplier had a responsiveness rate of 97 percent, meaning that 3 percent of its order requests had to be rejected because of insufficient capacity in its supply chain. GATP could help there, too, because it continuously checks current inventory levels, thereby allowing the user to determine whether an order request could be satisfied with an alternative product. The consultants also suggested using the APO module Demand Planning (DP) to enhance responsiveness. DP enables the combination of several data sources within the company. Additionally, the software accepts data from external sources and uses them to carry out automatic demand forecasting. Although best-in-class suppliers have a responsiveness rate of 99 percent, the consultants estimated that this supplier could only achieve a rate of 98 percent.

In addition, because the existing level of sales forecast accuracy was just 88 percent, the company had to hold safety stocks of finished goods in excess of 12 percent of expected customer demand. The consultants projected that combining DP with the supplier's automatic forecasting methods could improve forecast accuracy to 90 percent.

Material planning accuracy was only 90 percent, and the resulting uncertainty forced the company to maintain safety stock equivalent to 10 percent of the estimated demand for manufacturing inputs. The consultants determined that using the APO modules Supply Network Planning (SNP), Production Planning (PP), and Detailed Scheduling (DS) could bring the automotive supplier's material planning accuracy up to 92 percent.

The SNP module lets users develop a dynamic, companywide demand forecast by providing access to all relevant data sources throughout the supply chain, starting with the procurement/purchasing stage and continuing through manufacturing and distribution. This type of demand forecasting exceeds what usually is possible with a basic enterprise resource planning (ERP) system. The PP module offers short-term material and manufacturing planning that takes all capacity restrictions into consideration, and the DS module is used for optimizing resources and order-sequence planning.

The automotive supplier had an average order lead time of three days; that is, the average order takes three days from the time the order is placed to get to the customer. To handle 40,000 orders in one year takes the time equivalent of 120,000 days. Therefore, if we assume 365 working days per year, then an average WIP of 329 orders per day (120,000/365) has to be taken into consideration. Using the PP and DS modules, the company could improve its production layout and order-sequence planning, and thereby reduce the lead time to 2.5 days. This would result in a reduction of the overall WIP from 329 orders to 274 orders per day. The consultants found that this improvement could be achieved through better monitoring of manufacturing processes and earlier identification of bottleneck situations. Moreover, waiting time in production could be shortened through the optimal allocation of orders to machinery. With the assistance of the order-sequence planning in the DS module, the production setup times for single machines could also be reduced.

Quantification of predicted improvements
As a result of increasing reliability by 1 percentage point to 96 percent in future years, 38,400 orders (96 percent of the 40,000 annual total) could be filled and shipped in compliance with customers' requirements. The current sum of 2,000 insufficiently met orders (worth 10 million euros in sales) would therefore be reduced by 400 orders (2 million euros in sales). These future orders, which would have been lost if no improvements were made, will instead be retained. And because the company's input-output ratio is 92.5 percent, this additional sales volume would result in additional cash flows of 150,000 euros. The typical time lag for such improvements to take effect is one year, thus the delta cash flow must be considered from the second year on.

By improving the supplier's responsiveness rate from 97 percent to 98 percent, sales would rise within a year by 2.06 million euros, to 202 million euros. The cost of manufacturing and preparing the associated additional products for sale would be 1.91 million euros. The additional cash flow per year would be 154,639 euros.

Assuming a monthly demand forecast and a forecast accuracy of 88 percent, the company would have to hold 3,000 units of finished goods as safety stock. In future years, the supplier could reduce this stock level by 500, bringing it down to 2,500. With average production costs of 616.67 euros per product, this would lead to an estimated reduction in the total inventory value of 308,333 euros. If we assume a projected interest rate for the cost of capital of 7 percent, this would equal a potential saving of 21,583 euros per year.

Given the current material planning accuracy of 90 percent, safety stock for input material (raw material and half-finished products) with a total value of 916,667 euros would be needed to ensure continuous production with no supply bottlenecks. This value equals 10 percent of monthly demand. The anticipated improvement in material planning accuracy would reduce the need for safety stock, and the value of input safety stock would decline by 183,333 euros to 733,333 euros. Taking into consideration the company's expected interest rate of 7 percent on capital, this improvement would lead to savings—and therefore positive delta cash flow—of 12,833 euros per year.

As noted, by reducing the overall order lead time, the consulting project could reduce the average WIP from 329 to 274. Currently, orders with a total value of 1,520,548 euros are tied up in WIP, but after the optimization, this would be reduced to 1,267,123 euros. Again, taking into account an assumed interest rate of 7 percent, this reduction would result in savings of 17,740 euros per year.

The consultants estimated the financial impact of the software implementation and any further innovations over three years, as shown in Figure 5. This period was chosen in part because the software's SAP APO tool requires an update after three years.

To determine the overall financial impact of the consulting project, the automotive supplier must discount these amounts by the company-specific project interest rate of 10.5 percent (which includes a premium of 3.5 percent for projects over the basic interest rate of 7 percent), and then add up the estimated delta cash flows that would result from the improvements in the supply chain KPIs. The projected payments to the consulting firm of 500,000 euros cover all consulting services, as well as all necessary fees for software licensing for a period of three years.

Simply for the sake of illustrating the methodology, let us assume the payments would be made at the beginning of the project (t = 0 in Figure 5). The calculation is made using the net present value formula shown in Figure 3, with the company-specific project interest rate of 10.5 percent. Accordingly, the calculation for Year 1 would be:

and for Year 2, it would be:

Figure 6 shows that the NPV achieves a positive value in Year 3:

Using this methodology, managers at the car-parts company can see—before they agree to the project— that in the third year the software implementation and consulting project will improve the company's finances and justify the cost of the consulting services.

Calculating future benefits
The methodology outlined in this article gives companies the ability to forecast the impact of a supply chain consulting project on their financial performance prior to starting any work. Although our example involved a software implementation, the methodology is applicable to other kinds of SCM projects. Because it focuses only on relevant KPIs, it provides a quick and cost-effective method for assessing the financial benefit of a wide range of projects with comparatively little time or human resources required.

Companies using this method can also compare the actual results against those forecasted by the consultants for the supply chain KPIs. Although engaging consultants for any project still involves elements of both risk and trust, companies can at least minimize their risk by undertaking this analysis to gauge the financial benefit before starting a project.

Endnotes:

1. James S. Keebler, "Measuring Performance in the Supply Chain," in Supply Chain Management, ed. John T. Mentzer, 411?435 (Thousand Oaks, California: Sage Publications, 2001).

2. Paul D. Larson, Richard F. Poist, and Árni Halldórsson, "Perspectives on Logistics vs. SCM: A Survey of SCM Professionals," Journal of Business Logistics, Vol. 28, No. 1 (2007): 1?24.

3. Robert S. Kaplan and David P. Norton, "The Balanced Scorecard—Measures that Drive Performance," Harvard Business Review, Vol. 70, No. 1 (1992): 71?79.

4. Gerhard Knolmayer, Peter Mertens, and Alexander Zeier, Supply Chain Management Based on SAP Systems, (Berlin: Springer, 2002).