Process-Oriented Production Planning and Control: Factors That Influence System Design

Finch, Byron J.; Cox, James F.

doi:10.5465/256501

Cited by 11 publications

(22 citation statements)

References 4 publications

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“…Ashayeri et al (1996) makes a similar comparison, divided into four categories; Relationship with the market, The production process, Quality and Planning & Control. Finch and Cox (1988) explore planning and control in six process industry companies with the goal of describing how planning and control functions should be accomplished in process industry firms. They identify factors that influence planning and control functions and how they are related to planning and control.…”

Section: Characteristicmentioning

confidence: 99%

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Kylinger¹

2015

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Process industries has for long been important for the development of Swedish industry and society. All industries face different conditions that affect how to best run their operations. This thesis aims to describe some of the conditions that characterize process industries compared to other industries. Further one of these characteristics has been studied more closely.One of the traits of process industries is that they are positioned at the start of the transformation process close to the raw material mixing, separating or forming it into products often used for further transformation. Process industries hence become dependent on the properties of these materials. One of the most prominent characteristics inherent from the raw material properties is the divergent bill of material. The divergent bill of material originates from the fact that a given raw material is made up of different components that will yield several products with different characteristics when processed. When splitting the raw material into the desired products the yielded products from a certain raw material usually have different value to the producer, some more desired than others. These multiple products generated poses a challenge from a planning perspective raising questions like "How should we balance the supply and demand of all the products produced?", "What shall we do with the excess products produced?".The first paper in this thesis describe the production planning in four Swedish process industries with the ultimate aim to connect their planning to the supply chain characteristics they face as process industries. The study concludes that the industry specific conditions mainly affect planning at short time ranges when planning becomes more detailed. In general the use of planning or decision support systems is low, stemming from a, warranted or not, belief that general decision support systems do not fit process industries. Another finding is that the case companies mainly operate in niche markets. This study also highlighted that the planning complexity arising from characteristic of co-and by-product generation in combination with the lack of decision support systems requires further studies.The subsequent two papers focus on supply chain planning and coordination with a divergent bill of material. They present a mathematical model over the supply chain planning in a real case company in the specialty oils industry. The second paper investigates transfer pricing as a coordination tool by comparing decentralised supply chain planning with centralized planning in an integrated model. Transfer pricing is found to have a potential positive effect on supply chain planning while simultaneously creating problems in terms of uneven distribution of the contribution margin among supply chain partners.Finally the third paper more closely investigates different ways to set transfer prices and comparing them to the optimal transfer prices. Setting optimal transfer prices with a divergent bill of material has proven to be less str...

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Section: Characteristicmentioning

confidence: 99%

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Kylinger¹

2015

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“…They name environmental demands, danger and quality measurements under the quality group which are considered to be highly important for the process industry but of less importance for DIs. Finch & Cox (1988) mention 35 different characteristics divided into two groups: general characteristics such as the importance of co-products balance and high intermediate products demand; and the factors which affect the MPC design such as demand seasonality and the natural sequencing problem in production. All these classifications though are based on the initial studies by Taylor and his group in the late 1970s (Van Donk & Fransoo, 2006).…”

Section: Process Industriesmentioning

confidence: 99%

“…Process industries though still lag behind the discrete manufacturing industries (DIs) in the implementation of manufacturing planning processes which match their specific characteristics and requirements (Dennis & Meredith, 2000b). This is specifically noticeable at the strategic/tactical planning level (Finch & Cox, 1988) (Proud, 1999) including S&OP.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Sales and Operations Planning in Process Industries

