EXCELLENCE IN FORECASTING, PLANNING, AND SCHEDULING

ROHAN NAKRANI

Team Leader

Creative Path Solutions

303 Jolly Plaza, Athwagate, India, Gujarat, Surat-395002

Responsibilities include overall development of project, interacting with the clients and

presenting them with the status updates, database schema development and maintenance,

overseeing software codes, maintaining inventory system.

EXCELLENCE IN FORECASTING, PLANNING, AND SCHEDULING

Abstract

The evolution of modern technology has provided means to forecast the consumer

demand, plan the resource required to fulfill the demand and schedule the facilities

needed to produce the product or provide service. Manufacturing industries and service

providers use forecasting methods, planning and scheduling techniques as an integral part

of supply chain management to manage inventory, design work schedules and offer

seasonal variations in product and services. This term paper will give the detailed

information about methods and techniques used by many big firms to forecast the

demand of their product and services, plan resource allocation to meet demand, and

schedule the labor, equipment, and facilities needed.

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Forecasting

Forecasting can be broadly considered as a method or a technique for estimating many

future aspects of a business or other operation. Planning for the future is a critical aspect

of managing any organization, and small businesses are no exception. Forecasting is used

by companies to determine how to allocate their budgets for an upcoming period of time.

This is typically based on demand for the goods and services it offers, compared to the

cost of producing them. Investors utilize forecasting to determine if events affecting a

company, such as sales expectations, will increase or decrease the price of shares in that

company. Forecasting also provides an important benchmark for firms that have a long-

term perspective of operations. Below are the examples of some big firms that tackled

their problems with the support of forecasting techniques.

Case Study- Futron

The FAA is the United States government agency responsible for regulating and

promoting commercial launches, spaceports, and commercial human spaceflight. Futron

provides the Federal Aviation Administration with an annual forecast of non-

geostationary launch demand (www.futron.com). To accurately plan, prepare, and

formulate decisions, the FAA depends on a detailed and accurate forecast of launch

activity for the next ten years.

Problem

The major challenges of accurately forecasting commercial launch activity include

uncertainty inherent in the global nature of the industry, as well as the interplay between

commercial and government sponsored space launch.

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Solution

The FAA and Futron collaborate to develop an optimal forecast methodology that

includes utilization of Futron’s unique resources, experience, and global industry

contacts. The forecasts foundation is derived from Futron’s Electronic Library of Space

Activity (ELSA) database.  ELSA contains all launch vehicles and satellites launched

globally since sputnik including satellite and launch vehicle capability, lifespan,

reliability, cost, and much more. The forecast utilizes Delphi forecasting method that

includes interviews with spacecraft manufacturers, satellite operators, launch providers,

and regulators.

Although no forecast is ever perfect, Futron’s forecasting methodology lays out the

known sources of uncertainty and the possible magnitude of influence that each could

cause in the forecast. This transparent approach provides the FAA, and the industry that

rely on the launch forecast, with not only a transparent quantitative methodology but also

insight into the cycles and relationships that influence the dynamics of the industry.

Result

The FAA forecast is used by government and industry within the United States and

globally.  FAA forecast data is routinely cited in publications from around the world.

Heineken

Heineken is an international brewer based in Amsterdam with over 200 brands of beer

sold in over 100 countries and with annual revenues over €14,000 million Euros

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(www.forbes.com). Heineken ships full container loads directly from the ports to the

warehouses of its largest distributors; for smaller distributors loads are shipped from

Heineken demand centers near the ports.

Problem

A depletion forecast showing how much beer distributors are currently and historically

selling to retailers is converted to a sales demand forecast, which indicates the amount of

inventory the distributors will be buying from Heineken. The sales demand forecast is

then used in Holland to create a production/replenishment plan for product supply.

Distributors order beer over the Internet on a weekly basis at which time they also

provide their three-month depletion forecast, which is a major input to the demand

forecast that drives production in Holland. One week later they receive delivery from a

port or demand center. Retailers order directly from the distributors. Since lead times are

long (four to six weeks) and many consumers will switch beers if it is out of stock,

forecasting at Heineken is critical. Under-forecasting results in lost sales; over-

forecasting results in costly excess inventory, which becomes obsolete and is destroyed if

it sits in inventory too long and is not absolutely fresh.

Solution

The technique used by Heineken for demand forecasting was unreliable and often leaded

to either over-forecasting or under-forecasting. In order to tackle this problem Heineken

used weighted moving average forecasting along with exponential smoothing.

