Mastering Data Migration – the Blood That Flows through IT Systems
We draw a parallel between two seemingly disparate yet intricately linked processes: a blood transfusion and a data migration.
As any supply chain professional can tell you, the data used to drive processes and decision-making is one of the key factors for how efficient and effective a supply chain works. Typically, this includes performance indicators like product lead times, sales history, forecasts, production and storage capacities, or inventory targets — internal metrics and data that is specific to each separate company. For decades, this data has been the standard for companies. However, in today’s global, constantly changing, and highly uncertain supply chain environment, does this data really drive the best supply chain practices? In this blog article, you will learn the benefits of the outside-in planning concept and how to implement this approach within your organization.
Outside-in planning challenges standard supply chain planning practices, emphasizing the importance of using “outside” market data instead of relying solely on enterprise data that is internal and company-specific. Outside-in recognizes that many of the assumptions made about supply chains in the past simply do not apply and that enterprise data alone is not sufficient to make a supply chain work in the real world.
A reduction in global logistics availability acts as a good illustration of this: In the past, it was assumed that companies would always have logistics providers available at a relatively stable price: If their logistics plan worked internally, it could be executed. Nowadays, the availability has been reduced dramatically and prices fluctuate and spike on a regular basis. Without outside data alerting organizations to this data, a company’s logistics plan could be either severely delayed due to unavailability, or prohibitively expensive. An outside-in approach would integrate external logistics availability and price data into planning, enabling teams to create plans that are responsive to real-world and real-time constraints.
While access to external market data is a core element of the concept, outside-in planning asserts that companies must also change their processes to prioritize this data and have it at the heart of decision-making. Since external market data is not easily controlled or influenced by internal decisions of individual organizations, outside-in processes focus on adaptability and being able to respond appropriately to different changes. This minimizes disruptions in demand and supply flows. As these processes mature and more market data becomes available, a business’ process latency (the time it takes to react to a situation) will improve, leading to less impact from possible disruptions.
Currently, given the vast amount of external data available, coupled with the lack of IT systems which can properly synthesize and analyze this data effectively, it remains a challenge for organizations to operate entirely outside-in. Despite these limitations, there are concrete steps that supply chain organizations can take to drive towards an outside-in approach and position themselves for future success.
While some industry or market-wide data exists, the data a company requires often either does not exist, is too broad to make specific decisions, or requires significant IT efforts to properly synthesize and analyze the data. Despite these hurdles, companies can still start to take an outside-in approach by collaborating with business partners on both the demand and supply side.
Though not fully representative of the broader market, supplier and customer data as well as collaboration act as a great starting point for adopting an outside-in mindset. This provides the supply chain function with a better understanding of possible constraints or disruptions and opportunities with their partners. While this concept of collaboration is not new, it is typically used for enhancing forecasts or other internal metrics. To take the next step towards an outside-in approach, this information should be used to drive specific actions and planning adjustments rather than just being considered as another internal data point.
Another key to driving successful outside-in processes is understanding which different supply chains and demand flows exist and how they should be treated differently. For example, for a pharmaceutical company, finished goods may have a two-month lead time, whereas some raw and semi-finished goods could be closer to a year. This lead time difference, in addition to the many other differences these products share, will result in different ways of solving issues or planning for future demands and supplies.
The same applies to demand streams. It is very common for CPG companies to sell both to large retailers and direct-to-consumer via online channels. Not only will these relationships be managed differently – but in this case, sales patterns could be completely unrelated. One is dependent on a company’s ordering strategy, and the other on the direct customer demand.
This type of segmentation, coupled with ensuring that decisions are reflected over the correct time horizons, will help organizations to identify the most relevant data and prioritize its acquisition for optimal decision support.
Even if an organization possessed the entire desired external data and the appropriate tools to analyze that data, it would still take time to develop a fully outside-in supply chain. While the technical aspects of outside-in planning are paramount, cultivating a data-driven mindset is equally crucial to properly consider this data.
In early pilots and tests of outside-in planning, a lack of top-down leadership driving the outside-in planning was identified as a major hurdle for successful implementation. Especially for decisions that may be typically handled in Executive S&OP or Management Business Review meetings, outside-in processes have no chance of success without high-level support.
To ensure an organization is prepared to fully adopt outside-in when data and technology are sufficiently available, teams must start considering outside information or implications at the core of their decision-making processes. If outside data is available, it should be integral to the decision-making process. However, even if it is not integrated, scenario planning, for example, should strongly consider changes in external factors that would impact the business. While achieving the buy-in from all organizational levels is essential, the commitment from the leadership team is paramount to the successful adoption. Otherwise, there is a risk that employees at lower levels perceive the outside-in planning as lacking in value, hindering its implementation.
While a fully outside-in supply chain requires more external data and computing power than currently available, organizations do not need to wait to begin driving towards outside-in processes and reaping the benefits. By leveraging data that may be available to them via suppliers and customers, segmenting and treating individual supply chains within their companies differently, and having the management adopt an outside-in mindset, companies will begin to achieve the benefits and be able to react at an early stage to changes. With these ideas in place, they also put themselves in the best position possible to be truly outside-in and strengthen their supply chains.
We draw a parallel between two seemingly disparate yet intricately linked processes: a blood transfusion and a data migration.
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