Gantt Chart Simulation for Stock Flow Consistent Production Schedules

Gantt Chart Simulation for Stock Flow Consistent Production Schedules

 

I have knowledge of two software which do Gantt chart simulation for production scheduling.  These are used by top most companies in the world for production planning and scheduling now a days known as Supply Chain Management (SCM).

Production Schedules are stock flow consistent which means that starting inventories, and unused production of products result in cumulative inventory which is plotted for each of the product.

Production and Shipments (arrivals and dispatched) create Flows and Inventory levels indicate Stock level positions.

Gantt Chart simulators are excellent tools for operations management in plants.

The first Gantt chart was actually developed by Karol Adamiecki in Poland.  He called it a Harmonogram.  Henry Gantt in 1910 published first gantt chart which was later than publication by Karol Adamiecki.

These two charts below show Simulator window in which Gantt chart and inventory level plots are displayed.

Gantt Chart Simulator in Aspen Tech Plant Scheduler for Production Scheduling

active-guidance_10740930

 

Gantt Chart Simulator in Atlantic Decision Sciences Scheduler

Scheduling Board Single Chart

Key Sources for Research:

 

A Presentation by Chris Jones on Evolution of Graphical Production Scheduling Software

at the Cornell University Deptt of ORIE

 

 

 

Atlantic Decision Sciences

http://atlanticdecisionsciences.com

 

 

Aspen Technology

http://aspentech.com/products/aspen-plant-scheduler/

 

 

History of Gantt Chart

http://www.ganttchart.com/orgforwork.html

 

 

History of Production Scheduling

http://www.springer.com/cda/content/document/cda_downloaddocument/9780387331157-c1.pdf?SGWID=0-0-45-321351-p148129370

 

 

The harmonogram: an overlooked method of scheduling work.

Marsh, E. R. (1976).

Project Management Quarterly, 7(1), 21–25.

https://www.pmi.org/learning/library/harmonogram-overlooked-method-scheduling-work-5666

 

The Harmonogram of Karol Adamiecki

Edward R. Marsh

http://amj.aom.org/content/18/2/358

 

Karol Adamiecki

https://www.pocketbook.co.uk/blog/tag/karol-adamiecki/

Production and Distribution Planning : Strategic, Global, and Integrated

Production and Distribution Planning : Strategic, Global, and Integrated

 

Multiple Perspectives on production and distribution planning

  • Plant and Distribution Center Location problem – Strategic – Structural and Design
  • Procurement problem – where to source from – Tactical – Allocation, Assignment
  • Production and Distribution Scheduling – Operational  – Managing Flows
  • Multi Echelon Inventory Management- Operational – Managing Stocks
  • Supply Chain Integration, Collaboration, Coordination – Hierarchical Planning

Normally, production and distribution planning are handled separately in firms.  Integrated planning of production and distribution can add significant value to a company, particularly, in strategic decisions.

 

From Facility Location and Supply Chain Management – A comprehensive review

Since, in the literature, model objectives change as a function of the planning horizon length, we consider it opportune to define the features of each horizon in order to contextualize the parameters chosen for the models’ comparison. According to [14], the planning horizons of the supply chain can be clustered as follows:
Strategic planning: this level refers to a long-term horizon (3-5 years) and has the objective of identifying strategic decisions for a production network and defining the optimal configuration of a supply chain. The decisions involved in this kind of
planning include vertical integration policies, capacity sizing, technology selection, sourcing, facility location, production allocation and transfer pricing policies.
Tactical planning: this level refers to a mid-term horizon (1-2 years) and has the objective of fulfilling demand and managing material flows, with a strong focus on the trade-off between the service level and cost reduction. The main aspects considered in tactical planning include production allocation, supply chain coordination, transportation policies, inventory policies, safety stock sizing and supply chain lead time reduction.
Operational planning: this level refers to a short term period (1 day to 1 year) and has the objective of determining material/logistic requirement planning. The decisions involved in programming include the allocation of customer demands, vehicle routing, and plant and warehouse scheduling.

 

From

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From  Integrated Location-Production-Distribution Planning in a
Multi products Supply Chain Network Design Model

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Key words:

  • ‘supply chain strategic design’,
  • ‘supply chain planning’,
  • ‘supply chain optimization’,
  • ‘supply chain network design’,
  • ‘supply chain production planning’,
  • ‘supply chain delocalization’,
  • ‘logistic network design’,
  • ‘facility location’,
  • ‘distribution network design’,
  • ‘production-distribution systems’,
  • ‘location-allocation problem’,
  • ‘supply chain linear programming’
  • ‘supply chain mixed-integer programming’.

From  From Manufacturing to Distribution: The Evolution of ERP in Our New Global Economy

Over the past fifty years, manufacturing has changed from individual companies producing and distributing their own products, to a global network of suppliers, manufacturers, and distributors. Efficiency, price, and quality are being scrutinized in the production of each product. Because of this global network, manufacturers are competing on a worldwide scale, and they have moved their production to countries where the costs of labor and capital are low in order to gain the advantages they need to compete.

Today, the complex manufacturing environment faces many challenges. Many products are manufactured in environments where supplies come from different parts of the world. The components to be used in supply chain manufacturing are transported across the globe to different manufacturers, distributors, and third party logistics (3PL) providers. The challenges for many manufacturers have become how to track supply chain costs and how to deal with manufacturing costs throughout the production of goods. Software vendors, however, are now addressing these manufacturing challenges by developing new applications.

Global competition has played a key role in industrialized countries shifting from being production-oriented economies to service-based economies. Manufacturers in North America, Western Europe, and other industrialized nations have adapted to the shift by redesigning their manufacturing production into a distribution and logistics industry, and the skills of the labor force have changed to reflect this transition. Developing countries have similarly changed their manufacturing production environments to reflect current demands; they are accommodating the production of goods in industries where manufacturers have chosen to move their production offshore–the textile industry being a prime example of this move.

