Useful Concepts in Supply Chain Strategy Design.
Constraint Based Optimisation
Constraint-based optimisation in a supply chain strategy design context involves developing an optimal supply chain model by considering a set of constraints, and then maximising or minimising an objective function that describes the supply chain's overall goal. Typically, it focuses on service, cost, capacity and capability.
Baseline Calibration & Analysis
Establishing a baseline model in supply chain network design is crucial as it provides a reference point that represents the existing network under normal conditions, thereby enabling performance benchmarking. It aids in understanding the interconnections and dependencies within the supply chain, potentially revealing inefficiencies or areas for improvement. This model also serves as a means to validate the modeling approach. Once established, it forms the foundation for scenario modeling, which involves simulating different situations or strategies by altering model parameters. Comparing these scenarios against the baseline helps in making informed decisions about potential changes or enhancements to the supply chain network.
The optimised baseline is to highlight what can be achieved within the existing network design (infrastructure, asset profile, etc.) with ‘quick wins’ – e.g. process redesign, limited technology investments, etc.
Changing a network or the asset profile can trigger inventory outcomes. Inventory modelling is integral to supply chain design as it aids in cost optimisation, enhances service levels, manages risks, and guides strategic decisions. It helps manage working capital by avoiding excessive inventory, informs range decisions on what items to stock, and establishes safety stocks to mitigate supply-demand uncertainties. It guides the choice of replenishment methods, balancing stock levels to prevent stockouts and overstocking, thereby optimising overall supply chain performance and financial health.
Scenario modelling in supply chain network design involves creating and analysing various hypothetical scenarios that reflect potential changes or events impacting the supply chain. This can include shifts in demand, changes in supply, new product introductions, transportation cost variations, regulatory changes, or disruptions like natural disasters. This approach aids in preparing for different situations, making informed decisions, identifying risks and opportunities, and enhancing the flexibility and resilience of the supply chain. Scenario modelling often employs mathematical or computational models, leveraging techniques from operations research, statistics, and machine learning, depending on the supply chain's complexity and the scenarios in focus.
Monte Carlo Simulation
Monte Carlo simulation is a technique used in supply chain network design that uses random inputs to simulate a multitude of scenarios and assess potential outcomes of uncertain variables, thereby facilitating risk assessment in complex systems. It enables evaluation of uncertainties in demand, supply, transportation, logistics, and operational aspects by randomly generating values for these variables and simulating supply chain performance under these conditions. This provides a probabilistic view of risk and potential outcomes, aiding supply chain managers in making informed decisions and crafting strategies to enhance supply chain resilience.
Sensitivity testing is a crucial part of supply chain network design and scenario modelling, as it allows organisations to pressure test key inputs, assumptions, and dependencies within their models. It involves altering one variable at a time while keeping others constant to understand the impact of that variable on the overall system. This helps identify "sensitive" areas where small changes can have a large impact on outcomes, aiding in risk identification and mitigation. By conducting sensitivity testing, decision-makers gain a better understanding of the robustness of their models and strategies. It helps illuminate potential vulnerabilities in a supply chain, such as reliance on a single supplier or a particular distribution route and informs contingency planning. Additionally, sensitivity analysis supports more informed, resilient decision-making by providing a range of potential outcomes based on variable changes, rather than relying on a single, static scenario. Ultimately, it increases the overall reliability and validity of supply chain network design and scenario modelling.
Example Questions that we help answer.
Supply Chain Network Design
Design. What should my supply chain network look like?
Composition. What is the optimal composition of supply chain infrastructure across my network (e.g. distribution centres, fulfilment centres, dark stores, stores, etc.)?
Customer Value Proposition. What elements of our customer value proposition should we prioritise to help us grow?
Automation. What level of automation is optimal in my supply chain, for my products and customers?
Availability & Responsiveness. How can we effectively balance availability and responsiveness within my current network?
Operating Model & Cost. How can we improve the operating cost profile of our supply chain?
Online Fulfilment - Emerging Investment Decisions
Centralised or Decentralised? To what degree should we centralise our online fulfilment physical network?
Together or Dedicated? To what degree should we bring together our store and online fulfilment operations?
Manual or Automated? To what degree should we automate our online fulfilment – given volumes, product profile, etc.?
Push or Pull? What is the optimal inventory operating model for online fulfilment?
Technology Options? It is only once an organisation has a relative feel for the above that specific technology options should be considered.
Members of the trace. team have completed over 50 supply chain strategy, design and network optimisation projects and worked with many of Australia’s leading organisations – across retail, apparel, grocery, manufacturing and consumer goods.
