ARC Advisory Group has been covering the Supply Chain Planning (SCP) market for 17 years. The market has been growing at double-digit rates for several years, even during the pandemic. The pandemic brought home the need for companies to run agile and resilient supply chains. SCP is a critical application to help companies achieve better agility.
Supply chain agility reflects a company’s ability to respond quickly to surges or plummeting demand. Agility can also reflect a company’s ability to effectively deal with unexpected constraints caused by strikes, earthquakes, political strife, and a variety of other events. Supply chain resilience refers to planning for things that could go wrong and then creating inventory buffers or contingency plans.
SCP solutions provide a solid ROI based on hitting targeted service levels with less raw material, work-in-process, or finished goods inventory. Attaining high service levels can be strategic because this drives higher sales.
SCP solutions are often used in an integrated business planning (IBP) process. A robust IBP process allows companies to set better financial targets and achieve those targets with a higher level of certainty. For companies with any complexity surrounding products, channels, or customers, no IBP process can be considered robust without employing SCP tools.
ARC defines supply chain planning (SCP) products as including supply planning, demand planning/inventory optimization, and network planning. Network planning solutions include supply chain design, integrated business planning, and end-to-end supply chain analytics.
Supply Planning
Supply planning systems create models that allow a company to understand capacity and other constraints it has in producing goods or fulfilling orders. The factory models can include how long it takes to set up a machine, how many units per hour can be made by the machine, how long routine maintenance takes, how many workers are needed, and the hours the plant works, among others. Fulfillment constraints can include how long it will take to deliver goods to a destination, warehouse capacity, and warehouse labor requirements. The system then uses advanced algorithms to calculate the optimal way goods can be produced and fulfilled.
Supply planning engines “optimize” the schedule. An optimal plan is not a perfect plan. Optimization is needed when there are so many ways an end-to-end schedule could be developed that even if the planning engine ran a million years ago, it would still not have considered all the options. “Optimization” uses clever math to come up with a very good solution in a short planning run.
Supply chain planning solutions are tradeoff machines. A planner can see how much extra it would cost to take service levels from 95% for all customers to 99%. However, not just service levels and costs can be traded off. A planner could ask the SCP engine to achieve 95% service, with CO2 emissions under a million metric tons at a given factory in the coming month. This would be a three-way tradeoff. Other tradeoffs, such as maximizing cash on hand or allocating products in short supply to preferred customers, are also possible.
No plan is perfect. Demand will be higher or lower than expected. The ability to meet that demand can be less than expected. Concurrent planning links execution – the things a company needs to do in the next few days or weeks – to the longer-term financial plans. As new short-term schedules are created, the linkage to revenue and profitability goals in the IBP plan becomes instantly visible. This allows planners to run scenarios and pick a new strategy that helps to ensure that financial and strategic objectives will be met.
Demand Planning and Inventory Optimization
Demand planning is the process of forecasting the demand for a product or service so it can be produced and delivered more efficiently while meeting customer service level expectations. Demand planning is an essential step in supply chain planning.
These forecasts occur in three different time horizons:
Long-term planning. Often called strategic planning, this is a forecast spanning 1 – 5 years. In long-term planning, a company forecasts the volume and types of products it will produce over the next several years and then examines whether it has the necessary capacity and infrastructure to meet that demand.
Medium-term planning. Also known as tactical planning, these are monthly plans created for the next 12 to 24 months. The near-term plans are firmer and more nailed down than plans three months or longer out in the future.
Short-term planning. Also known as operational planning, this takes place weekly, daily, or sometimes multiple times per day.
These forecasts also occur at different levels of granularity. A company might produce a forecast that shows how many units in a product family will need to be made across all factories in the coming month. Or, the forecast might be much more detailed. How much of a given stock keeping unit will need to be shipped to each of our retail customer’s distribution centers in the coming week?
Forecasting has historically involved examining sales and order history and applying statistical techniques to that data. However, machine learning is increasingly being used, particularly for short-term forecasts.
Machine learning is particularly effective when it also uses external downstream data. So, for example, a manufacturer knows what it has sold to a retailer. However, suppose the retailer allows the manufacturer to access data on how much of their product is sold at each of their customers stores. In that case, the short-term forecasts can be improved. This downstream data allows for better demand sensing. Demand sensing forecasts can adjust to the swings in demand much more quickly than statistical forecasts.
Inventory optimization solutions take a demand plan and determine how much inventory needs to be produced and where and how much must be held across a network of factories, warehouses, and stores to hit defined service levels.
Here is the full Supply Chain Planning Viewpoint.