A report from the US Census Bureau titled Statistics for Industry Groups and Industries: 2005 and another from Statistics Canada titled Wholesale Trade: The Year 2006 in Review indicate that wholesalers are changing their business models to become distributors as opposed to manufacturers. Between 2002 and 2005, overall labor and capital in the manufacturing sectors decreased substantially. US industry data (from about 10 years ago) indicates that the North American manufacturing industry was engaged in 80 percent manufacturing processes and only 20 percent distribution activities. Today, however, these percentages have changed dramatically; the current trend is in the opposite direction. Manufacturing processes account for around 30 percent of the industry processes, and wholesale and distribution activities, approximately 70 percent.

In addition, a report from the National Association of Manufacturers indicates that the US economy imports $1.3 trillion (USD) worth of manufactured goods, but exports only $806 billion (USD) worth of goods manufactured in the US. This negative trade balance is a clear indication of the changing economic trend toward the manufacturing of goods in low-cost labor nations.

The main reason for this huge manufacturing shift is the increasing operating costs of production in industrialized countries. These rising costs are forcing manufacturers to move their production to developing nations because of the low cost of labor in these countries. This includes Asian countries (such as China and Indonesia) as well as Eastern European countries (such as the Czech Republic and Slovakia).

The number of workers (in percentages) in specified industries in G7 countries, and uses 1980 as the base year with 100 percent full employment in each industry. The industries with relatively constant rates of employment are the food and drink and the tobacco industries. Since 1995, all other industries have been maintaining less and less manufacturing employees, as indicated by the declining slopes in the graph. The shift in the textiles and leather, metals, and other manufacturing industries is moving toward production of goods in low-wage, developing countries.

Manufacturing is a global industry, and although a manufacturing company may be based in an industrialized country, it may have the bulk of its manufacturing facilities in a developing country. Producing goods in such a country reduces wage and capital costs for the manufacturer; however, some manufacturing control is lost in offshore production. Shipping, distribution, and rental costs, for example, are often difficult to track and manage, and quality control can be compromised in a production environment that is not local.

Two main outcomes can be seen within the manufacturing industry because of this manufacturing shift: manufacturers have a sense of having relinquished control of their production to low-cost labor nations, and supply chain management (SCM) has now become the answer to manufacturing within industrialized nations.

Suppliers that provide components to manufacturers often have issues with quality. Being part of a large network of suppliers, each supplier tries to offer the lowest prices for its products when bidding to manufacturers. Although a supplier may win the bid, its products may not be up to standard, and this can lead to the production of faulty goods. Therefore, when using offshore suppliers, quality issues, product auditing, and supplier auditing become extremely important.

Because the manufacturing model is changing, manufacturing has become more of a service-based industry than a pure manufacturing industry. Even though the physical process of manufacturing hasn’t changed, the actual locations of where the goods are being produced have. This fact is now compelling industrialized countries to engage in more assembly driven activities–a service-based model. The manufacturing process has transformed into obtaining parts and reassembling them into the final product. The final product is then redistributed throughout the appropriate channel or to the consumer. SCM methods are now reacting to this change as well; they are taking into account final assembly needs, and they are distributing particular products to consumers or manufacturers.

SCM is becoming the norm for manufacturers in the industrialized world. Offshoring is now standard practice, and methods such as SCM have been set up to deal with these economic and logistical business realities.

The economic shift happening in both industrialized and developing countries is dramatic. As the level of management knowledge increases, better methods of constructing offshore products are available in SCM solutions. In both types of economies, the changes in the labor force skill sets and manufacturing environments have consequently led to new software solutions being developed in order to manage this dramatic change.

Within the software industry, many SCM and enterprise resource-planning (ERP) vendors are following the economic shift. They are developing new functionality–ERP-distribution software–to meet the recent demands and needs of the changing manufacturing and distribution industries.

SCM and ERP software are converging to better address these new demands in the manufacturing industry. In the enterprise software market, ERP software vendors have reached a point of saturation; their installs are slowing down and they are seeing a reduction in sales. Therefore, ERP providers are developing new functionality in order to remain competitive with other ERP vendors, in addition to looking for new opportunities. ERP vendors are trying to adapt to the changing market in order to increase their revenues. They are integrating SCM functionality into their ERP offerings, creating ERP-distribution software that can span the entire production process across many continents (if necessary), and that is able to track final goods, components, and materials.

Traditional ERP solutions included some SCM functionality, which was needed to distribute the companies’ produced goods. These systems also allowed components and parts to be imported in order to assemble these goods. But offshore manufacturing and expansion into new markets has required SCM functionality in ERP software to be extended. Some larger vendors have acquired other companies in order to meet these changing demands. For example, Oracle acquired G-Log, a transportation management systems (TMS) vendor, and Agile, a product lifecycle management (PLM) vendor; and Activant acquired Intuit Eclipse.

SCM software vendors, in contrast, have felt encroached upon by ERP vendors. The situation has posed a real threat to SCM providers in the market, forcing them to extend their ERP functionality to compete with ERP vendors and to try to gain new clients in the distribution and logistics industry.

ERP-distribution software has integrated SCM functionality into its existing functionality to navigate through the complex global manufacturing environment. SCM software maps five processes into one solution: planning, sourcing (obtaining materials), producing, delivering, and returning final products if defective. These processes help to track and manage the goods throughout their entire life cycles. In addition, ERP solutions are used to manage the entire operations of an organization, not only a product’s life cycle. This gives users the broad capability to manage operations and use the SCM functionality to manage the movement of goods, whether components or finished product.

With the ability to gain accurate inventory visibility and SCM production, ERP-distribution software is able to see the whole chain of manufacturing and distribution events, from supplier to manufacturer, all the way to the final consumer.

There are three business models.

  • The first is the SCM model, which includes the manufacturing process.
  • The second is the retail model, which is the distribution of final products to the consumer, business, or retailer.
  • The third model is a combination of the first two business models, joined by the ERP-distribution software solution into one seamless process.

