SAS Supply Chain Analytics gives you a critical advantage by combining data from multiple sources. Understand the demand patterns, the supply networks, operations, quality and the customer service requirements like never before.
Quickly analyze, visualize and share information. Attain more accurate forecasts, greater integrated business planning efficiency, healthier profits and higher customer satisfaction.
Demand forecast drives integrated business planning (IBP) process and is its largest source of variation and uncertainty. Improving the demand forecast will affect everything throughout supply chain.
And, it can have a multiplier effect as it travels along IBP process. Even slight forecasting improvements can have a larger proportional effect on the revenue, costs, profit, customer satisfaction and working capital than any other factor, supply-oriented or otherwise.
Another crucial element we can provide to the IBP process is structure. Integrated Business Planning (IBP) can be one of the most unstructured processes found in an organization – often a jumble of disconnected data and spreadsheets.
Structure makes the process more efficient, manifesting itself in lower costs, faster forecasts and better decisions that enable your SAS Supply Chain management team to spend more time where it can add the most value.
SAS Supply Chain Demand-Driven Planning
Adopt an SAS SCM Analytics-based process for creating a demand-driven, weighted consensus forecast to automate and manage the information exchange between everyone involved in S&OP. With SAS for Demand-Driven Planning and Optimization suite, you can:
- Operate more efficiently.
Automatically generate statistically driven, weighted consensus forecasts. Monitor the forecast performance to understand the value added (or lost) at each step. Then share information easily among sales, marketing, finance, operations and SAS Supply Chain teams to readily implement the sales and operations plan.
- Plan more effectively.
Use time-series forecasting to build models that reflect the realities of the business, taking into account intermittent demand, new product launches, pricing, promotions – even weather. Use sophisticated optimization algorithms to compare and adjust the forecasts so you can choose the best strategy.
- Boost your profitability.
Gain near-real-time insight into SAS Supply Chain and demand dynamics so you can avoid under- or over-stocking. Calculate optimal inventory policies using multi-echelon optimization with state-of-the-art simulation. And use predictive modeling and what-if analysis to find out how different variables will affect the supply or demand balance.
- Multi-echelon inventory optimization.
Automate and optimize the inventory distribution, inventory levels and order quantities at every level to maintain adequate stock, maximize response times, increase revenue, reduce carrying costs and improve customer satisfaction. Calculate the optimal inventory levels using inventory policy parameters throughout the entire supply chain.
Automate and optimize the inventory distribution by providing the ability to take a forecast and from that calculate optimized inventory levels and order quantities for every SKU at every level and every location.
- Demand sensing and shaping.
SAS Supply Chain Challenges
- Antiquated approaches – Many organizations still manage their most important supply chain processes using spreadsheets.
- Basing demand on sales, not just supply – A lack of visibility into channel sales and inventory makes it difficult to identify demand patterns and then prepare a demand response model.
- Lack of technology – Most companies who have implemented S&OP processes have limited technology capabilities that focus primarily on the supply side.
- Uncertainty in demand and supply – Without a strong S&OP process and the ability to combine sales data and market-driven data it’s difficult, if not impossible, to synchronize demand and supply.
Multi-tiered Causal Analysis
Multi-Tiered Causal Analysis (MTCA) is an approach that links a series of quantitative methods to measure the impact of sales and marketing strategies on consumer demand. MTCA creates various what-if scenarios to shape and predict future demand.
This means you can link demand and supply using SAS SCM analytics rather than judgment. The Manufacturers can have several tiers and a series of causal models to measure the impact of demand on each level of the supply chain.
Integrating consumer demand to improve shipment forecasts has become a high priority in consumer packaged goods, automotive manufacturing, appliances, electronics, pharmaceuticals and many other industries. These industries use marketing tactics to pull the consumer demand through their distribution channels.
With improvements in technology, the data collection and storage, and the analytical know-how, companies are now integrating consumer demand with shipment forecasts to capture the impact of marketing activities on the entire supply chain.