Developing Risk Management Maturity

How can supply chain leaders develop a sense of where their firm is with respect to risk management and develop a realistic plan based on where they would like to be? Start by self-assessing where your firm is for each supply chain segment. The Digital Supply Chain Institute’s risk maturity framework allows leaders to rate themselves against a three-level metric (Digital Supply Chain Institute [DSCI], 2017). Level one is largely reactive risk management, that is, the firm usually waits until there is some evidence of imminent risk before taking action. Level two sees the firm taking steps to understand risks in segments and to develop mitigation plans. Level three is highly data driven and features the firm demonstrating the capability of risk prediction and reduction using integrated information, tools, and processes. As the assessment tool describes this level, the firm uses data analytics to seek patterns in supply chain risks and related negative impacts and adjust our risk management program to reduce or prevent recurrence. The DSCI (2017) maturity assessment describes the following best practices for supply chain leaders managing risks:

  • • Use data to reduce business performance and compliance risk through predictive analytics
  • • Place special focus on cybersecurity and data protection
  • • Strategically segment risks focusing on what to manage and where to selectively excel to gain a competitive advantage

Being Selective in Managing Supply Chain Risks

With the growing number of risks facing firms, it is likely beyond reach to have extensive plans for managing the majority of them (Lu et al., 2019). The first step, as mentioned, is to identify the critical risks that are likely to occur for a given supply chain segment. Then the issue becomes selecting where to focus. Firms could focus on the critical high-risk areas or choose to become excellent in a select number of them. Risk management practice has often promoted the idea that firms may be able to turn certain types of risks into a source of competitive advantage. Credit card firms famously use data analytic techniques to segment their card service portfolios and manage them according to the risk profiles exhibited. This allows these firms to expand their customer bases in certain hard-to-reach segments. By developing more finely tuned analytics capabilities, these firms are able to grow revenues, enhance profitability, and continue to manage risk in a way that has expanded their risk tolerance frontier.

While supply chain leaders may not have business models that reflect the card services industry, they might take a cue from this strategy and reflect on where they could invest in more acute risk management capabilities to enable growth in segments previously out of reach. From a compliance perspective, it is also imperative to reduce the potential negative brand impacts of public perception. Imagine an outdoor clothing and equipment firm being reported as being out of compliance on environmental issues (Moss, 2020) or a luxury brand being accused of child labor violations. These are not simple cost and benefit supply chain decisions; they are essential aspects of protecting a firm’s brand reputation.

Case Example

Multinational Conglomerate Manufacturer and Brand Marketer of Building Products and Industrial Chemicals

Industry and Competitive Landscape

This industry includes the manufacture of building materials, home improvement products but does not include construction materials such as forest products and cement. The target customer and consumers can range from professional contractors, to individuals performing home improvements. It is one of the least digitized of industries showing very little in the way of automated business processes, digital connections with consumers, or its own workforce. With many of the industries product segments tied to the construction industry, its sales volumes are quite sensitive to housing starts. A large percentage of products are sold through home improvement retailers such as Lowes and Home Depot. In addition to the building products industry, the case company also manufactures industrial chemicals in bulk for commercial customers. This sector tracks closer to the overall global economic growth and performance.

Organizational Situation

The case company has a wide variety of business sectors, customers, and end users in its portfolio. With its origins as a venerable industrial-oriented manufacture of a wide variety of products, the firm has grown up with minimal efforts at organizational, business unit, or data integration. Rudimentary implementation of Enterprise resource planning systems remained somewhat siloed. There has been minimal effort ro manage master data, and a recent project to consolidate procurement commodity data to find and leverage sourcing relationships has been stalled due to incongruities in data sets. Forecasts are critical to planning manufacturing runs, yet the ability to bring in external data sets (such as housing starts) into the process has been slow. Data quality and alignment has been a source of problems for planners and decision makers. Being a high-intensity manufacturing business, process control data from manufacturing assets are extremely important to manage quality and efficiency, yet opportunities for collecting, consolidating, and integrating this data remain untapped. The company sells bulk industrial chemicals to customers that store them in large tank farms adjacent to customer owner manufacturing sites. Historically, customers have managed the inventories of these chemicals and placed replenishment orders with our case company at somewhat unpredictable intervals.

The case company has what might be called a risk averse culture, that is, innovations and changes to existing processes are viewed with caution and are slow to be implemented. Supply chain expertise and technology teams to support them are disbursed throughout the firm by business unit and sometimes by individual product teams.

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