Within the SCM process, goods can either be brought in (imported) through foreign manufacturers, or acquired locally. The goods are then given to a distributor, 3PL provider, or wholesaler in order to reach the final client.

Within the retail model, the products are taken from a distributor, 3PL provider, or wholesaler, and are distributed to the appropriate person. Note that there is a “shift” for the consumer. This is to indicate that through the Internet or other forms of technology, consumers are now able to buy directly from distributors. The power of the consumer has changed; where manufacturers once provided products to consumers, consumers are now creating demand, and manufacturers have to meet that demand.

SCM solutions focus on the relationship between the supplier and manufacturer. However, ERP- distribution software has taken functionality from SCM software and combined it with retail software (such as point-of-sale and e-commerce solutions); it is now able to span across the entire supply chain and to track goods along the complete manufacturing process.

This is a simplified view of the complexities of today’s manufacturing processes. These complexities have made it crucial for trading partners to unite with manufacturers in order to help alleviate the frustrations that can occur within this global network. Specifically, trading partners are coming together with manufacturers to unite services, products, and customer experience so that business processes (such as manufacturing and distribution) become more efficient and that goods can move through these processes with minimal problems.

SCM can be thought of as the management of “warehousing processes,” in which the movement of goods occurs through multiple warehouses or manufacturing facilities. Tracking the costs of moving products and components through the maze of warehousing and manufacturing facilities is a tricky process, and many organizations lose money at each warehousing step.

Within the flow of goods in the manufacturing sector, the warehouse is a crucial part of the supply chain. Traditionally, the warehouse has been a source of frustration because the manufacturer or supplier pays for the use of the warehouse (whether owned or rented by the company). This leads to two possible scenarios: 1) the costs of the warehouse are incurred by a 3PL or manufacturing company, or 2) the costs are passed from one warehouse to another warehouse, and the original warehouse charges for these costs.

The typical warehouse process includes the following steps: receiving, put away, picking, kitting, packing, repacking, cross-docking, and shipping. ERP-distribution software is able to track costs across the entire organization and to aid companies in reducing costs that were previously tough to track.

ERP-distribution system encompasses the entire production of the final good. The ERP- distribution system is able to include inventory visibility from points “A to Z” (start to finish) and to track each warehouse cost from supplier to manufacturer to user, whether consumer, business, or retailer.

The Final Word: ERP-distribution software has been developed to meet the growing needs of the manufacturing and distribution industries. The capabilities incorporated into the software work across entire organizations, and even across continents.

Because of the economic shift in the manufacturing industry, the emergence of new software has been vital for businesses to stay competitive, meet the industry demands and emerging shift, and to keep business processes efficient to gain better profit margins.

ERP-distribution software is able to track the processes of manufacturing goods and distributing components, even if the manufacturer has facilities in North America and the Far East. With the SCM component in ERP software, manufacturing and tracking goods becomes manageable. Distributors and manufacturers can now work together in order to better meet customer requirements.

In addition of factors for domestic location selection analysis, other factors in international location selection are:

  • Exchange Rates
  • Taxes and Tariffs
  • Transfer Prices

How do companies in Computers, Automotive, Apparel, Electronics, Consumer Goods, Machinery manage their supply chain planning functions?  What software do they use for forecasting, planning, and scheduling?

I know of these software solutions for Network Design and Optimization:

Key Sources of Research:

 

Combined Strategic and Operational Planning – An MILP Success Story in Chemical Industry

Josef Kallrath

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.506.4194&rep=rep1&type=pdf

 

 

Planning in the Process Industry

Josef Kallrath

Click to access kallrath2008d.pdf

 

Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization

Josef Kallrath

Click to access kallr05a.pdf

 

 

Mathematical Programming Models and Formulations for Deterministic Production
Planning Problems

Yves Pochet

Click to access Pochet.pdf

 

Supply Network Planning and Plant Scheduling in the Chemical-Pharmaceutical Industry – A Case Study Investigation

Gang Yang, Martin Grunow and Hans-Otto Guenther

Click to access SNPandPSinCPI2003.pdf

 

 

Advanced Planning and Scheduling Solutions in Process Industry

Editors: Günther, Hans-Otto, van Beek, Paul (Eds.)

http://www.springer.com/la/book/9783540002222

 

Advanced Planning and Scheduling in Manufacturing and Supply Chains

Authors: Mauergauz, Yuri

http://www.springer.com/la/book/9783319275215

 

 

Centralised supply chain master planning employing advanced planning systems

Martin Rudberga* and Jim Thulin

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.177.7313&rep=rep1&type=pdf

 

 

Planning and Scheduling in Supply Chains: An Overview of Issues in Practice

Stephan Kreipl • Michael Pinedo

Click to access 2004-01-Kreipl.pdf

 

 

Sales and operations planning in the process industry

Sayeh Noroozi

Joakim Wikner

Click to access Salesandoperationsplanningintheprocessindustry.pdf

 

 

Optimal planning in large multi-site production networks

Christian H. Timpe, Josef Kallrath

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.565.6621&rep=rep1&type=pdf

 

 

Mixed Integer Optimization in the Chemical Process Industry –
Experience, Potential and Future Perspectives

Josef Kallrath

Click to access kall00c.pdf

 

Planning and scheduling in the process industry

Josef Kallrath

2002

https://pdfs.semanticscholar.org/79f2/bba952f67315ccfd639ce874f966b02d1c18.pdf?_ga=2.18515577.1763587969.1506656275-754417939.1465928807

 

Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms

Marc Goetschalckx  Carlos J.Vidal, Koray Dogan

Click to access 09e4150b3dc45e40ef000000.pdf

 

 

Strategic Analysis of Integrated Production- Distribution Systems: Models and Methods

Morris Cohen and H Lee

1988

Click to access 554578ab0cf23ff71686afbc.pdf

 

 

Integrated production/distribution planning in supply chains: An invited review

Sß. Selcßuk Erengucß a, N.C. Simpson b, Asoo J. Vakharia

1999

Click to access 1999_EJOR.pdf

 

 

