The movement toward intelligent agent-based applications for supply chain
management is in full swing, and there are many other new "agents"
emerging. One of the more exciting application areas is real-time order
promising of supply chain resources. Unlike conventional available-to-promise
(ATP), the intelligent agent-based application assesses the entire supply
chain to determine the best location to source finished product for a customer-considering
product cost, customer request dates and distribution costs. The result
is a revolution in order promising, where customer commitments can be made
based on real supply chain resource availability versus static master production
schedules.
Intelligent Agents at Work
A sales order promising agent evaluates the actual capacity and material
availability of an entire supply chain before suggesting a promise date.
Tightly coupled with existing ERP systems, the real-time agent intelligently
evaluates companywide resources so that sales operations teams can make
delivery commitments during their customers' initial telephone inquiry.
Conventional "available-to-promise" capabilities only allocate
demand against a previously approved master production schedule derived
from a forecast. In contrast, a real-time agent optimizes around the actual
constraints of distribution centers, production capacity, material availability
and transportation alternatives across the supply chain. The promising agent
takes a customer order and, in real time, presents the user with a set of
sourcing options, including a promise date and overall cost. This allows
a company to exploit several benefits.
Improves delivery performance. The promising agent enables sales
operations personnel to provide real-time responses to customer requests.
Promise dates are determined (in assemble-to-order or make-to-stock environments)
while the customer is on the phone, with the agent evaluating alternative
sourcing options across the supply chain. The system presents users with
the delivery timing and costs (including manufacturing, freight and duty)
associated with each sourcing option. Thus, promises are made immediately,
based on actual material and capacity available, which enables more reliable
and faster delivery commitments.
Improves customer satisfaction. Since customers will promptly get
accurate answers to questions about delivery dates and costs, customer satisfaction
improves significantly. Not only will customers get immediate answers to
these questions, but they can also consider cost tradeoffs-including distribution
costs. Since resources are intelligently allocated up front, on-time shipment
performance also improves, further strengthening customer satisfaction.
Sales channel productivity also increases when non-value-added customer
"follow-up" activity is eliminated.
Optimizes supply chain assets. Efficiently allocating supply chain
assets for each order processed dramatically improves return-on-assets.
If capacity and material are available anywhere across the enterprise supply
chain, the agent will find and allocate those assets before incurring unnecessary
transportation, overtime or expediting costs. This efficient deployment
of corporationwide assets reduces costs and improves profitability.
Reduces dependency on forecast. Conventional available-to-promise
functions of an ERP system only consume forecasted demand supported by the
master production schedule (MPS). The promising agent eliminates this forecast
dependency by dynamically adjusting the MPS based on current, actual information.
Based largely on a plan, the MPS is only as reliable as its underlying forecast.
The promising agent eliminates this risk.
Promising Agents vs. Available-to-Promise
The promising agent uses transactional systems (MRP, ERP) information for
inventory management, purchasing and shop floor control as a basis for order
promising and commitment. Employing an in-memory model of the supply chain,
the promising agent will evaluate viable options and present the user with
the best date/cost alternatives. These options represent truly viable alternatives
resulting from simultaneous optimization of material, capacity, request
date and other site-specific constraints.
Existing ERP or MRP II systems, on the other hand, rely on forecasted demand
to create a production plan and a master production schedule. Traditionally,
a master scheduler reviews this multi-week plan and approves or "firms"
the planned purchase orders and production orders necessary to meet the
forecasted demand. During sales order entry, a traditional available-to-promise
calculation might check to see if the order can be satisfied by the "firm-planned"
material or production orders.
So how do promising agents stack up against conventional available-to-promise
solutions in meeting other common challenges?
Forecasts
Traditional ATP allows commitments against the planned production and planned
procurement only. If actual demand is different in product mix, sales region
or requires an earlier due date than the "firm-planned" orders
can support, a reliable delivery commitment can only be made for the total
item lead time or later. Promising agents use actual production routings,
BOMS, capacity models and real demand to quote better, more reliable promise
dates.
Assumptions
Without planned or finished goods on hand to commit, order entry personnel
must use lead time estimates to make reliable promises. The standard make
or buy lead times assume infinite capacity or availability for dates beyond
the lead time. Promising agents use actual production loading and capacity
models to quote better, more reliable promise dates.
Blind Commitments
If existing systems can confirm the availability of material or production,
and not both, one can surely "commit" the order and hope the materials
management or production team can deliver on your commitment. The promising
agent will simultaneously optimize around multiple constraints to give better,
more reliable promise dates.
"We'll Get Back to You."
This is the only appropriate response using traditional systems. Promise
only to call the prospect/customer back after the potential order can be
scheduled and a reliable delivery estimate quoted. This gives the prospect
the incentive to call competing suppliers for a firm commitment-and the
customer will likely place the order with the most convincing alternative
supplier.
Other Limits
Another critical limitation of ATP is its general focus on single plants.
Without the help of several technology enablers, conventional systems are
limited to a narrow perspective indeed. Some of these technologies include: