Even after order confirmation, prices are being increased on backorders, and those cost increases are passed on to buyers who are frequently seeing component deliveries pushed out, which disrupts production schedules or—worse—causes complete line down situations.
A reactive approach that occurs too late in the production cycle will be ineffective. In today’s shallow (or dry) pond, buyers are already in competition with other organizations for component availability. Alternative components quickly come under similar demand pressure, scarcity causing price increases.
A proactive and dynamic approach puts a buyer ahead of the game. By being in a position to anticipate supply chain disruptions before they occur, a buyer can take action to avoid adverse consequences. Using multiple data inputs and automated processes, an organization can leverage both the benefits of digitization and the power of iterative decision-making by informed experts. Traditionally, the “proactive” approaches commonly implemented in today’s electronics industry are limited. When processes are primarily manual in nature, the work ends up being similar to a reactive approach, simply performed earlier.
Open Order Reports and Spreadsheets
Typically, component buyers export an open order report, potentially with the added step of combining that data with a longer-term forecast requirement that is not yet on order. Those spreadsheets, sometimes thousands of lines long, are emailed to suppliers, who review these spreadsheets manually, adding comments about inventory availability, downstream order activity, and factory lead time. With the return of the spreadsheet, buyers review the comments, taking appropriate action as required. It is a painful process for all parties involved.
One of the problems of this—or any—manual process is that it relies on human judgement. Not only may that judgement be inconsistently applied, review of the information consumes a tremendous amount of time and resources. Furthermore, the duration is problematic. From start to finish it consumes a tremendous amount of linear time, meaning the data at the end of the process is no longer applicable to the requirements due to changes in the interim.
Siloed Data Problems
Data in the electronics industry is primarily organized around specific documents or known data sets: purchase orders, order confirmations, order status, change orders, invoices, advance ship notices, forecasts, part data, pricing, and availability. These data objects are organized digitally in the industry in a structure recognizable to anyone familiar with EDI, the equivalent documents for the aforementioned being: 850, 855, 870, 860, 810, 856, 830, 888, 832 and 846. The creation of APIs mirrors this structure in both construction and purpose.
The result is that data is well-organized, and multiple trading partners in a (nearly) real-time basis commonly understand the digital language used. An unintended result is that these purpose-built solutions are singular in nature and siloed. These datasets do not interact on a dynamic basis. Information is delivered in a one-way exchange without a response. If a response is enabled, the format is call and answer. There is no conversation occurring in this exchange, and the buyer’s action is contingent upon answers requiring evaluation and subsequent inquiries. Purpose-built processes provide answers; they do not provide solutions or quantify impact.
Dynamic Forecasting As a Solution
As a buyer, it’s natural to question whether you’re going to get what you want and when you want it. This information is critical to an open order report confirmation process. Many steps are required to achieve a result, which will lead to productive corrective action.
For instance, an order status API may be effective in determining that parts will be delivered on time at the correct price in the correct quantity. An order status API does not provide corrective action suggestions. What other packaging may be available? Are the other approved components available from my supplier? What inventory is available at other suppliers?
Furthermore, an order status API reports statuses for issued purchase orders. It does not perform the same useful response for forecast requirements. To achieve the benefit of an order status API, buyers may be required to look further out, converting long-term forecast requirements to issued purchase orders, increasing liability and reducing flexibility.
A proactive, dynamic forecasting resource leads a buyer to action as opposed to more questions. Suggested steps include:
- As inputs, consider currently issued purchase orders and long-term forecast requirements in one combined data set.
- Utilize currently available order status information via API.
- Establish known acceptance criteria for acceptability: #% cost increase, # days early to # days late, #% quantity adjustment. Apply that criteria on a consistent basis to result in a shorter list of problem parts to action.
- Automatically fetch internally approved AVLs for the problem components.
- Automatically fetch factory lead time for problem components and AVL equivalents.
- Automatically fetch price and availability data for alternative packaging formats from the current supplier for problem components and AVL equivalents.
- Automatically fetch price and availability data from alternate suppliers for problem components and AVL equivalents.
- For global organizations, automatically fetch component availability and open order status of other business units.
- Reapply acceptance criteria to expanded data set.
The result of this automated real-time activity is the presentation to buyers of: 1) orders to cancel, 2) orders to issue, and 3) orders to action. The issuance and cancellation of orders may also be automated in subsequent development.
In providing a list of problem parts to a buyer to action, a 360-degree view of potential solutions is also provided. The list of problem parts, as a subset of the total list, is manageable. With the addition of suggested solutions provided, you can resolve potential supply chain disruptions faster and with less effort.
Orbweaver’s integration technologies break down these silos, organize all of the data moving through a supply chain, and allow for this type of forecasting on top of the inherent efficiencies gained by automation the motion of data. As data automation is more widely adopted, the next step is using the data (in examples like this) to better manage, forecast, and understand the supply chain holistically.