A Review of Integrated Analysis of Production-Distribution Systems

Ana Maria Sarmiento, Rakesh Nagi

1999

Click to access ana.pdf

 

Managing Perishability in Production-Distribution Planning: a discussion and review

P. Amorim H. Meyr C. Almeder
B. Almada-Lobo

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.475.3138&rep=rep1&type=pdf

 

 

Input-Output Analysis For Multi-location Supply Chain Management Control:
A Theoretic Model

Wang Lu, Tong Rencheng

Click to access Wang-274.pdf

 

 

Using Operational Research for Supply Chain Planning in the Forest
Products Industry

Sophie D’Amours

Mikael Ro¨nnqvist

Andres Weintraub

http://repositorio.uchile.cl/bitstream/handle/2250/125029/DâAmours_Sophie.pdf?sequence=1

 

 

Mathematical programming models for supply chain production and
transport planning

Josefa Mula *, David Peidro, Manuel Díaz-Madroñero, Eduardo Vicens

2010

Click to access 83f7e2405a9539c86dd593f5bb064f2695d5.pdf

 

 

Formation of a strategic manufacturing and distribution network
with transfer prices

Renato de Mattaa, Tan Millerb

Click to access 28a955be33b7a19b2077402d5b3b9cca1151.pdf

 

 

MEASURING THE IMPACT OF TRANSFER PRICING ON THE CONFIGURATION
AND PROFIT OF AN INTERNATIONAL SUPPLY CHAIN: PERSPECTIVES FROM
TWO REAL CASES

Marc Goetschalckx, Carlos J. Vidal and Javier I. Hernández

Click to access arq0310.pdf

 

 

Integrated Strategic Planning of Global Production Networks and Financial Hedging
under Uncertain Demands and Exchange Rates

Achim Koberstein,
Elmar Lukas,
Marc Naumann

Click to access 10.1007%2FBF03342750.pdf

 

 

 

The Design of Robust Value Creating Supply Chain Networks:  A Critical Review

Click to access CIRRELT-2008-36.pdf

 

 

 

 

Global supply chain design: A literature review and critique.

Meixell, M. J. and Gargeya, V. B.

(2005).

Transportation Research Part E: Logistics and Transportation Review, 41(6): 531-550.

Click to access V_Gargeya_Global_2005.pdf

 

 

 

A strategic model for exact supply chain network design and its application to a global manufacturer

C. Arampantzi, I. Minis, G. Dikas

Click to access DeOPSys_Lab_Report_SSCND_2016-5.pdf

 

 

Sequential Vs Integrated Optimization:  Production, Location, Inventory Control and Distribution

July 2017

Click to access CIRRELT-2017-39.pdf

 

 

Measuring Cost Efficiency in an Integrated Model of Production
and Distribution: A Nonparametric Approach

Subhash C. Ray

2011

Click to access 2011-04.pdf

 

 

Optimization/simulation modeling of the integrated production- distribution plan: an innovative survey

BEHNAM FAHIMNIA, LEE LUONG, ROMEO MARIAN

2008

 

Click to access 30-587.pdf

Click to access Optimization-simulation-modeling-of-the-integrated-production-distribution-plan-An-innovative-survey.pdf

 

 

Strategic Planning and Design of Supply Chains: a Literature Review

Alessandro Lambiase, Ernesto Mastrocinque, Salvatore Miranda and Alfredo Lambiase

2013

http://journals.sagepub.com/doi/pdf/10.5772/56858

 

 

The design of production-distribution networks: A mathematical programming approach

Alain Martel

https://www.researchgate.net/publication/226891333_The_Design_of_Production-Distribution_Networks_A_Mathematical_Programming_Approach

 

 

Process industry supply chains: Advances and challenges

Nilay Shah

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.4553&rep=rep1&type=pdf

 

 

Strategic, Tactical and Operational Decisions in Multi-national Logistics Networks:
A Review and Discussion of Modeling Issues

Gunter Schmidt
and
Wilbert E. Wilhelm

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5BA6B353BBCA48D0859B902AC3F2610D?doi=10.1.1.25.4951&rep=rep1&type=pdf

Strategic production-distribution models: A critical review with emphasis on global supply chain models

 

 

Dynamics of Global Supply Chain Supernetworks

A. NAGURNEY, J. CRUZ AND D. MATSYPURA

(Received and accepted November 2002)

 

https://ac.els-cdn.com/S0895717703001122/1-s2.0-S0895717703001122-main.pdf?_tid=f781c478-a79f-11e7-b471-00000aab0f6c&acdnat=1506969295_6d30c9e8a854b9cc1ec23a57d00143d0

 

 

Integrated production/distribution planning in the supply chain: the Febal case study

Fabio Nonino

 

 

Integrated supply chain planning under uncertainty using an improved stochastic approach

Hadi Mohammadi Bidhandi a,⇑, Rosnah Mohd Yusuff

 

https://ac.els-cdn.com/S0307904X1000452X/1-s2.0-S0307904X1000452X-main.pdf?_tid=49ede574-a7a1-11e7-87fa-00000aacb360&acdnat=1506969863_699a0bd5cc6d414ed2f1caebcdda820f

 

 

Optimizing the Supply Chain of a Petrochemical Company under Uncertain Operating and Economic Conditions

Haitham M. S. Lababidi,*,† Mohamed A. Ahmed,‡ Imad M. Alatiqi,† and Adel F. Al-Enzi§

Click to access 5620c42208ae93a5c9244ea5.pdf

 

 

A strategic model for exact supply chain network design and its application to a global manufacturer

C. Arampantzi, I. Minis, G. Dikas

Click to access DeOPSys_Lab_Report_SSCND_2016-5.pdf

 

 

Sequential versus Integrated Optimization: Lot Sizing, Inventory Control and Distribution

Maryam Darvish*, Leandro C. Coelho

Click to access CIRRELT-2017-39.pdf

 

 