Promotions and seasonal sales were factored into this demand forecast. Heineken also

used Delphi method for acquiring informed judgments of future trend.

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Result

As a result the forecast accuracy improved form 62 percent to 74 percent in two years.

The Company’s revenue grew by 1.3 percent. The Company’s sales increased by 2.9

percent and sale profit increased by 3.5 percent because of minimized cost for inventory

warehousing due to reliable forecast (www.businessinsider.com).

Planning

A resource planning addresses how the project will be resourced and what supporting

services, infrastructure and third party services will be required. Resource planning

enable organizations to maximize resource utilization, balance supply and demand and

plan resources over the entire project lifecycle. Companies may also use resource plans to

identify resources, which could include human, capital, time, or needed to achieve

strategic goals.

Boeing and Alcoa

Boeing is the world’s largest manufacturer of commercial jetliners with total revenues of

approximately $55 billion, and over 154,000 employees in 48 U.S. states and 67

countries (www.forbes.com). A key stage in its large and complex supply chain is its

Skin and Spar business unit, which supplies large wing products for most of Boeing’s

commercial jets. One of the Skin and Spar unit’s major raw material suppliers is Alcoa,

which is the world’s leading producer of fabricated aluminum with 130,000 employees

and over $26 billion in total revenues (www.businessinsider.com).

Problem

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The two companies experienced difficulties with its supply chain relationship that

resulted in unreliable deliveries, and shortages of some parts and excessive inventories of

others. The primary difficulties they faced were the mismanagement of inventory and

unreliable resource planning system.

Solution

Boeing and Alcoa used a team approach to find a solution, which, in the end, was the

implementation of ERP technique. Enterprise resource planning (ERP) is business

process management software that allows an organization to use a system of

integrated applications to manage the business and automate many back office functions

related to technology, services and human resources. ERP also integrates all facets of an

operation, including product planning, development, manufacturing, sales and marketing.

Under Enterprise Resource Planning technique, Boeing was able to optimize inventory

levels based on customer-buying habits. Both companies also implemented a vendor

managed inventory (VMI) program that was based largely on ERP process. This program

included an integrated information system and the real-time exchange of data to tighten

the linkage between both inventory management and resource planning operations.

Boeing started sending electronic data related to current issues and inadequacies directly

to Alcoa on weekly basis. Boeing increased its product delivery accuracy by making

production plans visible to everyone in the supply chain at Alcoa, so Alcoa would know

when changes were made in the production plan that were not reflected in the ERP model

—for example, when parts were pulled out of buffer stocks, what type of parts were

puller out, how many parts of each type were pulled out.

Result

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With the help of this approach Alcoa was able to get real time information about the

demand of parts and could start manufacturing them on the basis of demand. As a result

the cost of storing excessive inventory was minimized. The shortage of some parts were

taken care of and made available on timely basis.

Hershey’s

Problem

Hershey’s spent three years and $115 million working to replace its many legacy systems

with one integrated enterprise resource planning (ERP) system from market leader SAP.

Delays caused the system to go live during the busy fall season, in one fell swoop across

the enterprise. Problems ensued in order processing and shipping such that Hershey’s

warehouses piled up with undelivered Kisses, Jolly Ranchers, and peanut butter cups,

while store shelves were packed with competitor products. Hershey's missed the entire

Halloween season— causing sales to drop by 12% in the third quarter, earnings by 18%,

and stock prices by 35%. Hershey’s was not alone in its ERP implementation problems—

Nestle, Whirlpool, Dow Chemical, Boeing, Dell, Nike, and others followed suit.

Solution

Three years later, Hershey's ERP system was successfully implemented and upgraded.

The production line was optimized and lead-time delays were minimized causing the over

reduction in time from the starting phase of production to the final phase of finished

product. Hershey also optimized transportation routes to ensure that there is no delay in

distributing products across the stores.

Result

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This resulted in significant improvements to its core processes, reduced costs, faster

processing times, and a near-zero-defect production environment. Today, ERP has

become the mandatory standard for enterprise computing. Many firms are using ERP to

optimize their product planning, sales and market planning and optimizing manufacturing

process.

Scheduling

Scheduling involves establishing the amount, of work to be done and the time each

element of the work will start, or the order of work. This includes allocating the quality

and rate of output of the plant or department and also the date or order of starting of each

unit of work at each station along the route prescribed (Lansburgh, 1995)

Scheduling is difficult in many cases because variety of jobs are processed, each with

distinctive routing and processing requirements. In addition, although the volume of each

customer order may be small, there are probably a great number of different orders in the

shop at any one time. This necessitates planning for the production of each job as it

arrives, scheduling its use of limited resources, allocating necessary equipment for its use

and monitoring its progress through the system.

