Guidelines for successful supply chain analytics projects

Supply chain optimization is a topic with increasing importance, not only because efficiency is key in a progressively more competitive environment, but also because consumers are continuously more demanding about assortment diversity, lead time and price. Using analytics for supply chain optimization might just be the key to get the edge over the competition.

 

From understanding to decision-making

To run successful supply chain analytics projects, five guidelines should be followed.

 

1. Understanding the operation and mapping indicators before using analytics

Despite seeming an effortless task, disregarding the significance of a decision on adjacent areas is excessively common, mainly because managers are focused on their own operational scope and tend to neglect what surrounds it. Besides undermining the project’s expected results, ignoring side effects often becomes a pain point for change management in later phases.

 

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

The initial analysis frequently aims at modelling the as-is situation and comparing it with real costs and KPIs, guaranteeing that the model is a reliable representation of the reality. It is important to discuss all assumptions and outcomes with the involved stakeholders to ensure some comfort about what is and what is not being considered.

 

3. Mapping and exploring alternatives for further testing and evaluating

While taking other players moves into consideration is obviously valuable, bringing operational and mid-level teams together to brainstorm about new possibilities is often game-changing and might result in a competitive advantage.

 

4. Validating the developed models and retrieving final indicators and results

Considering the defined scenarios, it is time to adjust the models created to predict how each indicator would react to new supply chain configurations. Merging analytics’ prowess and business expertise is pivotal to successfully accomplish this mission. Validating the model and verifying if each lever leads to the expected result is a critical step to guarantee confidence in the results.

 

5. Gathering relevant stakeholders and making the final decisions

Since decision-makers always have the last call, independently of the quality of the analytics project, we reinforce the importance of involving every internal and, if possible, external stakeholders. The success of the change management stage largely depends on everyone's confidence in the benefits of the project.

 

Tailor-made Supply Chain optimization: a case study

Take the work we have recently done with a large electronics retailer, in which a challenge emerged to reshape the entire Iberian supply chain and to redesign the retailer's global network.

Let us go through the five guidelines.

1. Understanding the operation and mapping indicators before using analytics

In this context, the first phase of the project consisted of understanding the problem and analyzing the involved business requisites. At the same time, the team concentrated on mapping all relevant indicators (e.g., service level, lead times, etc.) and costs (rental cost, handling and stock costs for both warehouses and stores, transportation to stores and home-delivery and structure costs).

 

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

The following step of the project was to explore the available data and build an analytical model that represented the operation of the company at the time. Through statistical analysis and optimization modelling, the team was able to replicate the logistics process with close-to-reality operational indicators such as distance travelled during transportation, warehouse productivity and stock levels.

 

2. Mapping and exploring alternatives for further testing and evaluating

Together with the retailer and according to the company’s strategic goals, a wide variety of configuration hypothesis was established for testing and evaluation. The different available solutions included having storage hubs with or without stock, keeping stock in one country or choosing a hybrid approach and outsourcing or internalizing the warehousing process.

 

3. Validating the developed models and retrieving final indicators and results

The ensuing phase consisted in developing a holistic optimization and simulation model to test the different scenarios regarding warehouse future locations, logistic flows and warehouse-to-store allocations, aiming to minimize overall costs while ensuring a high service level. In this step, and for each scenario, all previously defined indicators and costs were predicted.

 

4. Gathering relevant stakeholders and making the final decisions

Towards the end, all involved stakeholders were gathered, the different options were discussed and compared, and a final decision was made. The most relevant decisions of the project involved the centralization of part of the distribution process in the Portuguese warehouse, the relocation of the Spanish warehouse to a more cost-effective area, and a significant boost of the supplier-store direct deliveries.

In this step, it was critical to ensure alignment between stakeholders and to reach a conclusion in which everyone agreed on, even though it wouldn’t necessarily bring gains to all parties involved. It is crucial to bear in mind that to maximize the overall benefits, some departments and stakeholders may temporarily have losses.

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