Analytics as a lever for Supply Chain performance

Most companies have now realized the importance of seeking and maintaining data, not only from their own operations but also from suppliers and customers. However, not many of them fully understand how to use that data to boost their supply chain competitiveness, by either decreasing costs or increasing customer satisfaction.

As analytics becomes a popular subject, managers are broadening its range of applications within the existing corporate departments. Nevertheless, many supply chain decisions are still uniquely based on qualitative insights or competitor moves.

To run successful supply chain analytics projects, there are a few guidelines that should be followed:

  1. Mapping and exploring alternatives for further testing and evaluating
  2. Understanding the operation and mapping indicators before using analytics
  3. Modelling the as-is operation and fully grasping its intrinsic characteristics
  4. Validating the developed models and retrieving final indicators and results
  5. Gathering relevant stakeholders and making the final decisions

 

Transforming supply chain decision-making

As stated above, when performing analytical projects, it is common to make the mistake of planning modifications to organizations without considering a holistic view of its impacts.

Having this thought in mind, a project was developed together with a food retailer aiming to simulate its supply chain from end-to-end, encompassing operations from the main warehouses all the way to the store shelves.

The ultimate goal of this project was not to find a better supply chain configuration on itself but to give the company a tool to continuously and independently do so in the future. Therefore, the main project steps were revisited.

 

  • Mapping and exploring alternatives for further testing and evaluating

The first phase of the project consisted on exploring and listing all the use cases to be covered, detailing all possible strategic decisions, tactical levers, external factors and operational variables. The defined scenarios range from high-level decisions, such as network configuration or modifying the promotional activity’s magnitude, to operational factors, such as workers’ productivity improvement.

 

  • Understanding the operation and mapping indicators before using analytics

In the following step, a significant mapping effort was made to get a thorough understanding of all supply chain processes – cargo unloading, warehouse picking, store delivery, shelf replenishment, etc. –, levers – transportation delivery windows, workers and machines productivity, etc. – and variables, considering both material and informational flows.

 

  • Modelling the as-is operation and fully grasping its intrinsic characteristics

The subsequent phase consisted of modelling and developing the simulator while, in parallel, a vast range of scenarios were outlined, in order to further test some of the retailer’s initiatives and action streams. This phase was particularly lengthy as model validation is a highly demanding process, implying an iterative process of both comparing model results with the reality of the operations and making further adjustments for model enhancement.

 

  • Validating the developed models and retrieving final indicators and results

For each simulated scenario, several comparisons can be made with the retailer current situation, ranging from the value of store and warehouse stocks, the costs of spoilage and shrinkage to the transportation and workforce costs.

The developed project and resulting tool enabled a more aggregate and holistic view of the value chain, allowing the testing of distinct scenarios and hypothesis, thus empowering a more conscious decision making and sustaining the retailer’s competitive advantage.

 

  • Gathering relevant stakeholders and making the final decisions

The delivery of a tool enables the continuity of the project and leverages the developed work since it allows for the teams to carry on exploring new hypothesis in an independent manner. In this way, each team or department will be able to find, filter and decide on its own improvement opportunities, leveraging the decision-making process all through the supply chain.

In summary, this project brought together business expertise and analytical knowledge, to give managers a state-of-the-art tool that allows them to automatically and continuously assess different what-if scenarios and choose the most beneficial for the company's future.

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