A MANUFACTURING ENGINEERING PERSPECTIVE ON SUPPLY CHAIN INTEGRATION

Samuel H. Huang, Ge Wang

John P. Dismukes

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.41.1852&rep=rep1&type=pdf

 

 

A review and critique on integrated production–distribution planning models and techniques

Oscillations and Amplifications in Demand-Supply Network Chains

Oscillations and Amplifications in Demand-Supply Network Chains

 

From Modeling and Measuring the Bullwhip Effect

Demand variability and uncertainty is a driver of supply chain inventory. Managing supply chains can be a challenge when demand variability and uncertainty is high. For a company in a supply chain consisting of multiple stages, each of which is run by a separate organization (or company), the variability of demand faced by this company can be much higher than the variability faced by downstream stages (where “downstream stages” refers to the stages closer to the final consumption of the product). The bullwhip effect refers to the phenomenon where demand variability amplifies as one moves upstream in a supply chain (Lee et al, 1997a, or LPW). LPW described this as a form of demand information distortion. Lee et al (1997b) further discussed the managerial and practical aspects of the bullwhip effect, giving more industry examples. The bullwhip effect phenomenon is closely related to studies in systems dynamics (Forrester, 1961; Sterman, 1989; Senge, 1990). Sterman (1989) observed a systematic pattern of demand variation amplification in the Beer Game, and attributed it to behavioral causes (i.e., misperceptions of feedback). Macroeconomists have also studied the phenomenon (Holt et al, 1968; Blinder, 1981; Blanchard, 1983).

 

From Operational and Behavioral Causes of Supply Chain Instability

 

Supply chain instability is often described as the bullwhip effect, the tendency for variability to increase at each level of a supply chain as one moves from customer sales to production (Lee et al. 1997, Chen et al. 2000). While amplification from stage to stage is important, supply chain instability is a richer and more subtle phenomenon. The economy, and the networks of supply chains embedded within it, is a complex dynamic system and generates multiple modes of behavior. These include business cycles (oscillation), amplification of orders and production from consumption to raw materials (the bullwhip), and phase lag (shifts in the timing of the cycles from consumption to materials). High product returns and spoilage are common in industries from consumer electronics to hybrid seed corn (Gonçalves 2003). Many firms experience pronounced hockey-stick patterns in which orders and output rise sharply just prior to the end of a month or quarter as the sales force and managers rush to hit revenue goals. Boom and bust dynamics in supply chains are often worsened by phantom orders—orders customers place in response to perceived shortages in an attempt to gain a greater share of a shrinking pie (T. Mitchell 1923, Sterman 2000, ch. 18.3, Gonçalves 2002, Gonçalves and Sterman 2005).

What are the causes of supply chain instability? Why does supply chain instability persist, despite the lean revolution and tremendous innovations in technology? What can be done to stabilize supply chains and improve their efficiency?

Here I describe the origins of supply chain instability from a complex systems perspective. The dynamics of supply chain networks arise endogenously from their structure. That structure includes both operational and behavioral elements.

 

From Operational and Behavioral Causes of Supply Chain Instability

Oscillation, Amplification, and Phase Lag

Exhibit 1 shows industrial production in the US. The data exhibit several modes of behavior. First, the long-run growth rate of manufacturing output is about 3.4%/year. Second, as seen in the bottom panel, production fluctuates significantly around the growth trend. The dominant periodicity is the business cycle, a cycle of prosperity and recession of about 3–5 years in duration, but exhibiting considerable variability.

The amplitude of business cycle fluctuations in materials production is significantly greater than that in consumer goods production (exhibit 2). The peaks and troughs of the cycle in materials production also tend to lag behind those in production of consumer goods. Typically, the amplitude of fluctuations increases as they propagate from the customer to the supplier, with each upstream stage tending to lag behind its customer. These three features, oscillation, amplification, and phase lag, are pervasive in supply chains.

 

From Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

A central question in operations management is whether the oscillations, amplification and phase lag observed in supply chains arise as the result of operational or behavioral causes.

Operational theories assume that decision makers are rational agents who make optimal decisions given their local incentives and information.  Supply chain instability must then result from the interaction of rational actors with the physical and institutional structure of the system.

  • Physical structure includes the network linking customers and suppliers and the placement of inventories and buffers within it, along with capacity constraints and time delays in production, order fulfillment, transportation, and so on.
  • Institutional structure includes the degree of horizontal and vertical coordination and competition among firms, the availability of information to decision makers in each organization, and the incentives faced by each decision maker.

Behavioral explanations also capture the physical and institutional structure of supply chains, but view decision makers as boundedly rational actors with imperfect mental models, actors who use heuristics to make ordering, production, capacity acquisition, pricing and other decisions (Morecroft 1985, Sterman 2000, Boudreau et al. 2003, Gino & Pisano 2008, Bendoly et al. 2010, Croson et al. 2013).

Amplifications and Phase Lag

Amplification and phase lag arise from the presence of basic physical structures including stocks of inventory and delays in adjusting production or deliveries to changes in incoming orders.

Oscillations

Oscillations, however, are not inevitable. They arise from boundedly rational, behavioral decision processes

The difference matters: if supply chain instability arises from operational factors and rational behavior, then policies must be directed at changing the physical and institutional structure of the system, including incentives.

If, however, instability arises from bounded rationality and emotional arousal such policies may not be sufficient.

