Increasing the Responsiveness of Supply Chain Planning
Next-generation supply chains will sense and respond to demand changes in real time in order to maximize business outcomes.
by Stuart Roy, September 2010
To adapt to the increasingly challenging environment in which they operate, supply chain organizations have transformed processes and added IT capabilities that reduce cost, improve responsiveness and increase performance. The build-to-forecast model has evolved into a demand-driven supply chain, utilizing postponement and build-to-order capability to provide high service levels at a reduced cost. Functional supply chain silos have been eliminated by aligning incentives and integrating end-to-end processes to improve efficiency and reduce cycle time. The linear supply chain has evolved into the networked supply chain as companies have outsourced increasingly strategic operations and increased the use of multi-tier supplier relationships.
The dynamic environment in which supply chains operate today challenges the traditional planning model. The shelf life of a typical operational plan is short; invariably, unforeseen changes in demand or supply will render the plan obsolete shortly after release. This necessitates re-running the plan. However, the time required to rerun the plan may well be greater than the time available to effectively respond to the unanticipated supply constraint or demand change. Frequently, this results in manual adjustment of the plan to accommodate actual business and operational conditions. Manual adjustment of the plan risks compromising the integrity or effectiveness of the original plan. Typically, several factors limit planning effectiveness:
1. Serial planning process: While supply chains have evolved from forecast-driven execution to demand-driven models, the planning process remains largely hierarchical and serial in nature. It takes time to cascade strategic and tactical objectives down to operational plans. Planners must assemble a vast amount of data to support the planning process, including demand, actual and in-transit inventory, supplier and manufacturing constraints, and lead time. Commonly, a significant portion of data requires manual intervention, including data extraction, consolidation, cleansing or formatting — all of which increase planning cycle time. In many cases, the data upon which the operational plan is based may be outdated before the plan is ever run.
2. Limited integration between planning and execution processes: Many companies treat planning and execution as distinct and separate processes. Most companies have implemented sales and operations planning (S&OP) and this remains the primary integration point between planning and execution. S&OP ensures the entire organization executes from a common plan derived from a single-consensus forecast. The process strives to optimize business outcomes by aligning sales, marketing, financial and operational objectives. However, the maturity of S&OP processes varies widely across companies. Many organizations lack the tools to support the S&OP process, relying on spreadsheets and manual data collection, formatting and analysis. This increases cycle time and the risk of outdated planning data being used while the lack of optimization and scenario modeling tools may result in sub-optimum outcomes. In addition, the typical weekly S&OP cycle limits effectiveness as it is too infrequent to respond to unanticipated changes that may occur with a higher frequency.
3. Limited visibility of demand and supply: Many supply chain organizations lack a single view of demand across multiple distribution channels. Frequently, a view of demand is manually assembled by downloading sales information from partners, extracting order management activity from various systems and then manually cleansing and consolidating demand information using spreadsheets. Similarly, demand forecasts are frequently created using spreadsheet tools that depend heavily on manual data manipulation. This approach results in data latency and quality issues and creates an incomplete, lagging view of demand.
Creating visibility into supply has become increasingly complex as companies have outsourced manufacturing, entered into increasingly strategic relationships with suppliers, and relied on multi-tier sourcing relationships. Many supply chain organizations have not evolved collaborative planning or process orchestration capabilities to keep pace with increasingly complex supplier relationships. Consequently, tracking supplier constraints or commitments and identifying process exceptions is largely manual, and does not occur in real time.
Evolving Supply Chain Capability
Next-generation supply chains will sense and respond to demand changes in real time in order to maximize business outcomes. As supply chains continue to evolve, the intelligent use of information will become increasingly important, and deficiencies in planning and execution will negatively impact performance. The real-time supply chain operates on a more granular timescale than ever before, requiring tight alignment between planning and execution processes as well as real-time planning and execution capability. New or enhanced capabilities are needed to support the model, including:
Demand sensing close to the point of consumption, and real-time demand management
Process orchestration across functions, partners and multiple supplier tiers
Continuous tracking of supply constraints and commitments
Detection of manufacturing and supplier exceptions in real time
Real-time planning to determine optimum action based on changing demand and supply
Supply chain organizations need to improve performance by more tightly coupling planning and execution capabilities, reducing cycle time, detecting supply and demand in real time and enhancing S&OP processes.
Eliminate data latency
Many supply chain organizations rely on several systems to support operations. Limited integration across systems results in manual data manipulation, latency and data quality problems, introducing latency into planning and execution processes. Data latency can be reduced by using integration technology to link disparate systems. Master data management can be used to manage complex data sets and transform them into a format suitable for planning and execution activities.
Improve demand sensing and management
Supply chain organizations can improve visibility of demand across multiple channels by sensing demand at or close to the point of consumption and automating data processing and analysis. The approach used will depend on the particular channel; organizations may need several approaches to sense demand across multiple channels. For example, point-of-sale data for retail, sell-through and inventory for the distribution channel, and contracts for contract-driven activity. Software tools can be used to consolidate, analyze and transform demand information into a form which can be used to drive planning and decision-making activities.
Enhance process orchestration
Supply chain organizations can eliminate process latency and coordinate supply chain activity by seamlessly orchestrating activities across functional boundaries and integrating processes and IT systems. Synchronize execution across the supply network and eliminate data latency by using supplier collaboration capabilities and intelligent alerts to coordinate activities, electronically share plans and schedules, track supplier commitments, and identify and act on exceptions.
Enhance S&OP process
The S&OP process is the primary integration point between planning and execution. Performance can be improved by increasing the frequency at which S&OP occurs, providing better optimization tools to support planning, and using accurate and current data. Eliminate and replace spreadsheets with robust planning tools supporting optimization and scenario modeling. Electronically populate tools with current data to eliminate latency and data quality issues. Increase the frequency of S&OP processes from weekly to daily to better optimize business outcomes based on actual supply chain conditions and business objectives.
Next-generation supply chains will increasingly rely on information to drive real-time decision-making. Supply chain organizations will need to sense demand across multiple channels, immediately detect and respond to supply exceptions, and drive real-time decision-making to maximize business outcomes. Today, data latency, limited visibility of supply and demand, and limited integration between planning and execution hinder deployment of capabilities needed to enable the real-time supply chain. To enable the real-time supply chain, supply chain organizations must first eliminate data latency, increase visibility, orchestrate processes, intelligently detect and process exceptions, and provide planning tools to drive real-time decision-making.
Stuart Roy is Senior Director of Industry Strategy and Insight at Oracle.