Noroozi¹

2014

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This thesis studies Sales and Operations Planning (S&OP) in process industries. S&OP is a planning process which has a role of balancing demand and supply at an aggregate level. S&OP has traditionally been considered as a generic process meaning that it is independent from the context of the industry in which it is implemented. Process industries do; however, have specific characteristics which affect their planning and control processes, including S&OP. Therefore, the aim in this thesis is to reconcile these two contradictory stances and furthermore investigate how the specific properties in process industries should be included into an S&OP framework. Such a differentiated framework aims to support process industries to design/implement their S&OP process based on their unique requirements in relation to their markets, products and processes while it also takes into account the specific characteristics regarding the continuous production. Consequently, the process industries would realize the expected benefits from S&OP process to a greater extent.The study starts with a literature review on S&OP in process industries in order to build the knowledge foundation for subsequent studies. The results show that the underlying difference between process industries and discrete industries is the object continuity which affects the selection of production processes including resources. Thus, process industries are often hybrids in the sense that their production processes include both continuous production, when the transformed object (material) is continuous, and discrete production when the object (material) turns into discrete products after the discretization point. In other words, process industries deploy both continuous production and discrete production while discrete industries use only discrete production. The specific characteristics of process industries are actually related to the continuous production part and influence the planning processes as well. As the result of this study, the continuous production characteristics that can affect the S&OP process are identified.In the continuation of the thesis and in order to provide a typology for planning and control purposes, the object type (continuous and discrete) is combined with two other dimensions i.e. mode type (onetime, intermittent and continuous) and driver type (customer order driven and forecast driven) into a planning and control typology. The mode type addresses the repetitivity of the flow and the driver type takes into account the trigger of the flow. Each dimension has a transition point -i.e. the discretization point for the object type, mode interface point for the mode type and customer order decoupling point for the driver typeand represents a hybrid situation since different production environments are required before and after each transition point. The typology aims to illustrate how each dimension affects the planning and control issues as well as how the dimensions are interrelated and how this combination influence the manage...

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“…In a study of master production scheduling, Novitsky () lists characteristics of the process industries, including for example well‐defined capacities and short product manufacturing lead times. Further on, Finch and Cox () give a more detailed list of characteristics, including that the variability of raw material quality is great, and the balance of co‐products is important, etc. Using these characteristics, supplemented by input from process industry professionals, the list of variables previously presented by Storm et al.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Managing the manufacturing–R&D interface in the process industries

2013

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In the perspective of value creation and capture, firms in the future must not only be excellent in developing commodities or innovative functional products; they must also be able to manufacture them in a competitive cost structure within the framework of a proper business model. Deploying a conceptual model of the material transformation system in the process industries, the relationship between firms' manufacturing and innovation activities has been explored in three case studies representing the food, mineral and steel industries. Using the methodology of Quality Function Deployment, each firm's position on the model structure has been condensed into a matrix relating the manufacturing system's characterizing variables to the firm's raw material innovation, innovation of process technology and product innovation. The importance of the area of process innovation stands out in all these firms, and among the individual variables 'product flexibility' ranked highest in all of them. It is recommended that in the development of corporate innovation strategies, the productmarket perspective ought to be supplemented by a process-manufacturing perspective. Combining the information from both perspectives and striking a proper balance ought to be beneficial in overbridging the manufacturing-R&D interface. bs_bs_banner 252 R&D Management 43, 3, 2013. • number of primary raw materials; range (95) • environmental footprint from raw material conversion (95) Managing the manufacturing-R&D interface No. C3 Number of products (product range) No. C4 Number of product varieties (product variety range) No. C5 Volume of semi-finished products No. C6 Number of co-products No. C7 Num ber of byproducts No. C8 Internal product structural complexity No. C9 Product inherent changeability during handling No. C10 Product traceability in the production system No. C11 Product environmental footprint Key financially related production variables No. D1 Raw material intensity (raw m aterial cost/company turnover) No. D2 Conversion intensity (sales-raw material cost/ company turnover) No. D3 Product inventory turnover (product inventory/total assets) No. D4 Dow nstream product supply chain complexity Managing the manufacturing-R&D interface No. D1 Raw material intensity (raw m aterial cost/company turnover) No. D2 Conversion intensity (sales-raw material cost/ company turnover) No. D3 Product inventory turnover (product inventory/total assets) No. D4 Downstream product supply chain complexity Managing the manufacturing-R&D interface No. A1 Volume of captive (company ow ned) primary raw material supplies No. A2 Volume of recycled external raw material supplies No. A3 Number of primary raw materials (range) No. A4 Prim ary raw material specifications No. A5 Prim ary raw material changeability during handling No. A6 Environmental footprint during raw m aterial conversion Key process-related production variables No. B1 Volume of internally recycled material No. B2 Production flow characteristics (batch -continuous) No. B3 Processing complexity (nu...

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Process-Oriented Production Planning and Control: Factors That Influence System Design

Cited by 11 publications

References 4 publications

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

Supply chain coordination using optimal transfer pricing to balance co- and byproduct demand within a process industry

A Framework for Sales and Operations Planning in Process Industries

Managing the manufacturing–R&D interface in the process industries

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