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THE OUTSIDE-IN PLANNING HANDBOOK | 2023
Understanding Supply Chain Flows
Traditional supply chain planning systems use time-phased data as an output to drive alignment. In an outside-in model, the construct is to sense and manage supply chain flows. Making this shift requires overcoming many change management issues. The first is the recognition of multiple
of change to redefine the models from inside-out to outside- in. Focusing on transactional processes results in most planners not seeing the forest for the trees. In addition, the visualization helps organizations to understand better how fad- based processes like Demand- Driven Material Requirements Planning (DDMRP) or flowcasting apply to specific streams but not others. The key is to align the streams with policy and rules to improve outcomes. This is not possible with existing approaches
Table 3. Definition of Supply Chain Flows
Flow Flow (Volume) Forecastability Order Cycle Efficient High Value High (COV .03 or less) Regular Order Cycle Responsive High Low Short and Rapidly Changing Cycles Agile Low Low (COV of .05 or greater) Variable New Product Launch Varies Low Variable Seasonal Medium Low Variable
supply chains; the second is providing role-based views by function and user change. The outside-in process moves from a planner-centric organization to a new environment where the business user is the primary user. Planning self-service enables business leaders to collaborate on supply chain decisions across functions. In traditional definitions of supply chain planning, it is difficult to see demand and supply flows and detect market shifts in individual supply chains. In outside-in processes, the flows align with rules and policies based on market sensing (demand and supply). As shown in Table 3, companies typically have five to seven distinctly different flows. A helpful technique to drive understanding is to map each stream to understand the rocks, barriers and flows within the organization. This activity is beneficial to building a guiding coalition for change. In Figure 5, for example, we show the map of the river of demand for a large food manufacturing company for a new product launch. The mapping of flow changes the paradigm, laying the seeds
and planning taxonomies. In Table 4, note the number of players that struggle to make decisions on the demand stream without synchronized role- based views. Today, there are no applications in the market to enable team collaboration on demand visibility to support the new product launch processes. The second issue is that today, each team operates in isolation using their own data. Each data set has its problems, making integration difficult—the data granularity differs, and the integration is laden with latency and master data issues. For example, the latency of IRI data (a syndicated data provider), in Figure 4, is three weeks. The order latency is four to five weeks. While the company has daily point-of-sale data (twenty- four-hour latency), the data sits in the sales teams and is not used by marketing, finance, or supply chain teams. The lack of standard views for demand collaboration is an opportunity.
Table 4. Data Latency Example for Demand Data
Data
Team
Latency
Sales account point of sale, warehouse withdrawal, and replenishment data Syndicated data. This data comes in many forms, such as IRI, Nielsen, etc.
Sales. Data is managed independently by each sales team. The average food company has 40 account teams. Marketing. Syndicated data has a two-to-three-week latency from the channel. Supply Chain. Order data is a two-week to four-month view of channel demand.
Three days to 2 weeks
A two-to-three-week historical view.
Order data. Retail orders.
Two weeks to four months.
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