Supply Chain Management (SCM) is the control of all processes and data associated with an order. SCM covers the entire lifecycle of the order, from the moment that the customer places their request until the delivery is in their hands.
Effective SCM helps to create efficiency and improve customer service. SCM process supported by the right technology can generate substantial data for use in analytics.
What are the Elements of Supply Chain Management (SCM)?
Most businesses have several discrete processes involved in order fulfillment, from the process of sourcing materials to the process for handling returns.
SCM takes a holistic view of all of these processes, with the order itself as a unifying concept behind everything. On a conceptual level, we can think of a single item flowing through a unified process. On a more practical level, the organization may have some or all of the following individual systems:
Every organization, whether they supply goods or services, has to try to anticipate demand. In a just-in-time model, the organization will know exactly much stock they need or how many staff they need available at any particular moment. This kind of system relies on accurate data analytics to identify demand patterns and predict future trends.
Most businesses rely on suppliers to provide them with raw materials. Food companies need ingredients, manufacturing companies need parts, and service companies require support. Supplier management is intrinsically connected to demand planning. The company orders what they need based on forecasted demand, and then they track deliveries from their supplier with a system such as ERP.
Customer Relationship Management
Businesses will use a number of tools to manage the customer relationship during the order lifecycle. This can include a CRM platform that tracks all communications, plus self-service tools that allow the user to track deliveries online. Expectation management is a crucial part of SCM – customers want a fixed delivery date, and they expect their delivery to arrive on time.
Billing and Accounts
Each order involves a cash transaction. Typically, this means that the organization will issue a bill via the invoicing system. The customer will settle this either in cash or by electronic payments. There are alternative payment models – the customer may pay a regular subscription, for example. In all cases, the business needs to reconcile money received against invoices created.
This involves all systems involved in the manufacturing workflow. In a factory environment, that means tracking an item's progress from raw materials to a finished product. It may also refer to other methods of fulfilling customer orders, such as preparing a digital product for delivery by electronic means.
When the product is ready, it needs to move from the manufacturing area to the customer's hands. With physical goods, this can mean using the company's own fleet of vehicles. More commonly, it involves working with a logistics partner whose systems are available via a web interface or API. Logistics systems also need to track the delivery by other channels, such as digital delivery.
Rejections, refunds, and returns are a minor supply chain process in themselves. The company may need to provide logistics support to facilitate a return. They will then issue a replacement, resulting in a new logistics process, or a refund, which triggers a billing action.
What Happens to Supply Chain Management (SCM) Data?
SCM processes are a rich seam of data, offering unprecedented insight into customer activity, market conditions, the current state of supply chain and logistics, and opportunities for growth.
Consolidation is the first step in marshaling this data. Many organizations rely on an automated ETL (Extract, Transform, Load) process that unites all data in three steps:
- Extract: The ETL platform pulls data from each of the various SCM systems, including ERP, CRM, and billing systems.
- Transform: The extracted data passes through a transformation layer. Here, the ETL platform cleanses, validates, and integrates everything before passing it on.
- Load: The platform loads all SCM data into a single repository, such as a data warehouse. Management and analysts can put this data to practical use.
After integration, businesses can use SCM data in several ways:
- Dashboards and KPIs: Integrated SCM data offers a bird's eye view of the current state of the business. BI tools can present the data as a simple visual dashboard, enabling clear decision making. Managers can also track progress against several key performance indicators (KPIs), helping them to see if the business is on the right track.
- Improved demand forecasting: SCM data is invaluable when creating demand forecasts. With detailed data, businesses can make accurate guesses about sales volumes, which in turn allows them to pinpoint the amount of stock and other resources they will require. Ordering data then helps to plan the optimal time to place orders with each supplier.
- Logistics fine-tuning: Logistics is the most important part of supply chain management. Delays in transit can disrupt manufacturing plans, or cause a delay in the delivery to the client. SCM data allows the business to finesse their logistics strategy by identifying efficiencies and flagging up recurring problems.
- Better resource planning: When a business considers supply chain management as a whole, they can find ways to improve resource allocations. For example, better demand forecasting can allow a company to decide whether to hire and train additional staff or if they need to redeploy current staff to another area. They may also see other opportunities for efficiency: a period of high demand could be a chance to buy raw materials in bulk, or to batch dispatches together.
- Data analytics: A large body of SCM data can reveal things that may not be visible to the human eye. Data analytics tools can find patterns in data that offer deeper insights into manufacturing, logistics, customer behavior patterns, and overall market conditions. These insights can drive changes from process improvements to changes to the company's product line. For effective data analytics, however, the company will need a large volume of data, ideally consolidated in a single location.