Supply chain benchmarking: recognizing potential quickly and efficiently

Efficiently and effectively identifying the relevant optimization potential along a supply chain is a central task in today’s supply chain management (SCM). This is one of the main challenges, particularly in the consumer goods industry.

This is because companies are confronted with a variety of challenges: On the one hand, increased supply chain complexity due to geopolitical tensions and economic conflicts is a major challenge. It is therefore particularly important to strengthen supply chain resilience. Another challenge is the rapid pace of digitalization and the potential applications of artificial intelligence (AI). If the potential here is not exploited and the degree of digitalization along the supply chain processes is not reviewed, it will be virtually impossible to keep up with the competition.

In addition, volatile freight prices due to changing personnel and transport capacities make planning more difficult. As price volatility is not expected to end in the medium term, freight prices must be continuously monitored in order to realize potential quickly. Effective supply chain benchmarking helps to meet these challenges.

Analysis framework for supply chain management

FMCG supply chains are generally complex, the starting points are diverse and resources are limited. Structured supply chain benchmarking of the optimization potential of all dimensions of SCM helps to identify significant potential quickly and accurately.

The major challenge in identifying optimization potential along a supply chain lies in the broad spectrum of associated functions and processes. These are typically assigned to the supply chain or supply chain management. For example, the functions of production, logistics and purchasing are often assigned to the supply chain.

At the same time, a wide range of structures and processes must be analyzed in the search for optimization potential. This ranges from strategic issues such as the physical structure of the supply chain to the value creation process and supporting tasks such as supply chain controlling.

With this multitude of areas and topics, it is easy to lose track. As a result, there is a risk of not identifying all relevant potential and focusing on the wrong topics.

In this situation, structured supply chain benchmarking helps to focus on the topics that are actually relevant. An analytical framework for supply chain management is presented below. On this basis, various qualitative and quantitative benchmarking methods are examined. Finally, it is shown how a stringent program of measures can be derived from the results of the analysis.

Optimal benchmarking: The analysis dimensions of the SCM Integration Framework

The Supply Chain Management Integration Framework (SCMIF, see Fig. 1) is a structured analysis grid for holistic supply chain benchmarking. It systematically evaluates all relevant dimensions of supply chain management and divides them into five functional levels.

The performance status of a company is benchmarked qualitatively and – where possible – also quantitatively. This allows optimization potential to be identified in a targeted manner and translated into specific programs of measures.

Qualitative supply chain benchmarking: quickly identify deviations from best practice

The qualitative benchmarking is based on a so-called “Stages of Excellence” assessment.In this, all tasks and processes of a dimension of the Supply Chain Management Integration Framework are described at four different levels (e.g. “Laggard”, “Average”, “Progressing” and “Best Practice”). For example, the task “Forecast Quality Measurement and Incentives” in the dimension “Sales & Operations Planning” can have the following levels (see Fig. 2).

The next step is to record the company’s current performance and assign it to a level. Overall, numerous individual assessments must be carried out for an overall evaluation of the company’s current performance level. In this way, it quickly becomes clear in which dimensions and for which topics the greatest deviations from best practice exist.

Quantitative supply chain benchmarking: determining orders of magnitude of potentials

In addition to qualitative benchmarking, quantitative benchmarking is carried out in a number of analysis dimensions.In this way, many of the qualitative benchmarking assessments are objectified once again. In many cases, they are also quantified so that the order of magnitude of a possible potential can be determined. The possibilities of quantitative supply chain benchmarking are manifold. Some benchmarking examples:

• Forecast quality benchmarking is a good way of assessing the performance of the S&OP process. For this purpose, quality measures for forecasts can be collected and evaluated, for example the mean absolute percentage error (MAPE) or the systematic bias of forecasts (BIAS). This can be done at product group or SKU level.
• Key balance sheet figures according to HGB can give a good first impression of the inventories of raw materials and supplies, intermediate products and finished goods. The electronic Federal Gazette provides the relevant data from comparable companies for calculating inventory turnover rates, for example.
• Process costs in logistics can be easily compared using benchmarking. The reason for this is the relatively standardized activities that are similar in many companies. A comparison of the cost rates for incoming goods, storage and outgoing goods of Euro pallets quickly reveals potential savings. This makes it possible to recognize whether there is potential for cost reduction.

As with all benchmarking, there is of course a risk of comparing “apples with pears”. For this reason, quantitative comparisons should initially be viewed with a healthy degree of skepticism. Major deviations from the relevant best practice benchmarks generally indicate existing potential. This can then be leveraged in a targeted manner.

Overall evaluation of supply chain benchmarking and optimization measures for your SCM

Finally, the benchmarking results are summarized in order to define the optimization measures to be initiated for your supply chain (see Fig. 3). Three assessments are included for this purpose:

1. The company’s own assessment of its performance level in the individual dimensions of the SCM Integration Framework forms the basis. An average of the assessments of individual employees from different departments and functional areas (360-degree assessment) is formed for this purpose.
2. The external view of an SCM consultant supplements the company’s own benchmarking, as self-perception and external perception are often not congruent.
3. Ultimately, a target state isjointly defined that needs to be achieved. This target state does not always have to encompass 100% of what is possible; in medium-sized companies in particular, it is essential to set objectives “with a sense of proportion”.

The corresponding optimization measures are then defined to close the gaps. The supply chain benchmarking results ensure that

• Optimization measures are initiated in the right areas where the biggest gaps to best practice exist,
• the scale of possible potential can already be estimated, and
• the involvement of many employees in the benchmarking project ensures a high level of acceptance of the measures within the organization.
  The results of the supply chain benchmarking are summarized as an example in the following spider web diagram (see Fig. 3):

Conclusion

With qualitative supply chain benchmarking and – wherever possible – quantitative benchmarking, weaknesses in a company’s supply chain management can be quickly identified. They are identified in a targeted and precise manner.

The comprehensive approach across all dimensions of the Supply Chain Management Integration Framework ensures a holistic analysis. This means that all relevant topics are examined and no potential is overlooked. In addition, benchmarking provides an initial indication of the level of possible potential. Functional benchmarking is therefore the ideal approach for starting a supply chain optimization project.