We have a long, established history of working with 3rd party tools and supply chain optimisation technology variants, a sample of which are listed below. In addition, trace. has multiple in-house designed and developed network optimisation toolsets at varying levels of scale and complexity depending on the nature of the Supply Chain optimisation question that needs to be answered.
Operational benefits are often a key driver of supply chain strategy and network design. Figure 1 is an illustrative view on an example list of benefit areas – subject to the nature of the scenario being tested.
It is key when designing & implementing supply chain strategy that senior leadership have an appreciation for operational considerations.
This is why at trace. in developing supply chain strategy, we focus on elements such as sensitivity testing – reviewing not just the network design and assets profile. Importantly, we view the system holistically to also consider elements such as inventory, supporting technology, and upstream & downstream implications. This is to ensure a theoretical scenario and its benefits can translate and be implemented in the ‘real world’.
Network Design Data Considerations and Complexity.
Supply chain data and financial data serve distinct purposes, particularly in relation to supply chain strategy and network design. At a high level, here's how they differ:
Type of Data: Supply chain data is operational, encompassing the flow of goods, services, and information across the supply chain. Like other types of data it can be classified into ‘item master data’ and transactional data (both on the demand and supply side). It often includes information such as SKU, location, transaction volumes, units of measure, source & destination, product profiles, cubic information, weights, handling types, lead times, capacity, service levels, etc. Conversely, financial data is monetary, including revenues, costs, profits, assets, liabilities, and cash flows.
Purpose of Data: Combined supply chain and finance data can help inform decisions around transport, labour, automation, network design, etc. Supply chain data typically drives operational decisions such as network strategy, automation decisions, inventory management, demand forecasting, and transportation planning, essential for improving efficiency and service levels while reducing costs. Financial data guides financial decisions like investments, risk management, and performance evaluation.
Granularity of Data: Supply chain data offers granularity, detailing information down to individual transactions or SKU levels, providing attributes such as specific product weights, sizes, and handling requirements. Financial data is generally more aggregated, presenting an overall picture of the business's financial performance or of major divisions or product lines.
In context to supply chain strategy and network design, supply chain data, with its detailed operational attributes, is essential for understanding and making informed decisions about supply chain structures. Meanwhile, financial data, though less detailed operationally, offers crucial insights into the financial implications and constraints of these decisions.
Product Master Data is Critical.
The availability of detailed product information and data is critical when considering the automation of warehouse operations. Below are some examples of why these specific data points are important:
Units of Measure: Understanding the units in which products are stored, handled, and transported is vital to design automated systems. For example, robots may need to be designed to handle items of certain sizes, or conveyor systems might need to be configured to accommodate specific package dimensions.
Cubic Information: Information about the volume of products helps in space optimisation. Automated storage and retrieval systems (AS/RS) require exact dimensions of items to optimise the storage layout and retrieval sequence.
Weights: Knowledge of product weights is crucial for the design and selection of automated equipment. Automated equipment has weight limits, and knowing product weights ensures the selected systems can handle the loads safely and efficiently.
Handling Requirements: Certain products may have specific handling requirements, such as fragile items, perishables, or hazardous materials. These requirements can dictate the type of automation technology suitable for use.
When reviewing supply chain design, data forms the cornerstone of decision-making.
Typically, a supply chain strategy review requires the following data elements.
Demand Data: Forecasts of customer demand by SKU and by region or customer group are critical. Historical sales data can be used to predict future demand.
Supply Data: Information about suppliers, including lead times, reliability, costs, and capacity constraints, is necessary for strategic sourcing and risk management.
Inventory Data: Current inventory levels, receiving & carrying costs, lead times, fill rates, and other key inventory metrics should be collected.
Cost Data: Detailed data on all costs associated with the supply chain, including procurement, production, warehousing, transportation, etc.
Transportation Data: Information about transportation routes, modes, times, and costs, as well as capacity constraints, helps optimise the logistics network.
Facility Data: Information on existing facilities (e.g., warehouses, factories), including their capacities, costs, locations, etc.
Transactional Level Data: Detailed transactional data, such as individual sales, purchases, and shipments, is vital as it provides granular insights into supply chain operations. It enables precise demand forecasting, detection of patterns and trends, and can aid in optimising inventory management, procurement, and distribution strategies.
Service Level Requirements: Data on required or target service levels, such as fill rates, on-time delivery rates, and order cycle times
Risk Data: Potential risks & disruptions should be collected for robust risk management.
Regulatory & Competitive Data: This data may benefit strategic planning.