Deere & Co.’s Moline

Problem

Deere & Co.’s Moline, Illinois, plant sported the latest machinery, self-managed work

teams, and spotless surrounding and still the plant had trouble meeting deadlines. Deere

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made parts for seed planters at Moline, which were shipped to dealers and assembled

there to customer order. As the product became more complex, dealers couldn’t handle

the intricate assembly. Now the Moline plant manufactures the parts and assembles them.

A planter has a steel frame with 4 to 32 row units, each holding seeds and fertilizer.

Controls can be hydraulic or electronic at various levels of sophistication. With over 300

options, millions of combinations are possible. Deere found that even scheduling the

5000 most likely configurations was unmanageable.

Fabrication work takes considerably longer than assembly. For example, welding an

extra-large planter can take an hour or more, creating quite a bottleneck. The sequence in

which planters are assembled is important too, as completed machines leave the assembly

line and move directly to waiting trucks. Planters going to the same location need to

come off the assembly line at approximately the same time. Demand varies so widely that

patterns detected one month are no longer valid the next. The Moline plant averaged a

500-order backlog, orders that farmers could cancel at any time.

Solution

In order to solve the problem the company came up with the solution method called

Generic Algorithm. Based on the theory of natural selection, genetic algorithms (GA)

change or mutate the sequence of items assembled until a satisfactory schedule is

determined, then keep experimenting until a good schedule emerges. Details about the

production process and factors that make a good schedule are assigned point values that

influence their selection in GA. The computer system tries different sequences of orders,

observes the results, and learns whether to keep certain characteristics in the offspring of

the next generation. As the program runs, red, yellow, and blue lights flash on the

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computer screen—red for trouble, yellow for better, and blue for best. It takes about an

hour to generate a two-week schedule that increases output and uses less overtime than

the old schedule (which took three days to calculate).

Result

Genetic Algorithms now schedule tractors and $200,000 combines at other Deere plants.

Companies like GM, GE, Motorola, and Caterpillar also use Genetic Algorithm, to cut

inventories, shorten assembly time, and boost yields.

Ghirardelli Chocolates

The Ghirardelli Chocolate Company, headquartered in San Francisco, produces over 45

million pounds of premium chocolate each year. The Company manufactures and

markets its own chocolate fromraw cocoa beans to finished product, using proprietary

methods of blending, roasting, and processing to create over 400 unique chocolate

products.

Problem

A monthly requirement plan generated from the Company’s ERP system was

downloaded into Excel spreadsheets for managers to manually build production

schedules. The manual production schedules assigned work to machines, but did not

schedule labor or consider an efficient sequence of product changeoverson the production

lines.

Solution

Recently, Ghirardelli began using a specialized factory scheduling system to streamline

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production, control costs, and respond to changes in demand. The new system, called

Factory Scheduler, was designed for process industries where production is based on

formulas or recipes, and where concerns about quality and inventory spoilage are

paramount. Factory Scheduler worked seamlessly with the ERP system through XML

interfaces to provide production schedules by week, shift, and line. These schedules were

used to project labor requirements, optimize changeovers, and adjust production when

unplanned events occurred, such as outages, labor shortages, or changes in demand.

Ghirardelli planners could take the schedule as given, or experiment with alternative

ways to smooth production and meet demand.

Result

With more efficient scheduling, Ghirardelli was able to increase production volume,

reduce labor costs, and be more responsive to customer requests. That's especially

important in an industry where shelf life is limited and demand is seasonal.

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References

Andrew Osterland, Blaming ERP, CFO Magazine, January 1, 2000;

Andy McCue, IT Glitch Results in Cadbury Chocolate Glut, CNET News, (June 8,

2006)

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scenarios-have-benefited-companies-in-times-of-crisis/

Brian Dershem, Financial Incentives Improve he Supply Chain: Heineken’s Journey,

The Journal of Business Forecasting, vol. 26 (2007).

C. C. Poirier, Forecasting, Demand Management and Capacity Planning (2009).

Ciriello D. M., Project Schedule Strategies: Helpful Hints for Project Managers and

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Shelf/Scheduling-Management.aspx

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from http://www.fortune.com.

Ghirardelli Chocolate uses One Plan Factory Scheduler to Reduce Costs and

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Systems (2006), retrieved from

http://www.cdcsoftware.com.cn/downloads/pdf/CS_Ghirardelli.pdf.

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http://www.boeing.com/boeing/commercial/cmo/

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