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" RPE is one of the only consulting companies to offer strategic, functional and technical expertise focused exclusively on the retail industry. Our global technology skills, capacity to deliver results and collaborative approach are key assets that help clients achieve success. By identifying critical issues and implementing innovative solutions, clients generate revenue, reduce costs and access the right information at the right time. "
Source :RPE
Order Forecast Collaboration: Benefits for the Entire Demand Chain
Forecast Collaboration is also known as :
Forecast Collaboration,
Forecast Pro Software,
Forecasts on Collaboration Industry,
Forecasting Software,
Order Forecasting,
Successful Order Forecast Collaboration,
Without Order Forecast Collaboration,
Demand Forecast Collaboration,
Investing in Forecast Collaboration,
Product Forecast Collaboration,
Order Forecast Collaboration Benefits,
Improve Forecast Quality,
Purchase Order Forecast Collaboration,
Efficient Forecast Collaboration,
Forecast Collaboration Process,
Supply Chain Forecast Collaboration,
Incentives to Drive Forecast Collaboration,
Forecast Collaboration One Network,
Order Forecast Collaboration WP,
Forecast Collaboration Process Component,
Deployment Order Forecast Collaboration,
Supply Chain Collaboration,
Collaborative Demand Management,
Improve Demand Forecast Quality,
Enables Web-based Collaboration,
Best Collaboration Practices,
Purchase Order and Forecast Collaboration,
Collaborative Planning Forecasting,
Forecasting Software Optimize,
Transmission Forecast Collaboration,
Initial Steps in Collaboration,
Collaboration as Platform,
Purchase Orders Forecast Transmission,
Share Forecast and Supply Planning,
Simple Web Collaboration,
Collaboration Software Market Forecast,
Forecast Web,
Collaboration Service Provider Market,
Forecast Warehouse Sales.
In the recent past, suppliers and retailers often viewed themselves as adversaries. Retailers would order what
they wanted a lead time prior to expected receipt and be upset if first time fill rates were not 100%. Suppliers
would prepare for future orders in isolation using the history of past shipments as a forecasting basis and were
constantly surprised to receive large promotional orders with little warning.
It became clear that sharing information would benefit both suppliers and retailers. Initial collaborative efforts
focused on sharing retail POS data with suppliers so they could more quickly read changes in consumer buying
patterns.
Key demand chain drivers such as future promotions or new store openings were not included when looking
purely at sales history. The collaboration evolved into sharing demand forecasts, but this approach also had its
struggles.
Forecasting future demand - either consumer sales or warehouse shipments to stores - did not provide
manufacturers and raw materials suppliers the data they needed. With collaborative demand forecasts the
retailer now had a better estimate of future outflows, but those supplying the retailer did not know how the
retailer was going to bring in product to support the future sales.
Knowing that an item will sell 50 per week to the end consumer is interesting but deceptive if the retailer
doesnt order the item for two months due to current overstocks and then orders 480 pieces to meet bracket
pricing rules. In addition, many of the tools supporting early demand collaboration were rudimentary web
comparison solutions that failed when the volume of exceptions overwhelmed the well-intentioned partners.
Benefits of Order Forecast Accuracy
Improving the accuracy of order forecasts and sharing the information throughout the demand chain increases
sales, removes unnecessary inventory and increases planning time.
Accurate order forecasts enable raw materials suppliers and manufacturers to better meet the needs of the end
consumer. Production plans can reflect the anticipated order flow and fill rates will increase. When the retailer
is able to procure the product needed to meet the end consumer demand, service levels improve and sales for
all members of the demand chain increase.
In addition to increasing sales for all members, buffer inventory levels throughout the entire system are greatly
reduced. Without visibility to upcoming orders, partners must prepare for the largest order received lately or
risk losing sales. When partners prepare for the unknown by holding additional product, buffer inventories
grow.
Because much of the supply and demand variance is removed with the introduction of accurate order
forecasts, the safety stock inventory necessary to hedge against uncertainty is reduced. With the risk of
unusually large orders being received without notice removed, buffer inventory can be reduced and profitability
increased.
Collaboration on order forecasts enables changes to forecasts to be immediately visible and actionable. For
example, once a raw materials supplier knows of a shortage in a key product component, that information
can be shared with the demand chain partners through order forecast collaboration. If alternative sources
for the component are unavailable, the retailer can immediately decide what consumer drivers such as price,
promotion, product placement or in-store signage should change to reduce demand and more closely match
the upcoming reduced supply. Without order forecast collaboration, the first time a retailer may know of a raw
materials shortage is when expected orders are received incomplete at the warehouse dock.