 

Key People:

  • Jay W Forrester
  • John Sterman
  • Rogelio Oliva
  • Hau L Lee

 

Key Terms:

  • Bullwhip Effect
  • Oscillations
  • Amplifications
  • Negative Feedback Loop
  • Positive Feedback Loop
  • Phase Lag
  • Supply Chain Networks
  • Inventory Management
  • Production Smoothening
  • Beer Distribution Game
  • Industrial Dynamics
  • Operational and Institutional Structures
  • Behavioral causes
  • Instability
  • Variability
  • Uncertainty

 

Key Sources of Research:

 

Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues

Rogelio Oliva

Paulo Gonçalves

 

Click to access 1889_087eb4f1-0532-4a7d-a206-3d565efc02af_2005-OLIVA133.pdf

 

 

REDUCING THE IMPACT OF DEMAND PROCESS VARIABILITY WITHIN A MULTI-ECHELON SUPPLY CHAIN

Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García

2008

 

Click to access CAMPU215.pdf

 

 

The impact of order variance amplification/dampening on supply chain performance

 

Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt

 

Click to access KBI_0603.pdf

 

 

Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains

 

Rachel Mason-Jones, and Denis R. Towill

Click to access 24557527da7aa9da7de238fe7f4a463b2af6.pdf

 

 

Bullwhip in Supply Chains ~ Past, Present and Future

Steve Geary Stephen M Disney and Denis R Towill

 

Click to access 492a6e6ae1d0f186fe2570b7477428e8e467.pdf

 

 

Shrinking the Supply Chain Uncertainty Circle

R Mason-Jones

Click to access 19980901d.pdf

 

 

THE BULLWHIP EFFECT IN SUPPLY CHAIN Reflections after a Decade

Gürdal Ertek, Emre Eryılmaz

 

Click to access ertek_eryilmaz_cels2008.pdf

 

 

Information distortion in a supply chain: The bullwhip effect

Hau L Lee; V Padmanabhan; Seugjin Whang

Management Science; Apr 1997; 43, 4;

Click to access f26117d56ab96aabe2d6cee4c390ab20ee18.pdf

 

 

 

THE SUPPLY CHAIN COMPLEXITY TRIANGE: UNCERTAINTY GENERATION IN THE SUPPLY CHAIN

 

Click to access 140687.pdf

 

 

The Bullwhip Effect in Supply Chains

Hau L. Lee, V. Padmanabhan and Seungjin Whang

1997

http://sloanreview.mit.edu/article/the-bullwhip-effect-in-supply-chains/

 

 

The Bullwhip Effect: Analysis of the Causes and Remedies

 

Jonathan Moll

Rene Bekker

 

Click to access werkstuk-moll_tcm243-354834.pdf

 

 

‘BULLWHIP’ AND ‘BACKLASH’ IN SUPPLY PIPELINES

Vinaya Shukla, Mohamed M Naim, Ehab A Yaseen

 

https://hal.archives-ouvertes.fr/hal-00525857/document

 

 

How human behaviour amplifies the bullwhip effect – a study based on the beer distribution game online

Joerg Nienhaus, Arne Ziegenbein*, Christoph Duijts

 

Click to access Bullwhip_Effect_Article.pdf

 

 

The Bullwhip Effect in Different Manufacturing Paradigm: An Analysis

Shamila Nabi KHAN1 Mohammad Ajmal KHAN2 Ramsha SOHAIL

 

Click to access 11.pdf.pdf

 

 

On replenishment rules, forecasting and the bullwhip effect in supply chains

Stephen M. Disney1 and Marc R. Lambrecht

 

Click to access Disney%20-%20On%20replenishment%20rules%20forecasting%20and%20the%20bullwhip%20effect%20in%20supply%20chains%20pre%20print.pdf

 

 

Causes and Remedies of Bullwhip Effect in Supply Chain

Sivakumar Balasubramanian Larry Whitman Kartik Ramachandran Ravindra Sheelavant

 

Click to access 2001IERCBullwhip.pdf

 

 

Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

John Sterman

 

Click to access Sterman%20Beh%20Ops%20Handbook%20Chapter%20140210.pdf

 

 

Modeling and Measuring the Bullwhip Effect

Li Chen and Hau L. Lee

2015

Click to access Chen_Lee_Bullwhip_2015.pdf

 

 

Operational and Behavioral Causes of Supply Chain Instability

John D. Sterman

Click to access 2a3118c5c7d2bd475335549b0b943009d66c.pdf

 

 

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

Rachel Croson, Karen Donohue, Elena Katok, and John Sterman

Click to access Order%20Stability%20in%20SCs050212.pdf

Click to access Order_Stability_070505.pdf

 

 

SUPPLY CHAIN DYNAMICS, THE “BEER DISTRIBUTION GAME” AND MISPERCEPTIONS IN DYNAMIC DECISION MAKING

John D. Sterman

Click to access E6-63-01-02.pdf

 

 

When Do Minor Shortages Inflate To Great Bubbles?

Paulo Gonçalves

2002

 

Click to access Gonca1.pdf

 

 

A new technology paradigm for collaboration in the supply chain

Branko Pecar and Barry Davies

Click to access c522d454d1dc036a22db29b2dee005dbc44e.pdf

 

 

MANAGERIAL INSIGHTS ON THE IMPACT OF FORECASTING AND INFORMATION ON VARIABILITY IN A SUPPLY CHAIN

Frank Chent, Zvi Drezner2 , Jennifer K. Ryan3 and David Simchi-Levi

 

Click to access 4%20chen.pdf

 

 

Supply and Production Networks: From the Bullwhip Effect to Business Cycles

Dirk Helbing Stefan Laemmer

2004

 

Click to access 04-12-033.pdf

 

 

Inventory dynamics and the bullwhip effect : studies in supply chain performance

Udenio, M.

2014

 

Click to access 776508.pdf

 

 

RECENT WORK ON BUSINES CYCLES IN HISTORICAL PERSPECTIVE: REVIEW OF THEORIES AND EVIDENCE

VictorZarnowitz

1984

 

Click to access w1503.pdf

 

 

THEORY AND HISTORY BEHIND BUSINESS CYCLES:ARE THE 1990S

THE ONSET OF A GOLDEN AGE?

