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
Gantt Chart Simulator in Atlantic Decision Sciences Scheduler
Key Sources for Research:
A Presentation by Chris Jones on Evolution of Graphical Production Scheduling Software
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 , 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 Integrated Location-Production-Distribution Planning in a Multi products Supply Chain Network Design Model
‘supply chain strategic design’,
‘supply chain planning’,
‘supply chain optimization’,
‘supply chain network design’,
‘supply chain production planning’,
‘supply chain delocalization’,
‘logistic network design’,
‘distribution network design’,
‘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:
Taxes and Tariffs
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:
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, 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.
Jay W Forrester
Hau L Lee
Negative Feedback Loop
Positive Feedback Loop
Supply Chain Networks
Beer Distribution Game
Operational and Institutional Structures
Key Sources of Research:
Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues
REDUCING THE IMPACT OF DEMAND PROCESS VARIABILITY WITHIN A MULTI-ECHELON SUPPLY CHAIN
Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García
The impact of order variance amplification/dampening on supply chain performance
Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt
Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains
Rachel Mason-Jones, and Denis R. Towill
Bullwhip in Supply Chains ~ Past, Present and Future
Steve Geary Stephen M Disney and Denis R Towill
Shrinking the Supply Chain Uncertainty Circle
THE BULLWHIP EFFECT IN SUPPLY CHAIN Reflections after a Decade
Gürdal Ertek, Emre Eryılmaz
Information distortion in a supply chain: The bullwhip effect
Hau L Lee; V Padmanabhan; Seugjin Whang
Management Science; Apr 1997; 43, 4;
THE SUPPLY CHAIN COMPLEXITY TRIANGE: UNCERTAINTY GENERATION IN THE SUPPLY CHAIN
Beyond this, there are other analytical approaches:
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 variousechelons 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.
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.”