Finding a Solution
Demand chain partners need to work together to accurately estimate the order forecast variable that drives
the majority of interactions between partners. Order forecast collaboration is best addressed with use of a
software solution. While demand chain partners can create their own tools and processes, solutions such as
JDA ®s Electronic Dynamic Agreement already have proven successful in the retail arena.
Whether using an existing tool or creating a new one, the ideal order forecast collaboration tool:
- Creates item order forecasts as far into the future as is actionable by the partners.
- Leverages the same logic and rules as actual purchase orders.
- Self-adjusts order forecasts within preset tolerances as market conditions change.
- Supports user collaboration on order forecasts with ability to propose manual changes.
- Links to execution by creating purchase orders from order forecasts.
- Measures accuracy of the order forecasts compared to actual orders placed.
Order forecasts are driving item production decisions and the upstream procurement of raw materials.
Because of these item level activities, order forecasts should be created at the item level. In addition, order
forecasts are only actionable within a certain timeframe that is unique to each partnership. Creation of several
years of order forecasts may prove useful for the long term planning teams at a manufacturer or retailer, but
the time and effort to create these accurate order forecasts often outweighs the benefit of planning accuracy.
Larger gains from order forecasts are realized when partners make actionable decisions such as purchasing
raw materials or beginning production of certain product lines. These decisions are most often made several
months - not years - ahead of expected delivery.
Too often shortcuts are used that create order forecasts differing from actual order patterns. Order forecasts
must use the same logic as actual orders. If forecasts are based on a different set of logic than the real
purchase orders, avoidable variance has been introduced into a process where the intent is to predict future
actions with reduced error.
In addition, order forecast collaboration should minimize manual intervention and workloads by automating
forecast updates within tolerances and notifying users of changes desired outside of tolerances. These
tolerances should vary based on the time remaining before an order forecast becomes an actual purchase
order and ships to the retailer.
In the example below, order forecasts may have an allowable variance of 50% when the order forecast is
14 weeks or further away from shipment. That variance level may reduce to 20% when forecasts are four to
13 weeks away from shipping and orders three or fewer weeks away from shipment may have all automated
forecast adjustments locked.
Even with automated adjustments within tolerances, there will be times when partners need to manually change
the system-calculated order forecasts because of information not available in the system. Plant closures and
out of stock situations are common examples of business conditions impacting order forecasts.
Partners need to easily collaborate on these known issues and order forecast collaboration solutions should
support a request and approval process for changes. This enables demand chain partners to collect all useful
supply and demand information in one place yet still preserve order forecast accuracy measures.
Order forecasts should also drive actual order quantities. If trading partners have adjusted system quantities
over the past few months on order forecasts they will build inventories and sales plans supporting those
decisions. If actual orders are generated that are not reflective of these order forecasts, order forecast
accuracy decreases and trust in the systems is lost. When order forecasts use the same logic as orders,
solutions can enable a direct link to execution by turning order forecasts into actual purchase orders when
receipt requirements move within a lead time.
The End Result
Order forecast accuracy measures reflect how closely actual orders match the order forecasts used to prepare
for the actual orders. Without a high accuracy, partners will not take action on the forecasts and benefits
are lost. Accuracy measures must account for both quantity variance and timing variance. These variance
measures should use standard statistical formulas (such as MAPE - Mean Absolute Percentage Error) and
should be measured over different time intervals that carry meaning for demand chain partners.
For example, if an October shipment has been forecasted to be 1000 units for the past three months the
demand chain may order raw materials and schedule production time to support the upcoming order for 1000.
If the retailer actually orders only 250 units and does not notify the supplier until the PO is cut, demand chain
partner faith in future order forecasts is lost.
Order forecast collaboration focuses partnership efforts on the most critical forecast component across the
demand chain. Accurate order forecasts drive sales increases, inventory decreases and better decisions. With
so many benefits across the entire demand chain, shouldn't you look into order forecast collaboration with your
partners?
About the Author
John Schwechel is the Replenishment Practice Lead and Senior Project Manager with Retail Process
Engineering, LLC. His background at Target Stores, Andersen Consulting, E3 Corporation, JDA Software and
Retail Process Engineering (RPE) gives him unique insight into retail change initiatives and their success
factors. Visit www.rpesolutions.com to learn more.