 

Victor Zarnowitz

WorkingPaper7010 htp:/w.nber.org/papers/w7010

1999

 

Click to access w7010.pdf

 

 

The Beginning of System Dynamics

Jay W. Forrester

 

Click to access D-4165-1.pdf

 

 

Profiles in Operations Research: Jay Wright Forrester

David C. Lane John D. Sterman

 

Click to access jwf-profile-in-op.pdf

 

 

SYSTEM DYNAMICS MODELLING IN SUPPLY CHAIN MANAGEMENT: RESEARCH REVIEW

2000

 

Click to access 54fe11ea0aaa47f4c8e08959be2ef52d50a6.pdf

 

 

INDUSTRIAL DYNAMICS-AFTER THE FIRST DECADE

JAY W. FORRESTER

 

Click to access Forrester68.pdf

 

 

Industrial Dynamics

Jay W Forrester

1961

 

 

Business Dynamics

John Sterman

2000

 

Hierarchical Planning: Integration of Strategy, Planning, Scheduling, and Execution

Hierarchical Planning: Integration of Strategy, Planning, Scheduling, and Execution

In Manufacturing environments, there are hierarchical levels of planning and analysis.

  • Strategic – Tactical – Operational
  • Macro – Meso – Micro
  • Long Term – Medium Term – Short Term
  • Corporate – Business – Functional
  • Aggregate Plans – Detailed Schedules – Execution Results
  • Time Buckets – Gantt Charts – Plan Vs Actuals
  • Forecasts – Plans – Schedules – Results
  • Strategy – Planning – Execution
  • Time Series – Optimization-Simulation-Statistics
  • Structural – Cyclical – Sequential

 

slide_18

Beyond this, there are other analytical approaches:

  • Industry Analysis,
  • Scenario Planning,
  • Environmental Scanning,
  • Sectorial Analysis
  • Macro-Economics

 

From Integration of multi-scale planning and scheduling problems

A supply chain may be defined as an integrated process wherein various entities work together in an effort to meet the objectives of each entity as well as the common objectives of the overall supply chain. It is theoretically possible and preferable to build mathematical models for entire supply chains including all interacting strategic and operational decisions throughout the supply chain. Such monolithic models will not be consistent with the nature of the managerial decision process or practical due to computational complexity of models, data and solution techniques. Mathematical programming is most commonly used to formulate planning and scheduling problems within the process industry. The problems are combinatorial in nature which makes them very difficult to solve and it is vital to develop efficient modelling strategies, mathematical formulations and solutions methods. One of the major difficulties in building mathematical programming models is to keep the size within reasonable limits without sacrificing accuracy. To solve full-scale real-world planning and scheduling problems efficiently, simplification, approximation or aggregation strategies are most often necessary (Grunow et al., 2002, Engell et al., 2001).

It is widely recognized that the complex problem of what to produce and where and how to produce it is best considered through an integrated, hierarchical approach which also acknowledges typical corporate structures and business processes (Shah, 1999). Production planning and scheduling in a typical enterprise involves managers at various echelons within the organization and the decisions that need to be made differ by scope and time horizon and the underlying input information differs by its degree of certainty and aggregation. The decisions also need to be made with different timing and frequency and according to the correct sequence which even further makes the case for an integrated hierarchical approach.

The literature often describes problems solved individually but less often the integration of different problems or the integration of different detail levels of the same problems. An example of an integrated strategic and operational planning problem is described by Kallrath (2002) and an investigation on the integration of long-term, mid-term and short-term planning operations through a common data model is reported by Das et al. (2000). Some typical economical benefits of integrated decision making are listed by Shobrys and White (2002) who conclude that the major challenges in integrating planning, scheduling and control systems are involved in issues like changing human and organizational behavior rather than technical issues. The general conclusion made in the literature is that the integration of decisions with synchronized models is desirable but at the same time it is very difficult to solve such models efficiently.

 

Key Sources of Research:

Bodington, Charles E., and Thomas E. Baker.

“A history of mathematical programming in the petroleum industry.”

Interfaces 20.4 (1990): 117-127.

 

Baker, Thomas E., and Leon S. Lasdon.

“Successive linear programming at Exxon.”

Management science 31.3 (1985): 264-274.

 

Baker, Thomas E.

“Petro-chemical industry.”

Encyclopedia of Operations Research and Management Science. Springer US, 2001. 612-614.

 

Baker, Thomas E., and Donald E. Shobrys.

“The integration of planning, scheduling and control.”

Natl. Pet. Refiners Assoc.,(Tech. Pap.);(United States) 200.CONF-8510288- (1985).

 

Baker, Thomas E.

“A hierarchical/relational approach to modeling.”

Computer Science in Economics and Management 3.1 (1990): 63-80.

 

Jones, Chris, and Thomas E. Baker.

“MIMI/G: A graphical environment for mathematical programming and modeling.”

Interfaces 26.3 (1996): 90-106.

 

 

Cleaves, Gerard W., and Thomas E. Baker.

“Chesapeake R&D sponsor groups.”

Interfaces 20.6 (1990): 83-87.

 

A RELATIONAL MODELING SYSTEM FOR LINEAR AND INTEGER PROGRAMMING

A.ATAMTU RK,E.L.JOHNSON,J.T.LINDEROTH,andM.W.P.SAVELSBERGH

Click to access or48-2000.pdf

 

A bibliography for the development of an intelligent mathematical programming system

Harvey J. Greenberg

Click to access Greenberg96impsBib.pdf

 

Supply Chain Planning Optimization 

Click to access AMR%20Supply%20Chain.pdf

 

MIMI Brings OR Tools Together

http://www.eudoxus.com/mp-in-action/software/mpac9712

 

INTEGRATION OF PRODUCTION PLANNING AND SCHEDULING: OVERVIEW, CHALLENGES AND OPPORTUNITIES

Christos T. Maravelias and Charles Sung

Click to access 004635251c8f0cd8fd000000.pdf

 

HIERARCHICAL INTEGRATION OF PRODUCTION PLANNING AND SCHEDULING

Arnoldo C. Hax and Harlan C. Meal

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.2470&rep=rep1&type=pdf

 

Discrete Optimization Methods and their Role in the Integration of Planning and Scheduling

Ignacio E. Grossmann , Susara A. van den Heever and Iiro Harjunkoski

March 1, 2001

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.1826&rep=rep1&type=pdf

 

