Supply Chain Planning and Execution

Organisations frequently experience conflict between the intention of having a large variety of products to compete and capture market share and the desire to reduce inventory costs and avoid obsolescence. This means they have to continuously monitor and optimise their portfolio to find the ideal balance, which in turn involves understanding each stock keeping unit (SKU) and its contribution to the overall performance of the portfolio. This is done through a combination of metrics – including margin – so that the impact can be understood from multiple perspectives and action can be taken. We can help your organisation to strike the right balance between product variety and operational efficiency.

In today’s dynamic and interconnected business landscape, effective demand planning is essential for organisations seeking to optimise their operations, manage resources efficiently and meet customer expectations. To ensure that the input into your demand planning is accurate, you must:

• Enhance data quality and integration, ensuring the availability and accuracy of data from various sources and implementing robust data integration techniques to provide a single source for demand planning.
• Utilise advanced analytics and technology, using machine learning and predictive modelling techniques to improve demand forecasting accuracy, scenario modelling capabilities, and automated workflows to streamline the planning process.
• Implement agile and adaptative processes, embracing agility and flexibility to enable rapid adjustments and course corrections in response to changing market dynamics and unforeseen events; implementing agile methodologies such as iterative planning, continuous feedback loops and scenario-based planning to improve responsiveness and resilience.

By segmenting your customer base into distinct groups based on relevant characteristics, you can better understand the unique needs and preferences of each group. This enables you tailor your demand forecasts to account for variations in demand across different segments, leading to more accurate predictions. Moreover, utilising AI algorithms such as machine learning models can enable you to analyse large volumes of historical sales data, market trends and external factors to identify patterns and correlations, generating forecasts with greater accuracy and adaptability, continually refining predictions as new data becomes available.

Strategies such as integrating real-time data, utilising flexible forecasting models and fostering cross-functional collaboration can equip your teams with the necessary tools to boost agility. By incorporating multiple demand scenarios, dynamic regression models or machine learning algorithms, teams can develop multiple demand scenarios and rapidly adapt to changing conditions and incorporate new information. Additionally, iterative improvement processes enable you to continuously refine forecasts based on actual performance and market feedback, facilitating quick responses to market fluctuations and customer demands.

You can do this by structuring supply chain processes, systems and networks to be flexible and adaptable to changing market conditions. Some of the key components of agile supply planning are:

Scenario planning: Developing contingency plans and simulating various scenarios such as changes in demand, supply disruptions or shifts in market dynamics can prepare your teams to anticipate potential disruptions and proactively identify vulnerabilities and develop strategies to mitigate risks.
Collaborative partnerships: Fostering strong partnerships with key suppliers and distributors across the supply chain to share information, align goals and harness each other’s strengths and resources to respond more effectively to changes in demand or supply.
Flexible manufacturing processes: Implementing manufacturing processes that can adjust production levels and product configurations in response to changes in either supply or demand. Using modular production systems or advanced manufacturing technologies such as 3D printing can boost manufacturing flexibility.

Supply chain leaders are constantly under pressure to contribute to cash flow by reducing inventory. Our team can help your organisation assess the best approach to reducing inventory costs by means of various measures:

• Structural network changes: Optimising your supply chain network by consolidating suppliers, warehouses and distribution centres to reduce inventory holding costs; using cross-docking facilities to minimise inventory storage time and handling costs.

• Review production parameters: Analysing production parameters such as batch sizes and production schedules to align with demand variability; calculating economic order quantity (EOQ) and minimum production quantities (MPQ) to determine order quantity that minimises total inventory costs, ensuring efficient production runs while avoiding excessive inventory buildup.

• Inventory level calculation: By strategically implementing adequate measures such as safety stock levels and reorder points to trigger replenishment orders in your operation, you can reduce inventory costs while maintaining high service levels. More sophisticated methods are also being applied in many operations, for instance using multi-echelon inventory optimisation (MEIO) based on advanced algorithms, balancing inventory levels across multiple nodes in the supply chain, taking interdependencies and constraints into account.  

Inventory optimisation poses significant challenges, ranging from data quality issues to regulatory compliance requirements. Traditional approaches often struggle to manage the complexities of master data and demand forecast uncertainties. However, by leveraging advanced algorithms and data analytics, AI can enhance inventory management processes:

• Data quality improvement: AI algorithms can be employed to identify and rectify errors in master data. Machine learning models can detect patterns of inaccuracies or inconsistencies, allowing the automatic correction or flagging of problematic data points.

• Data synchronisation: AI-powered systems can automate the synchronisation of master data across multiple systems in real time by understanding and interpreting updates from various sources.

• Complexity management: Techniques such as deep learning can extract relevant features from complex data structures, processing and analysing large datasets with numerous attributes, hierarchies, and relationships.

• Inventory optimisation: AI-powered optimisation algorithms can dynamically adjust inventory levels based on changing demand, supply and regulatory constraints, balancing the trade-offs between inventory costs and service levels to optimise inventory allocation across the network.