Supply chain analytics will enjoy growing relevancy. There are several reasons for why supply chain analytics is becoming increasingly relevant to industry. Some reasons that I will point out are increasing supply chain complexity, rising customer expectations, and increased relevancy of supply chain risk reduction. Moreover, with supplier price indices reflecting sustained inflation in most regions of the world, analytics can contribute to more efficient resource allocation and utilization, eventually contributing to lower costs and increased profits.

Supply chains have to improve performance

Increasing complexity makes it more challenging to manage transportation logistics, track inventory and goods in transit, improve customer satisfaction and delivery lead times, and manage capacity utilization. Customer expectations are rising, with consumers demanding faster delivery times, greater product customization, and higher quality products. Analytics can help companies identify trends and patterns in customer behavior, allowing them to better anticipate demand and tailor their supply chain accordingly. Analytics can also improve visibility and understanding of underlying supply chain issues and thereby contribute to higher supply chain performance.

With natural disasters, geopolitical conflicts, and other factors causing disruptions in the flow of goods analytics can help companies mitigate the impact of these disruptions by providing real-time visibility into their supply chains and enabling them to quickly identify and respond to potential problems.

Increased inflation due to supply shortages can make analytics even more relevant for companies. Analytics can help companies to better understand and manage their supply chain operations in the face of supply shortages, which can be caused by factors such as disruptions in transportation, production, or raw material supply.

Supply chain departments must master core analytics

There are many different types of analytics but here are the key domains that I think supply chain departments should master:

• Demand forecasting
• Inventory optimization
• Transportation optimization
• Supplier performance analysis
• Availability and backlog analysis
• Risk management
• Sustainability analytics

Mathematical programming and simulation can be used to build predictive models that can forecast future demand for products. By analyzing historical data and identifying trends, companies can use these models to optimize inventory levels and production schedules to meet future demand.

Mathematical programming can be used to determine the optimal inventory levels to maintain, based on factors such as lead time, demand variability, and order costs. It can also be used for transportation route, mode, and carrier optimization.

Simulation can be used to test different inventory policies and scenarios to identify the most cost-effective and efficient inventory strategy. It can also test various transportation scenarios and identify the most cost-effective and efficient transportation strategy.

Mathematical programming and simulation can be used to identify and analyze risks in the supply chain, such as natural disasters, geopolitical conflicts, or disruptions in transportation, and develop contingency plans to mitigate the impact of these risks. Both methods can furthermore be used to pro-actively design supply chain structures that are especially resilient towards defined external supply chain risks (e.g. geopolitical conflicts).

Technical skills for supply chain analytics teams

Supply chain management and operations research are not new domains. Most companies actively manage their supply chain and deploy supply chain analytics in some sense. But, does your supply chain analytics team have the basic technical skills?

A supply chain analytics department must have a setup for how to collect, organize, and analyze data from various sources. The data must be accessible for relevant stakeholders and be suitable for modeling, especially statistical and machine learning modelling. This aspect also involves knowledge of data governance practices, including data quality, data security, and data privacy regulations.

Analytical models (forecasting, network design, routing network etc.) are built with programming languages, such as Python, R and JAVA. It is generally also required to have at least a strong confidence in SQL.

Your supply chain analytics team must be capable of communicating data. This is done through visualization and meaningful reporting. This aspect also involves defining meaningful KPIs and metrics that can be communicated, understood and visualized.

A supply chain analytics team must also master relevant management software. In manufacturing and assembly industry your team should e.g. be confident in the use of ERPs such as SAP, with all relevant modules.

Lastly, familiarity with business intelligence tools such as Tableau or Power BI can help in visualizing and analyzing data.

Related content

You can read about exemplary applications of analytics, mathematical optimization and discrete-event simulation in some of our other publications. See e.g. the following SCDA blog contributions:

• Link: Optimized SCM capacity scheduling
• Link: Visual Components financial KPI simulation
• Link: Simulation methods for SCM analysts
• Link: Agent-based simulation for business
• Link: Prescriptive analytics for network design
• Link: Optimization via master production scheduling
• Link: Inventory simulation for optimized stock
• Link: Price and inventory optimization
• Link: Supply chain trends 2023

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python