Working capital improvement Working capital is money that’s ready to spend, not tied up in inventory, real estate, or other assets. Working capital analyt- ics places the focus on end-to-end supply chain inventory. For example, analytics might determine that a company has an inventory in excess of 15 percent, which could be liquidated (or just not replenished) to improve cash flow. Inventory analytics enables setting efficient inventory levels, determining how much
CHAPTER 3 Understanding the Basics of Metrics and KPIs 21
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stock is currently in the supply chain, identifying slow-moving or obsolete stock, and deciding where stock should best reside for optimal logistics.
To improve cashflow further, companies should be considering shortening daily sales outstanding (DSO) and reviewing payment terms with suppliers so monies owed are collected much sooner. The combination of improved inventory management, shorter DSO, and reduced payment terms help improve the overall work- ing capital available across the business.
Operating margin improvement The most common approach to improving your operating margin — the difference between your revenue and your costs — is through savings. Within the supply chain, that means know- ing where your key areas of cost occur. So a company looking to reduce its operating costs and improve supply chain efficiencies will look for where unnecessary expense is happening. This could be where orders are incorrect and need to be redelivered or where customers are rejecting invoices or delaying payments. Addition- ally, the goal is to minimize the cost of capital and right-size the inventory levels to balance between OOS and inventory hold- ing costs. To find out what’s happening and why, you need to combine analytics covering supply chain network performance, inventory analytics, and supply chain expenses.
Risk management Businesses ensure their continued survival by responsibly man- aging risks and that extends to the supply chain. Along with look- ing at KPIs, businesses today also frequently talk about key risk indicators (KRIs), which are risks they track and manage. Ana- lytics can identify operational, financial, and compliance risks within a business’s own supply chain operations as well as those of its trading partners.
Supply chain analytics is most effective when it starts by address- ing a real business objective. When you’re defining your metrics, always make sure that they’re fully aligned with your corporate goals.
22 Supply Chain Analytics For Dummies, OpenText Special Edition
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Two Strategic Considerations Defining the key strategic goal to achieve is a great first step in developing a company’s analytics initiative, but there are two important considerations. This section examines them.
Top down versus bottom up When planning supply chain analytics, starting at the top with corporate goals ensures that the analytics are aligned with the business’s overall needs. However, many organizations have built their analytics capabilities in the opposite direction. They started by addressing a specific operational problem, such as “How many invoices are we sending, and how quickly are they getting paid?” and then developed analytics to address the specific question. Figure 3-1 illustrates the different approaches.
As companies mature in their use of analytics, they tend to increasingly take a top-down approach. That doesn’t mean that the top-down approach is always the best one, however. The key is to find the correct balance for the situation and company. For example, a company might start by addressing ways to improve order cycle time and use that as the beginnings of a program to achieve better forecast accuracy.
Positive versus negative variance Supply chain analytics is frequently about managing the variance of a particular metric. A simple rule is that lower variance — the difference between the predicted and actual value of the
FIGURE 3-1: Top-down versus bottom-up analytics development.
CHAPTER 3 Understanding the Basics of Metrics and KPIs 23
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metric — indicates a well-managed enterprise based on the accuracy of their forecasting.
When a company optimizes a process to meet its business objec- tives, some metrics will be negative and some positive. Part of ana- lytics is ensuring that you find the most effective balance between them. The key is to understand the overall impact of both positive and negative variances and define the acceptable range of variance to optimize your business operations as shown in Figure 3-2.
Don’t treat variances the same across all metrics. That’s like pre- scribing a single medicine for all patients without individual eval- uations. If you do focus closely on individual metrics, make sure they’re of real value to the bottom line.
For example, imagine you’re an automotive supplier using Just- in-Time (JIT) manufacturing, a system where materials or com- ponents are delivered immediately just as they’re required in order to minimize inventory costs. Your customers place small, frequent orders, so you’re constantly adjusting your forecast to anticipate customer demand. When they demand more than expected, that’s a positive variance and represents an opportunity for additional revenue. When the demand is lower, it’s a nega- tive variance and represents a liability of additional inventory and holding costs. However, the supplier needs to understand the acceptable range for both variance types to manage its financial risk and maximizing its commercial opportunities. Analytics is critical to understanding these values.
Even where you’re not trying to balance positive and negative variance, you should clearly define the relationships between metrics. That’s where the power lies. One small change in every metric can add up to a massive improvement across the business.
FIGURE 3-2: Defining acceptable ranges of positive and negative variance within metrics to optimize business processes.
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Maturity Models, Reference Models, and Benchmarking
What are the metrics for evaluating an analytics solution? (In other words, how do you know if it’s working?) This section gives you a few basic ways to measure the success of your analytics program.
Maturity models When planning a supply chain analytics strategy, a good place to start is to determine the maturity level of your current activities. Luckily, a number of services are available to help. Many organi- zations, like analytics training company TDWI (www.tdwi.org), have developed Analytics Maturity Models. These models help an organization understand the phases of maturity in analytics, interpret assessment scores, and provide best practices to move forward. Its customized strategies and actionable recommenda- tions can help quickly advance a company’s analytics initiatives to gain more value.
Don’t use maturity modeling as a one-off exercise. Keep coming back to it and assess how you’re improving. By combining this with regular benchmarking, you can establish a framework for continuous improvement.
Reference models A reference model provides a set of management tools and best practice approaches for a specific business activity. Within the supply chain, the reference model most often applied is the Supply Chain Operations Reference (SCOR) model.
SCOR was developed in 1996 by the management consulting firm PRTM, now part of PricewaterhouseCoopers LLP (PWC) and AMR Research, now part of Gartner and endorsed by the Supply Chain Council, now part of APICS, as the cross-industry de facto stan- dard, strategy, performance management, and process improve- ment diagnostic tool for supply chain management.