Planning and scheduling models for refinery operations

J.M. Pinto , M. Joly , L.F.L. Moro

Click to access 5686be3508ae1e63f1f5aa45.pdf

 

MATHEMATICAL PROGRAMMING MODELS AND METHODS FOR PRODUCTION PLANNING AND SCHEDULING

by Jeremy F. Shapiro

January, 1989

http://dspace.mit.edu/bitstream/handle/1721.1/5082/OR-191-89-24512977.pdf?sequence

 

Supporting supply chain planning and scheduling decisions in the oil and chemical industry

Winston Lasschuit, Nort Thijssen

Click to access 5516aa990cf2f7d80a383c39.pdf

 

Planning and Scheduling in Supply Chains: An Overview of Issues in Practice

Stephan Kreipl • Michael Pinedo

Click to access 0c96052de86f847e9b000000.pdf

 

Hierarchical approach for production planning and scheduling under uncertainty

Dan Wu, Marianthi Ierapetritou

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.169.9977&rep=rep1&type=pdf

 

Supply chain management and advanced planning––basics, overview and challenges

Hartmut Stadtler

Click to access 00b7d53327c5cd6d44000000.pdf

 

Integration of multi-scale planning and scheduling problems

Hlynur Stefanssona, Pall Jenssonb, Nilay Shah

 

Click to access 00b7d53aa958d6f7ba000000.pdf

 

Bitran, Gabriel R., and Arnold C. Hax.

“On the design of hierarchical production planning systems.”

Decision Sciences 8.1 (1977): 28-55.

 

Bitran, Gabriel R., Elizabeth A. Haas, and Arnoldo C. Hax.

“Hierarchical production planning: A single stage system.”

Operations Research 29.4 (1981): 717-743.

 

Bitran, Gabriel R., Elizabeth A. Haas, and Arnoldo C. Hax.

“Hierarchical production planning: A two-stage system.”

Operations Research 30.2 (1982): 232-251.

 

Bitran, Gabriel R., and Devanath Tirupati.

“Hierarchical production planning.”

Handbooks in operations research and management science 4 (1993): 523-568.

 

Axsäter, Sven, and Henrik Jönsson.

“Aggregation and disaggregation in hierarchical production planning.”

European Journal of Operational Research 17.3 (1984): 338-350.

 

Gfrerer, Helmut, and Günther Zäpfel.

“Hierarchical model for production planning in the case of uncertain demand.”

European Journal of Operational Research 86.1 (1995): 142-161.

 

Hax, Arnoldo C., and Gabriel R. Bitran.

“Hierarchical planning systems—a production application.”

Disaggregation. Springer Netherlands, 1979. 63-93.

 

THE CORPORATE STRATEGIC PLANNING PROCESS

Arnoldo C.:,Hax and Nicolas S. Majluft

1983

 

http://dspace.mit.edu/bitstream/handle/1721.1/2031/SWP-1396-09362356.pdf?..

 

A hierarchical decision support system for production planning (with case study)

 

Linet Ozdamar *, M. Ali Bozyel, S. Ilker Birbi

Click to access 55f3fdbf08ae63926cf26516.pdf

Gelders, Ludo F., and Luk N. Van Wassenhove.

 

“Hierarchical integration in production planning: Theory and practice.”

Journal of Operations Management 3.1 (1982): 27-35.

Gabbay, Henry.

 

A Hierarchical Approach to Production Planning.

No. TR-120. MASSACHUSETTS INST OF TECH CAMBRIDGE OPERATIONS RESEARCH CENTER, 1975.

 

Axsäter, Sven.

“Technical note—On the feasibility of aggregate production plans.”

Operations Research 34.5 (1986): 796-800.

 

Gabbay, Henry.

“Optimal aggregation and disaggregation in hierarchical planning.”

Disaggregation. Springer Netherlands, 1979. 95-106.

 

Fleischmann, Bernhard, and Herbert Meyr.

“Planning hierarchy, modeling and advanced planning systems.”

Handbooks in operations research and management science 11 (2003): 455-523.

 

Liberatore, Matthew J., and Tan Miller.

“A hierarchical production planning system.”

Interfaces 15.4 (1985): 1-11.

 

Nam, Sang-jin, and Rasaratnam Logendran.

“Aggregate production planning—a survey of models and methodologies.”

European Journal of Operational Research 61.3 (1992): 255-272.

 

Hierarchical mathematical programming for operational planning in a process industry 

W.G.M.M. Rutten

Click to access 394701.pdf

 

Saad, Germaine H.

“Hierarchical production-planning systems: extensions and modifications.”

Journal of the Operational Research Society 41.7 (1990): 609-624.

 

Omar, Mohamed K., and S. C. Teo.

“Hierarchical production planning and scheduling in a multi-product, batch process environment.”

International Journal of Production Research 45.5 (2007): 1029-1047.

 

Kistner, Klaus-Peter, and Marion Steven.

“Applications of operations research in hierarchical production planning.”

Modern Production Concepts. Springer Berlin Heidelberg, 1991. 97-113.

 

Shobrys, Donald E., and Douglas C. White.

“Planning, scheduling and control systems: why cannot they work together.”

Computers & chemical engineering 26.2 (2002): 149-160.

 

Stadtler, Hartmut.

“Hierarchical production planning: Tuning aggregate planning with sequencing and scheduling.”

Multi-stage production planning and inventory control. Springer Berlin Heidelberg, 1986. 197-226.

 

McKay, Kenneth N., Frank R. Safayeni, and John A. Buzacott.

“A review of hierarchical production planning and its applicability for modern manufacturing.”

Production Planning & Control 6.5 (1995): 384-394.

 

Combined Strategic and Operational Planning – An MILP Success Story in Chemical Industry

Josef Kallrath

 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.506.4194&rep=rep1&type=pdf

 

Das, B. P., et al.

“An investigation on integration of aggregate production planning, master production scheduling and short-term production scheudling of batch process operations through a common data model.”

Computers & Chemical Engineering 24.2 (2000): 1625-1631.