Strategic Supply Chain Management - Supply Chain Online

CHAPTER 2 Core Discipline 2: Develop an End-to-End Process Architecture 65
For example, one of our clients was implementing new demand/
supply planning processes, supported by a supply chain planning application.
As part of the new process, the company wanted to track forecast consumption
over time to ensure that demand and supply were balanced on an
ongoing basis. This required gathering data from two separate sources—an
existing CRM application, which held data related to customer orders, and
the new supply chain planning application, which contained information
about incoming supply. Unfortunately, the ordering information described
products one way, whereas the planning application used different data to
describe products for the purpose of planning. The chief information officer
was concerned that resolving this disconnect would require restructuring
the entire data model, a major undertaking. In the end, an answer was
found that integrated the flow of information between the two applications.
A translation table was developed that took the elements of a customer’s
order and translated them into planning items.
Data quality and availability are as important as integration between
applications. The typical company orchestrates hundreds, if not thousands,
of supply chain activities and decisions every day, each depending
on a wide range of data: master data (supplier lead times, material masters,
prices, terms and conditions), transaction data (sales orders, inventory
data, purchase orders, etc.), and analytics (which compare actual
performance with target performance to ensure process management).
Despite the importance of accurate data, one study estimates that between
15 and 20 percent of a typical organization’s data are wrong or unusable. 8
Inaccurate or missing data lead to errors and ineffective execution.
Consider the example of a procurement system that captures quantities
ordered and confirmed by suppliers but doesn’t capture backorders—
quantities ordered but not confirmed. Backorder management must be
done either manually, at the risk of error, or not at all, which could easily
lead to overordering and excess inventory.
Inaccurate or unusable data also create manual work, reducing speed
and efficiency and adding costs to the supply chain. In the worst case,
inaccurate data can drive poor performance. As an example, we had a
client that felt the repercussions of inaccurate data for almost a year after
it first implemented a supply chain planning solution. When preparing for
the cutover to the new system, it had entered standard defaults for supply
lead times, with every intention of updating the data before the “go live”
date. Unfortunately, day-to-day tasks took precedence, and no one got
around to updating the supplier lead-time data—resulting in an increase in
materials inventory (lead times too short) for some materials and inventory
shortages (lead times too long) for others.